The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.
\ns3://aws-public-blockchain/v1.0/btc/s3://aws-public-blockcha",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "New data is delivered daily to the current date folders Parquet files.",
"tags": "aws-pds,blockchain,web3",
"license": "https://github.com/aws-samples/digital-assets-examples/blob/main/LICENSE",
"deprecated": false
},
{
"slug": "bdsp-harvard-eeg",
"name": "Harvard Electroencephalography Database",
"description": "The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University:Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH).",
"managed_by": "[Brain Data Science Platform](https://bdsp.io/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,neurophysiology,medicine,machine learning,neuroscience,deep learning,life sciences,bioinformatics",
"license": "BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/harvard-eeg-db/view-license/1.0/); [Data User Agreement:](https://bdsp.io/content/harvard-eeg-db/view-dua/1.0/)\"",
"deprecated": false
},
{
"slug": "bdsp-heedb",
"name": "Harvard-Emory ECG Database",
"description": "The Harvard-Emory ECG database (HEEDB) is a large collection of 12-lead electrocardiography (ECG) recordings, prepared through a collaboration between Harvard University and Emory University investigators.",
"managed_by": "[Brain Data Science Platform](https://bdsp.io/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,neurophysiology,medicine,machine learning,neuroscience,deep learning,life sciences,bioinformatics",
"license": "BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/heedb/view-license/1.0/); [Data User Agreement:](https://bdsp.io/content/heedb/view-dua/1.0/)\"",
"deprecated": false
},
{
"slug": "bdsp-hsp",
"name": "The Human Sleep Project",
"description": "The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~15K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting. This data is being used to develop CAISR (Complete AI Sleep Report), a collection of deep neural networks, rule-based algorithms, and",
"managed_by": "[Brain Data Science Platform](https://bdsp.io/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,neurophysiology,medicine,machine learning,neuroscience,deep learning,life sciences,bioinformatics",
"license": "[BDSP Restricted Health Data License 1.0.0](https://bdsp.io/content/hsp/view-license/1.0/); [terms of use](https://github.com/bdsp-core/bdsp-license-and-dua/blob/main/terms-of-use.md)",
"deprecated": false
},
{
"slug": "bdsp-icare",
"name": "I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography Database",
"description": "The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalography (EEG) recordings from 1,020 comatose patients with a diagnosis of cardiac arrest who were admitted to an intensive care unit from seven academic hospitals in the U.S. and Europe. Patients were monitored with 18 bipolar EEG channels over hours to days for the diagnosis of seizures and for neurological prognostication. Long-term neurological function w",
"managed_by": "[Brain Data Science Platform](https://bdsp.io/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,neurophysiology,medicine,machine learning,neuroscience,deep learning,life sciences,bioinformatics",
"license": "BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/bdsp-icare/view-license/1.0/); [Data User Agreement:](https://bdsp.io/content/bdsp-icare/view-dua/1.0/)\"",
"deprecated": false
},
{
"slug": "bdsp-sparcnet",
"name": "SPaRCNet data:Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalography",
"description": "The IIIC dataset includes 50,697 labeled EEG samples from 2,711 patients' and 6,095 EEGs that were annotated by physician experts from 18 institutions. These samples were used to train SPaRCNet (Seizures, Periodic and Rhythmic Continuum patterns Deep Neural Network), a computer program that classifies IIIC events with an accuracy matching clinical experts.",
"managed_by": "[Brain Data Science Platform](https://bdsp.io/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,neurophysiology,medicine,machine learning,neuroscience,deep learning,life sciences,bioinformatics",
"license": "BDSP Restricted Health Data License 1.0.0 \"[BDSP Licence](https://bdsp.io/content/bdsp-sparcnet/view-license/1.1/); [Data User Agreement:](https://bdsp.io/content/bdsp-sparcnet/view-dua/1.1/)\"",
"deprecated": false
},
{
"slug": "beataml",
"name": "Beat Acute Myeloid Leukemia (AML) 1.0",
"description": "Beat AML 1.0 is a collaborative research program involving 11 academic medical centers who worked\ncollectively to better understand drugs and drug combinations that should be prioritized for\nfurther development within clinical and/or molecular subsets of acute myeloid leukemia (AML)\npatients. Beat AML 1.0 provides the largest-to-date dataset on primary acute myeloid leukemia\nsamples offering genomic, clinical, and drug response.\n\nThis dataset contains open Clinical Supplement and RNA-Seq Gene E",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,life sciences,cancer,genetic,genomic,Homo sapiens,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "bhl-open-data",
"name": "Biodiversity Heritage Library Metadata and Page Images",
"description": "The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. BHL operates as a worldwide consortium of natural history, botanical, research, and national libraries working together to digitize the natural history literature held in their collections and make it freely available for open access.",
"managed_by": "[The Biodiversity Heritage Library](https://biodiversitylibrary.org/)",
"update_frequency": "Metadata is updated monthly. Images are updated weekly.",
"tags": "biodiversity,bioinformatics,life sciences",
"license": "Public Domain, CC0, or Creative Commons. Exact licenses are found in the related metadta files and documentation.",
"deprecated": false
},
{
"slug": "binding-db",
"name": "Binding DB - Data Lakehouse Ready",
"description": "This a parquet representation of The Binding Database's [Full BindingDB Database Dump](https://www.bindingdb.org/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes) that you can query straight from Athena in under 60 seconds (no Oracle database required). The Binding Database projects aims to make experimental data on the noncovalent association of molecules in solution searchable via the world wide web. The initial focus is on biomolecular systems, but data on host-guest and supramolecular",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Within 2 months after an new BindingDB release.",
"tags": "aws-pds,chemistry,genetic,genomic,molecule,life sciences,biotech blueprint,parquet",
"license": "https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt",
"deprecated": true
},
{
"slug": "biolip",
"name": "BioLiP",
"description": "BioLiP is a semi-manually curated database for high-quality, biologically relevant ligand-protein binding interactions. The structure data are collected primarily from the Protein Data Bank (PDB), with biological insights mined from literature and other specific databases. BioLiP aims to construct the most comprehensive and accurate database for serving the needs of ligand-protein docking, virtual ligand screening and protein function annotation.",
"managed_by": "[Zhang Lab](https://zhanggroup.org/)",
"update_frequency": "No regular schedule; updated upon availability of major dataset revisions",
"tags": "protein,structural biology,molecular docking,bioinformatics,molecule,life sciences,chemistry",
"license": "No explicit license stated (publicly available for academic and research use).",
"deprecated": false
},
{
"slug": "black_marble_combustion",
"name": "Nighttime-Fire-Flare",
"description": "Detection of nighttime combustion (fire and gas flaring) from daily top of atmosphere data from NASA's Black Marble VNP46A1 product using VIIRS Day/Night Band and VIIRS thermal bands.",
"managed_by": "[USRA](https://srijachakraborty.com/) and [NASA Black Marble](https://blackmarble.gsfc.nasa.gov/)",
"update_frequency": "New combustion detections are added whenever it is available and with model updates.",
"tags": "aws-pds,anomaly detection,classification,earth observation,satellite imagery,disaster response,socioeconomic,environmental,urban,NASA SMD AI",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "blended-tropomi-gosat-methane",
"name": "Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane",
"description": "A dataset of satellite retrievals of atmospheric methane that extends from 30 April 2018 to present.",
"managed_by": "Nicholas Balasus",
"update_frequency": "Monthly",
"tags": "aws-pds,climate,environmental,satellite imagery",
"license": "There are no restrictions on the use of this data, but please contact nicholasbalasus@g.harvard.edu before its use in a publication.",
"deprecated": false
},
{
"slug": "blue_et",
"name": "IWMI DIWASA Blue ET for Africa",
"description": "Blue evapotranspiration (Blue ET) is the portion of ET derived from blue water sources, including surface water (rivers, lakes, reservoirs) and groundwater used for irrigation. It is a key component of blue water fluxes in water accounting. Blue ET consists of evaporation from irrigated fields, transpiration from irrigated crops, and water lost from artificial storage. It helps assess water productivity in irrigated agriculture, quantify consumptive water use, and support sustainable water resou",
"managed_by": "[IWMI](https://www.iwmi.org/)",
"update_frequency": "None",
"tags": "aws-pds,surface water,irrigated cropland,ground water,evapotranspiration,water",
"license": "Creative Commons open license",
"deprecated": false
},
{
"slug": "bluebrain_opendata",
"name": "Blue Brain Open Data",
"description": "The Blue Brain Open Data represents an extensive neuroscience dataset encompassing a diverse range of data types, including experimental, model, and simulation data, along with images and videos depicting reconstructed neurons and brain regions.",
"managed_by": "Open Brain Institute",
"update_frequency": "No updates",
"tags": "neuroscience,simulation neuroscience,brain models,morphological reconstructions,electrophysiology,life sciences,single neuron models,ion channels,brain images,microcircuit modeling and simulation",
"license": "CC-BY-4.0",
"deprecated": false
},
{
"slug": "bobsrepository",
"name": "Baby Open Brains (BOBs) Repository on AWS",
"description": "Manually curated and reviewed infant brain segmentations and accompanying T1w and T2w images for a range of 1-9 month old participants from the Baby Connectome Project (BCP)",
"managed_by": "Masonic Institute for the Developing Brain (MIDB) Open Data Initiative",
"update_frequency": "The repository is updated when: (1) all brain segmentations have undergone further rounds of manual correction, which may include refinement of existing ROIs and/or delineation of additional FreeSurfer ROIs and/or (2) new manually refined data from different age ranges and/or source studies becomes available",
"tags": "neuroimaging,magnetic resonance imaging,neuroscience,pediatric,nifti,segmentation,life sciences",
"license": "CC-By Attribution 4.0 International",
"deprecated": false
},
{
"slug": "bodym",
"name": "BodyM Dataset",
"description": "The first large public body measurement dataset including 8978 frontal and lateral\nsilhouettes for 2505 real subjects, paired with height, weight and 14 body\nmeasurements. The following artifacts are made available for each subject.\n - Subject Height\n - Subject Weight\n - Subject Gender\n - Two black-and-white silhouette images of subject standing in frontal and side pose\n respectively with full body in view.\n - 14 body measurements in cm - {ankle girth, arm-length, bicep girth, calf girth,\n",
"managed_by": "Amazon",
"update_frequency": "None",
"tags": "amazon.science,computer vision,deep learning",
"license": "Creative Commons Attribution-Non Commercial 4.0 International Public License - https://creativecommons.org/licenses/by/4.0/legalcode",
"deprecated": false
},
{
"slug": "boltz1",
"name": "Boltz-1 Training Data",
"description": "This is the data used to train the Boltz-1 model. It contains the following datasets:\n - Our pre-processed version of the Protein Data Bank\n - Our pre-processed version of the multiple sequence alignment data for each protein chain\n - The raw multiple sequence alginment data.\n - A pre-computed symmetry file for symmetry correction during training\n",
"managed_by": "MIT CSAIL - Regina Barzilay Group",
"update_frequency": "None",
"tags": "deep learning,protein folding,molecular docking,open source software,life sciences",
"license": "MIT License",
"deprecated": false
},
{
"slug": "boreas",
"name": "Boreas Autonomous Driving Dataset",
"description": "This autonomous driving dataset includes data from a 128-beam Velodyne Alpha-Prime lidar, a 5MP Blackfly camera, a 360-degree Navtech radar, and post-processed Applanix POS LV GNSS data. This dataset was collect in various weather conditions (sun, rain, snow) over the course of a year. The intended purpose of this dataset is to enable benchmarking of long-term all-weather odometry and metric localization across various sensor types. In the future, we hope to also support an object detection benc",
"managed_by": "[ASRL](http://asrl.utias.utoronto.ca)",
"update_frequency": "New driving sequences will be added as they are collected.",
"tags": "autonomous vehicles,robotics,computer vision,lidar,aws-pds",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)",
"deprecated": false
},
{
"slug": "bossdb",
"name": "BossDB Open Neuroimagery Datasets",
"description": "This data ecosystem, Brain Observatory Storage Service & Database (BossDB), contains several neuro-imaging datasets across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include dense segmentation and meshes.",
"managed_by": "[Johns Hopkins University Applied Physics Laboratory](https://https://jhuapl.edu)",
"update_frequency": "New datasets are added as soon as it is available. Minor updates on existing datasets occur sporadically.",
"tags": "aws-pds,life sciences,imaging,neuroscience,neuroimaging,electron microscopy,x-ray tomography,x-ray microtomography,x-ray,magnetic resonance imaging",
"license": "Creative Commons 4.0 International (CC BY 4.0); Creative Commons CC0 1.0 Universal (CC0-1.0); Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)",
"deprecated": false
},
{
"slug": "bps_microscopy",
"name": "Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset",
"description": "Fluorescence microscopy images of individual nuclei from mouse fibroblast cells, irradiated with Fe particles or X-rays with fluorescent foci indicating 53BP1 positivity, a marker of DNA damage. These are maximum intensity projections of 9-layer microscopy Z-stacks.",
"managed_by": "[NASA](https://osdr.nasa.gov/)",
"update_frequency": "New fluorescence microscopy mouse fibroblast nuclei data is added whenever it is available.",
"tags": "aws-pds,fluorescence imaging,genetic,genetic maps,microscopy,GeneLab,NASA SMD AI,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "bps_rnaseq",
"name": "Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset",
"description": "RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network.",
"managed_by": "[NASA](https://osdr.nasa.gov/)",
"update_frequency": "New spaceflight liver RNA sequencing data is added whenever it is available.",
"tags": "aws-pds,space biology,gene expression,genetic,genetic maps,GeneLab,NASA SMD AI,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "braidyn-bc_cued-lever-pull",
"name": "BraiDyn-BC: Cued lever-pull task dataset",
"description": "The BraiDyn-BC (Brain Dynamics underlying emergence of Behavioral Change) Database offers an extensive, multimodal dataset that links\nwide-field calcium imaging of the mouse neocortex to comprehensive behavioral measurements during a behavioral task.\nAs one of the contents in this database, we newly provide a dataset that includes 15 sessions spanning two weeks of motor skill learning, \nin which 25 mice were trained to pull a lever to obtain water rewards.\nSimultaneous high-speed videography cap",
"managed_by": "[BraiDyn-BC Database Project](https://boatneck-weeder-7b7.notion.site/BraiDyn-BC-Database-303cf08c89f94d81bb2eaed4c3c50345)",
"update_frequency": "NA",
"tags": "Mus musculus,neuroscience,calcium imaging,video,imaging,life sciences,aws-pds",
"license": "Creative Commons Attribution 4.0 International (CC-BY 4.0)",
"deprecated": false
},
{
"slug": "brain-encoding-response-generator",
"name": "Brain Encoding Response Generator (BERG)",
"description": "Brain Encoding Response Generator (BERG) is a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code.",
"managed_by": "[Neural Dynamics of Visual Cognition](https://www.ewi-psy.fu-berlin.de/en/psychologie/arbeitsbereiche/neural_dyn_of_vis_cog/index.html), [CVAI](https://www.cvai.cs.uni-frankfurt.de/)",
"update_frequency": "Updates will be released on a per-model or dataset basis as new models are trained and tested.",
"tags": "neuroscience,machine learning,brain models,deep learning,neuroimaging,computer vision,life sciences",
"license": "CC BY-NC 4.0. For Terms & Conditions, see https://brain-encoding-response-generator.readthedocs.io/en/latest/about/terms_and_conditions.html",
"deprecated": false
},
{
"slug": "brainglobe",
"name": "BrainGlobe Atlases",
"description": "BrainGlobe provides an archive and standardised interface to anatomical atlases from multiple species. This dataset includes these atlases, and other data (e.g. sample neuroanatomy data) to allow the greatest use of the atlases.",
"managed_by": "[BrainGlobe](https://brainglobe.info/)",
"update_frequency": "When new atlases are packaged",
"tags": "biology,life sciences,digital preservation,Homo sapiens,image processing,imaging,light-sheet microscopy,magnetic resonance imaging,medical imaging,microscopy",
"license": "Creative Commons CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "brainminds-marmoset-connectivity",
"name": "Brain/MINDS Marmoset Connectivity Resource on AWS",
"description": "Brain/MINDS Marmoset Connectivity Resource (BMCR) is a resource that provides access to anterograde and retrograde neuronal tracer data, made available by Brain/MINDS project. It is currently restricted to injections into the prefrontal cortex of a marmoset brain but is planned to include injections into entire cortical areas and representative subcortical brain regions.",
"managed_by": "[RIKEN Center for Brain Science](https://cbs.riken.jp)",
"update_frequency": "As new data become available.",
"tags": "brain images,imaging,microscopy,neurobiology,neuroimaging,neuroscience,nifti,non-human primate,life sciences",
"license": "Creative Commons Attribution 4.0 International",
"deprecated": false
},
{
"slug": "brazil-data-cubes",
"name": "Earth Observation Data Cubes for Brazil",
"description": "Earth observation (EO) data cubes produced from analysis-ready data (ARD) of CBERS-4, Sentinel-2 A/B and Landsat-8 satellite images for Brazil. The datacubes are regular in time and use a hierarchical tiling system. Further details are described in [Ferreira et al. (2020)](https://www.mdpi.com/2072-4292/12/24/4033).",
"managed_by": "[INPE - Brazil Data Cube](http://brazildatacube.org/)",
"update_frequency": "New EO data cubes are added as soon as there are produced by the Brazil Data Cube project.",
"tags": "earth observation,satellite imagery,geoscience,geospatial,image processing,open source software,cog,stac,aws-pds",
"license": "The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 and Sentinel-2 satellites. Data usage is subject to Terms and Conditions for the use and distribution of [Landsat data](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_supp",
"deprecated": false
},
{
"slug": "broad-gnomad",
"name": "Genome Aggregation Database (gnomAD)",
"description": "The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.\nThe v4.1 data set (GRCh38) spans 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, of [diverse ancestries](https",
"managed_by": "gnomAD Production Team at the Broad Institute",
"update_frequency": "Data from new releases are made public as soon as they are available. New releases, including both minor and major versions, have historically been issued on the order of once per year.",
"tags": "aws-pds,population genetics,population,whole genome sequencing,genomic,genetic,life sciences,bioinformatics,short read sequencing",
"license": "[MIT](https://github.com/broadinstitute/gnomad_methods/blob/master/LICENSE); [terms of use](https://gnomad.broadinstitute.org/terms)",
"deprecated": false
},
{
"slug": "broad-pan-ukb",
"name": "UK Biobank Pan-Ancestry Summary Statistics",
"description": "A multi-ancestry analysis of 7,221 phenotypes using a generalized mixed model association testing framework, spanning 16,119 genome-wide association studies. We provide standard meta-analysis across all populations and with a leave-one-population-out approach for each trait. The data are provided in tsv format (per phenotype) and Hail MatrixTable (all phenotypes and variants). Metadata is provided in phenotype and variant manifests.",
"managed_by": "Analytic and Translational Genetics Unit, Massachusetts General Hospital and the Broad Institute",
"update_frequency": "Occasional",
"tags": "aws-pds,genetic,genome wide association study,genomic,life sciences,population genetics",
"license": "CC BY-4.0 (usage may be restricted by UK Biobank, more details on the \"[Downloads page](https://pan.ukbb.broadinstitute.org/downloads)\")",
"deprecated": false
},
{
"slug": "broad-references",
"name": "Broad Genome References",
"description": "Broad maintained human genome reference builds hg19/hg38 and decoy references.",
"managed_by": "Broad Institute",
"update_frequency": "Monthly",
"tags": "aws-pds,biology,bioinformatics,cancer,genetic,genomic,life sciences,reference index,Homo sapiens",
"license": "CC0 1.0 Universal (CC0 1.0) Public Domain Dedication",
"deprecated": false
},
{
"slug": "busco-data",
"name": "BUSCO Datasets",
"description": "Lineage datasets for use with BUSCO software package. Each dataset contains HMM profiles for clade specific, universal, single-copy marker genes. Datasets are available across archaea, bacteria, eukaryota and virus domains. The repository also includes necessary data files for phylogenetic placement of an input assembly.",
"managed_by": "Computational Evolutionary Genomics Group, University of Geneva",
"update_frequency": "New datasets are released to correspond with updates in OrthoDB versions. Maintenance updates occur a few times a year if necessary to fix any bugs or update metadata.",
"tags": "assembly,bacteria,bioinformatics,genomic,life sciences,metagenomics,open source software,protein,virus,aws-pds",
"license": "The BUSCO datasets are licensed under the Creative Commons Attribution-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Any use of these datas",
"deprecated": false
},
{
"slug": "c2smsfloods",
"name": "Cloud to Street - Microsoft Flood and Clouds Dataset",
"description": "This dataset consists of chips of Sentinel-1 and Sentinel-2 satellite data. Each Sentinel-1 chip contains a corresponding label for water and each Sentinel-2 chip contains a corresponding label for water and clouds. Data is stored in folders by a unique event identifier as the folder name. Within each event folder there are subfolders for Sentinel-1 (s1) and Sentinel-2 (s2) data. Each chip is contained in its own sub-folder with the folder name being the source image id, followed by a unique chi",
"managed_by": "[Radiant Earth Foundation](https://radiant.earth/)",
"update_frequency": "Not updated",
"tags": "aws-pds,computer vision,deep learning,machine learning,floods,geospatial,earth observation,satellite imagery,cog,synthetic aperture radar",
"license": "CC-BY-4.0 https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "caendr",
"name": "Caenorabditis Diversity Natural Resource",
"description": "The Caenorhabditis Natural Diversity Resource (CaeNDR) is a data repository and analysis hub of wild strains of selfing Caenhorabditis species C. elegans, C. briggsae, and C. tropicalis from around the world to facilitate discovery of genetic variation across all three species through genome-wide association mappings to correlate genotype with phenotype and identify genetic variation underlying quantitative traits.",
"managed_by": "The Andersen Lab, Johns Hopkins University, Baltimore, MD, USA",
"update_frequency": "Annually",
"tags": "aws-pds,bam,bioinformatics,biology,Caenorhabditis elegans,fastq,gatk-sv,genetic maps,genome,genome wide association study",
"license": "All CaeNDR data is available under the MIT License. Use of the data should be cited in the usual way, with current details available at https://caendr.org/#citationModal",
"deprecated": false
},
{
"slug": "caladapt-coproduced-climate-data",
"name": "Co-Produced Climate Data to Support California's Resilience Investments",
"description": "Downscaled future and historical climate projections for California and her environs in support of California's Fifth Climate Assessment",
"managed_by": "Cal-Adapt: Analytics Engine (by Eagle Rock Analytics, Inc.)",
"update_frequency": "Infrequent, Irregular",
"tags": "atmosphere,aws-pds,climate,climate model,earth observation,geoscience,geospatial,meteorological,simulations,weather",
"license": "Varies, see dataset specific metadata",
"deprecated": false
},
{
"slug": "caladapt-wildfire-dataset",
"name": "Wildfire Projections to Support Climate Resilience",
"description": "Wildfire projections for California and her environs in support of California's Fifth Climate Assessment supported with historical weather observations and renewable energy capacity profiles for grid operations.",
"managed_by": "Cal-Adapt: Analytics Engine (by Eagle Rock Analytics, Inc.)",
"update_frequency": "Infrequent, Irregular",
"tags": "aws-pds,climate,climate model,climate projections,weather,solar,energy,electricity,sustainability,agriculture",
"license": "Varies, see dataset specific metadata",
"deprecated": false
},
{
"slug": "camelyon",
"name": "CAncer MEtastases in LYmph nOdes challeNge (CAMELYON) Dataset",
"description": "\"This dataset contains the all data for the [CAncer MEtastases in LYmph nOdes challeNge or CAMELYON](https://camelyon17.grand-challenge.org). CAMELYON was the first challenge using whole-slide images in computational pathology and aimed to help pathologists identify breast cancer metastases in sentinel lymph nodes. Lymph node metastases are extremely important to find, as they indicate that the cancer is no longer localized and systemic treatment might be warranted. Searching for these metastase",
"managed_by": "Radboud University Medical Center",
"update_frequency": "As required",
"tags": "aws-pds,life sciences,cancer,computational pathology,grand-challenge.org,histopathology,deep learning,computer vision",
"license": "CC0",
"deprecated": false
},
{
"slug": "canelevation-dem",
"name": "CanElevation - Canada Digital Elevation Models",
"description": "The Canadian DEM represents the current coverage of elevation data available. This dataset includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived products. This dataset includes a 1m, 2m and 30m DEM. The 1m and 2 m products are a combination of DEM data generated from airborne LiDAR and optical digital images. The 30 m DEM integrates data from the Copernicus DEM acquired during the TanDEM-X Mission, with the DEM data derived from airborne lidar and provides a com",
"managed_by": "[Natural Resources Canada](https://nrcan.gc.ca/)",
"update_frequency": "The dataset is updated as new DEM models becomes available.\n
\nL'ensemble de données est mis à jour à mesure que des nouveaux modèles numérique d'élévation deviennent disponibles.\n",
"tags": "aws-pds,canada,elevation,geospatial,stac,land,dsm,dtm,dem",
"license": "[Open Government License (OGL)](https://open.canada.ca/en/open-government-licence-canada)",
"deprecated": false
},
{
"slug": "canelevation-pointcloud",
"name": "CanElevation - LiDAR Point Clouds",
"description": "The [LiDAR Point Clouds](https://open.canada.ca/data/en/dataset/7069387e-9986-4297-9f55-0288e9676947) is a product that is part of the CanElevation Series created to support the [National Elevation Data Strategy](https://natural-resources.canada.ca/maps-tools-publications/satellite-elevation-air-photos/national-elevation-data-strategy) implemented by NRCan.\nThis product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition pr",
"managed_by": "[Natural Resources Canada](https://www.nrcan.gc.ca/)",
"update_frequency": "The dataset is updated as new LiDAR data becomes available.\nL'ensemble de données est mis à jour à mesure que de nouvelles données LiDAR deviennent disponibles.",
"tags": "aws-pds,lidar,elevation,geospatial,floods,land,urban",
"license": "The LiDAR Point Clouds dataset is available free of charge under the [Open Government License - Canada](https://open.canada.ca/en/open-government-licence-canada).\nLe jeu de données Nuages de points LiDAR est disponible gratuitement sous la [licence du gouvernement ouvert - Canada](https://ouvert.can",
"deprecated": false
},
{
"slug": "canoe",
"name": "CANOE (Canadian Aquatic Navigation for Observation of the Environment) Dataset",
"description": "This autonomous marine navigation dataset includes data from a 360-degree Navtech radar, a 128-beam Ouster OS1 lidar with integrated IMU, a Teledyne Bumblebee stereo camera, Oculus M3000d imaging sonar, motor inputs, and GNSS. This dataset was collected on a lake and reservoir in Ontario, Canada. The intended purpose of this dataset is to enable the development and benchmarking of autonomous navigation algorithms in aquatic environments. In the future, we hope to release localization and odometr",
"managed_by": "[Autonomous Space Robotics Laboratory (ASRL)](http://asrl.utias.utoronto.ca)",
"update_frequency": "New sequences will be added as they are collected.",
"tags": "aws-pds,autonomous vehicles,robotics,computer vision,lidar,radar",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)",
"deprecated": false
},
{
"slug": "capella_opendata",
"name": "Capella Space Synthetic Aperture Radar (SAR) Open Dataset",
"description": "Open Synthetic Aperture Radar (SAR) data from Capella Space. Capella Space is an information services company\n that provides on-demand, industry-leading, high-resolution synthetic aperture radar (SAR) Earth observation\n imagery. Through a constellation of small satellites, Capella provides easy access to frequent, timely, and\n flexible information affecting dozens of industries worldwide. Capella's high-resolution SAR satellites are\n matched with unparalleled infrastructure to deliver reliab",
"managed_by": "[Capella Space](https://www.capellaspace.com/)",
"update_frequency": "New data is added quarterly.",
"tags": "aws-pds,cog,stac,earth observation,satellite imagery,geospatial,image processing,computer vision,synthetic aperture radar",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\n",
"deprecated": false
},
{
"slug": "carbonpdf",
"name": "CarbonPDF",
"description": "A carbon question-answering (QA) dataset specifically designed to facilitate the extraction and analysis of data from real-world carbon reports of computing products. The dataset features annotated metadata, a variety of numerical reasoning tasks, and structured derivations to ensure accurate processing of fragmented and inconsistent information.",
"managed_by": "Pittcps lab",
"update_frequency": "Data for a new company is added once collected.",
"tags": "aws-pds,environmental,product comparison,csv,information retrieval,industry",
"license": "CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "cartostore",
"name": "CartoStore",
"description": "Cross-Platform Repository for High-resolution Spatial Transcriptomics Datasets.\n",
"managed_by": "[Hyun Min Kang](https://scholar.google.com/citations?user=8e0jy0IAAAAJ&hl=en)",
"update_frequency": "Monthly",
"tags": "spatial transcriptomics,spatial omics,genomic,bioinformatics,life sciences",
"license": "\"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\"\n",
"deprecated": false
},
{
"slug": "catalyst-cooperative-pudl",
"name": "Public Utility Data Liberation Project",
"description": "The Public Utility Data Liberation Project (PUDL) provides analysis-ready U.S. energy\nsystem data in bulk for programmatic use. Sources include the U.S. Energy\nInformation Administration (EIA), the Environmental Protection Agency (EPA), the\nFederal Energy Regulatory Commission (FERC), the Pipeline and Hazardous Materials\nSafety Administration (PHMSA), the Securities and Exchange Commission (SEC). The\nprimary focus is on the electricity sector, with additional data on the natural gas\nsystem and ",
"managed_by": "[Catalyst Cooperative](https://catalyst.coop/)",
"update_frequency": "New outputs published nightly as part of our continuous integration process.\nNew data is integrated on a rolling basis as it is published by the original sources.\nAgencies typically release data monthly, quarterly, or yearly.\nNew versioned releases of PUDL are published and archived at least once per quarter.\n",
"tags": "aws-pds,economics,electricity,energy,energy modeling,environmental,geospatial,government records,industry,industrial",
"license": "[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) (Creative Commons Attribution 4.0 International License).",
"deprecated": false
},
{
"slug": "cbers",
"name": "CBERS on AWS",
"description": "Imagery acquired\nby the China-Brazil Earth Resources Satellite (CBERS), 4 and 4A.\nThe\nimage files are recorded and processed by Instituto Nacional de Pesquisas\nEspaciais (INPE) and are converted to Cloud Optimized Geotiff\nformat in order to optimize its use for cloud based applications.\nContains all CBERS-4 MUX, AWFI, PAN5M and\nPAN10M scenes acquired since\nthe start of the satellite mission and is daily updated with\nnew scenes.\nCBERS-4A MUX Level 4 (Orthorectified) scenes are being\ningested star",
"managed_by": "[Frederico Liporace](https://github.com/fredliporace)",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,earth observation,geospatial,imaging,satellite imagery,disaster response,stac,cog",
"license": "https://creativecommons.org/licenses/by-sa/3.0/",
"deprecated": false
},
{
"slug": "ccic",
"name": "Chalmers Cloud Ice Climatology",
"description": "The Chalmers Cloud Ice Climatology (CCIC) is a novel, deep-learning-based climate record of ice-particle concentrations in the atmosphere. CCIC results are available at high spatial and temporal resolution (0.07° / 3 h from 1983, 0.036° / 30 min from 2000) and thus ideally suited for evaluating high-resolution weather and climate models or studying individual weather systems.",
"managed_by": "[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)",
"update_frequency": "Quarterly",
"tags": "atmosphere,aws-pds,climate,deep learning,environmental,exploration,geophysics,geoscience,geospatial,global",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "ccle",
"name": "Cancer Cell Line Encyclopedia (CCLE)",
"description": "The Cancer Cell Line Encyclopedia (CCLE) project is an effort to conduct a detailed genetic\ncharacterization of a large panel of human cancer cell lines. The CCLE provides public access to\ngenomic data, visualization and analysis for over 1100 cancer cell lines. This dataset contains\nRNA-Seq Aligned Reads, WXS Aligned Reads, and WGS Aligned Reads data.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genetic,genomic,life sciences,transcriptomics,whole genome sequencing,Homo sapiens,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "ccrsmodisalbedo",
"name": "CCRS MODIS albedo over Canada | Albédo MODIS du CCT couvrant le Canada",
"description": "Times series of 10-day spectral and broadband albedo products derived at 250-m spatial resolution over Canadian territory and neighboring areas produced at the Canada Centre for Remote Sensing (CCRS) since February 2000 using MODIS L1B C6.1 swath imagery as input. The imagery for all spectral bands was downscaled and re-projected into the Lambert Conformal Conic (LCC) projection at 250-m spatial resolution. The area size is 5,700 km x 4,800 km (22,800 pixel x 19,200 lines).\nSéries temporelles d",
"managed_by": "Canada Centre for Remote Sensing (CCRS), Canada Centre for Mapping and Earth Observation (CCMEO), Department of Natural Resources Canada (NRCan) https://natural-resources.canada.ca/science-data/scienc",
"update_frequency": "Semi-annually, until the end of MODIS operations\nDeux fois par an, jusqu'à la fin des opérations MODIS\n",
"tags": "aws-pds,analysis ready data,broadband,cog,earth observation,satellite imagery",
"license": "Creative Commons Licence. Creative Commons BY 4.0 https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "cell-painting-image-collection",
"name": "Cell Painting Image Collection",
"description": "The Cell Painting Image Collection is a collection of freely\ndownloadable microscopy image sets. Cell Painting is an\nunbiased high throughput imaging assay used to analyze\nperturbations in cell models. In addition to the images\nthemselves, each set includes a description of the biological\napplication and some type of \"ground truth\" (expected results).\nResearchers are encouraged to use these image sets as reference\npoints when developing, testing, and publishing new image\nanalysis algorithms for ",
"managed_by": "The Broad Institute",
"update_frequency": "irregularly",
"tags": "aws-pds,microscopy,biology,life sciences,imaging,high-throughput imaging,cell imaging,cell painting,fluorescence imaging",
"license": "CC0 1.0 Universal (CC0 1.0) Public Domain Dedication",
"deprecated": false
},
{
"slug": "cellpainting-gallery",
"name": "Cell Painting Gallery",
"description": "The Cell Painting Gallery is a collection of image datasets created using the [Cell Painting](https://pubmed.ncbi.nlm.nih.gov/27560178/) assay. \nThe images of cells are captured by microscopy imaging, and reveal the response of various labeled cell components to whatever treatments are tested, which can include genetic perturbations, chemicals or drugs, or different cell types. \nThe datasets can be used for diverse applications in basic biology and pharmaceutical research, such as identifying di",
"managed_by": "Carpenter-Singh and Cimini Labs at the Broad Institute",
"update_frequency": "Typically when an associated publication is posted on biorxiv",
"tags": "aws-pds,bioinformatics,biology,cancer,cell biology,cell imaging,cell painting,chemical biology,computer vision,csv",
"license": "CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, but please do cite the corresponding publication for each dataset, as listed [here](https://github.com/broadinstitute/cellpainting-gallery#citation).",
"deprecated": false
},
{
"slug": "census-2010-amc-mdf-replicates",
"name": "Estimating Confidence Intervals for 2020 Census Statistics Using Approximate Monte Carlo Simulation (2010 Census Proof of Concept)",
"description": "The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF25) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Decennial Census Disclosure Avoidance System (DAS) into the Redistricting and DHC products. The PPMF0 was created by executing the 2020 DAS TopDown Algorithm (TDA) using the con",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Not Updated",
"tags": "census,differential privacy,disclosure avoidance,ethnicity,group quarters,hispanic,latino,housing,housing units,noisy measurements",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-2010-dhc-nmf",
"name": "2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File",
"description": "The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-06-30) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorit",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Not Updated",
"tags": "aws-pds,census,differential privacy,disclosure avoidance,ethnicity,group quarters,hispanic,latino,housing,housing units",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-2010-pl94-nmf",
"name": "2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File",
"description": "The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic paramete",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Last updated November 10, 2023: Modifications to identifiers within the parquet metadata used to support internal tracking of source data.",
"tags": "aws-pds,census,differential privacy,disclosure avoidance,ethnicity,group quarters,hispanic,latino,housing,housing units",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-2020-amc-mdf-replicates",
"name": "Estimating Confidence Intervals for 2020 Census Statistics Using Approximate Monte Carlo Simulation (2020 Census Production Run)",
"description": "The 2020 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF50) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Census Disclosure Avoidance System (DAS) into the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographi",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Not Updated",
"tags": "census,decennial census,2020 census,differential privacy,disclosure avoidance,ethnicity,group quarters,hispanic,latino,housing",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-2020-dhc-nmf",
"name": "2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File",
"description": "The 2020 Census Demographic and Housing Characteristics Noisy Measurement File is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in [primitives.py](https://github.com/uscensusbureau/DAS_2020_DHC_Production_Code/blob/main/das_decennial/programs/engine/primitives.py)). The 2020 Census Demographic and Housing Characteristics Noisy Measurement File includes zero-Concentrated Differentially P",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Not Updated",
"tags": "aws-pds,census,differential privacy,disclosure avoidance,ethnicity,group quarters,housing,housing units,noisy measurements,population",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-2020-pl94-gls",
"name": "2020 Redistricting Data File Least Squares Estimates",
"description": "The 2020 Redistricting Data File Least Squares Estimates data product provides count estimates, and their standard deviations, for each tabulation that was published as part of the persons universe of the 2020 Redistricting Data File for the US, state, county, and tract geographic levels. These estimates are computed using the generalized least squares (GLS) estimator using as input the publicly available 2020 Census persons universe noisy measurement files for both [the Redistricting Data File]",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Not Updated",
"tags": "aws-pds,census,differential privacy,disclosure avoidance,ethnicity,group quarters,housing,housing units,noisy measurements,population",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-2020-pl94-nmf",
"name": "2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File",
"description": "The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File (NMF) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9, and implemented in the [DAS 2020 Redistricting Production Code](https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code)). The NMF was generated using [the Census Bureau's implementation](https://github.com/uscensusbure",
"managed_by": "[United States Census Bureau](http://www.census.gov/)",
"update_frequency": "Not Updated",
"tags": "aws-pds,census,differential privacy,disclosure avoidance,ethnicity,group quarters,housing,housing units,noisy measurements,population",
"license": "CC0 1.0 Universal",
"deprecated": false
},
{
"slug": "census-dataworld-pums",
"name": "U.S. Census ACS PUMS",
"description": "U.S. Census Bureau American Community Survey (ACS) Public Use Microdata Sample (PUMS) available in a linked data format using the Resource Description Framework (RDF) data model.",
"managed_by": "Data.world",
"update_frequency": "Yearly, after ACS 1-year PUMS raw data are released",
"tags": "aws-pds,statistics,census,survey",
"license": "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "cesm-hr",
"name": "CESM-HR",
"description": "This dataset provides several global fields describing the state of atmosphere, ocean, land and ice from a high-resolution (0.1o for the ocean/ice models 0.25o for the land/atmosphere models) numerical earth system model, the Community Earth System Model (CESM, https://www.cesm.ucar.edu/). Texas A&M University (TAMU) and National Center for Atmospheric Research together with international partners collaboratively carried out a large set of high-resolution climate simulations, including a 500-yea",
"managed_by": "[TAMU](https://www.tamu.com/)",
"update_frequency": "Rare. The CESM-HR PI-CTRL experiment is complete. Updates are expected only if any issues with the copy of the data on the AWS is reported in the future. Other experiments will be shared in the future according to the availability of resources.",
"tags": "aws-pds,climate,climate model,climate projections,CMIP6,ocean circulation,ocean currents,ocean velocity,ocean sea surface height,ocean simulation",
"license": "This dataset is created in collaboration with NCAR and the NCAR’s “Creative Commons Attribution 4.0 International license” used for their CESM2 LENS data product on AWS (https://www.ucar.edu/terms-of-use/data) will be applicable for the CESM-HR PI-CTRL datasets too.",
"deprecated": false
},
{
"slug": "cgci",
"name": "Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer",
"description": "The Cancer Genome Characterization Initiatives (CGCI) program supports cutting-edge genomics research of adult and pediatric cancers. CGCI investigators develop and apply advanced sequencing methods that examine genomes, exomes, and transcriptomes within various types of tumors. The program includes Burkitt Lymphoma Genome Sequencing Project (BLGSP) project and HIV+ Tumor Molecular Characterization Project - Cervical Cancer (HTMCP-CC) project.\nThe dataset contains open Clinical Supplement, Biosp",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences,transcriptomics,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "cgiardata",
"name": "CCAFS-Climate Data",
"description": "High resolution climate data to help assess the impacts of climate change primarily on agriculture. These open access datasets of climate projections will help researchers make climate change impact assessments.",
"managed_by": "[International Center for Tropical Agriculture](https://ciat.cgiar.org/)",
"update_frequency": "Every three months",
"tags": "aws-pds,agriculture,food security,climate,sustainability",
"license": "Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/",
"deprecated": false
},
{
"slug": "challenge-2021",
"name": "Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021",
"description": "The electrocardiogram (ECG) is a non-invasive representation of the electrical activity of the heart. Although the twelve-lead ECG is the standard diagnostic screening system for many cardiological issues, the limited accessibility of twelve-lead ECG devices provides a rationale for smaller, lower-cost, and easier to use devices. While single-lead ECGs are limiting [1], reduced-lead ECG systems hold promise, with evidence that subsets of the standard twelve leads can capture useful information [",
"managed_by": "[PhysioNet](https://physionet.org/)",
"update_frequency": "Not updated",
"tags": "aws-pds",
"license": "Creative Commons Attribution 4.0 International Public License",
"deprecated": false
},
{
"slug": "chammi",
"name": "CHAMMI-75",
"description": "Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. \nHowever, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. \nThis results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), \nor because the ",
"managed_by": "Morgridge Institute for Research",
"update_frequency": "Every 2 years",
"tags": "microscopy,machine learning,biology,life sciences,imaging,high-throughput imaging,cell imaging,fluorescence imaging,aws-pds",
"license": "CC BY 4.0 License",
"deprecated": false
},
{
"slug": "chembl",
"name": "ChEMBL - Data Lakehouse Ready",
"description": "ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. This representation of ChEMBL is stored in Parquet format and most easily utilized through Amazon Athena. Follow the documentation for install instructions (< 2 minute install). New ChEMBL releases occur sporadically; the most up to date information on ChEMBL releases can be found ",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Upon request. We try to keep it updated to every odd version.",
"tags": "chemistry,genomic,molecule,life sciences,biotech blueprint,parquet",
"license": "https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt",
"deprecated": true
},
{
"slug": "chimera",
"name": "CHIMERA",
"description": "This dataset contains the training data for the [CHIMERA - Combining HIstology, Medical imaging (radiology) and molEcular data for medical pRognosis and diAgnosis](https://chimera.grand-challenge.org/) challenge. The CHIMERA Challenge aims to advance precision medicine in cancer care by addressing the critical need for multimodal data integration. Despite significant progress in AI, integrating transcriptomics, pathology, and radiology across clinical departments remains a complex challenge. Cli",
"managed_by": "Radboud University Medical Center",
"update_frequency": "As required",
"tags": "aws-pds,life sciences,cancer,computational pathology,deep learning,grand-challenge.org,histopathology,computer vision,digital pathology,medical image computing",
"license": "CC BY-NC-SA 4.0",
"deprecated": false
},
{
"slug": "citrus-farm",
"name": "CitrusFarm Dataset",
"description": "CitrusFarm is a multimodal agricultural robotics dataset that provides both multispectral images and navigational sensor data for localization, mapping and crop monitoring tasks. \n1) It was collected by a wheeled mobile robot in the Agricultural Experimental Station at the University of California Riverside in the summer of 2023. \n2) It offers a total of nine sensing modalities, including stereo RGB, depth, monochrome, near-infrared and thermal images, as well as wheel odometry, LiDAR, IMU and G",
"managed_by": "[Autonomous Robots and Control Systems Lab](https://sites.google.com/view/arcs-lab)",
"update_frequency": "NA",
"tags": "aws-pds,robotics,computer vision,agriculture,localization,mapping,lidar,IMU",
"license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).",
"deprecated": false
},
{
"slug": "civic",
"name": "CIViC (Clinical Interpretation of Variants in Cancer)",
"description": "Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort.",
"managed_by": "The Griffith Lab at Washington University School of Medicine",
"update_frequency": "First of each month",
"tags": "aws-pds,genetic,genomic,life sciences,vcf,cancer",
"license": "[CC0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "clay-model-v0-embeddings",
"name": "Clay Model v0 Embeddings",
"description": "Machine learning model embeddings dataset providing pre-computed feature representations for satellite and aerial imagery analysis.",
"managed_by": "[Source Cooperative](https://source.coop/)",
"update_frequency": "As new model versions become available",
"tags": "machine learning,computer vision,satellite imagery,aerial imagery,earth observation,imaging",
"license": "Creative Commons Attribution 4.0 International License",
"deprecated": false
},
{
"slug": "clay-v1-5-naip-2",
"name": "Clay v1.5 NAIP-2",
"description": "National Agriculture Imagery Program (NAIP) dataset providing high-resolution aerial imagery for agricultural monitoring, land use analysis, and natural resource management.",
"managed_by": "[Source Cooperative](https://source.coop/)",
"update_frequency": "As new NAIP data becomes available",
"tags": "aerial imagery,agriculture,land use,natural resource,environmental",
"license": "Creative Commons Attribution 4.0 International License",
"deprecated": false
},
{
"slug": "clay-v1-5-sentinel2",
"name": "Clay v1.5 Sentinel-2",
"description": "Sentinel-2 satellite imagery dataset providing high-resolution optical data for land monitoring, agriculture, and environmental applications.",
"managed_by": "[Source Cooperative](https://source.coop/)",
"update_frequency": "As new Sentinel-2 data becomes available",
"tags": "satellite imagery,earth observation,agriculture,land use,environmental",
"license": "Creative Commons Attribution 4.0 International License",
"deprecated": false
},
{
"slug": "clinical-ultrasound-image-data",
"name": "Clinical Ultrasound Image Repository",
"description": "Generic Clinical Ultrasound Data from Random Subjects acquired for Clinical Reasons, to be used for Developing Artificial Intelligence Applications. This dataset is complete with 2000 studies from 2000 subjects (one third each from abdominal, cardiac, and OB/GYN cases)",
"managed_by": "[MONAI Development Team](https://github.com/Project-MONAI/MONAI)",
"update_frequency": "This is a static dataset; however, tutorials and resources will be updated as they are developed.",
"tags": "medicine,medical imaging,machine learning,life sciences,aws-pds",
"license": "[CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "clinvar",
"name": "ClinVar - Data Lakehouse Ready",
"description": "ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. ClinVar thus facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation. ClinVar processes submissions reporting variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other suppo",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Every Sunday at 1AM UTC",
"tags": "chemistry,genetic,genomic,life sciences,biotech blueprint,parquet",
"license": "https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt",
"deprecated": true
},
{
"slug": "cmas-data-warehouse",
"name": "CMAS Data Warehouse",
"description": "CMAS Data Warehouse on AWS collects and disseminates meteorology, emissions and air quality model input and output for Community Multiscale Air Quality (CMAQ) Model Applications. This dataset is available as part of the AWS Open Data Program, therefore egress fees are not charged to either the host or the person downloading the data. This S3 bucket is maintained as a public service by the University of North Carolina's CMAS Center, the US EPA’s Office of Research and Development, and the US EPA",
"managed_by": "[CMAS CENTER](https://cmascenter.org/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,air quality,meteorological,geospatial,environmental,climate",
"license": "There are no restrictions on the use of this data. US EPA License (https://pasteur.epa.gov/license/sciencehub-license.html)",
"deprecated": false
},
{
"slug": "cmip6-era5-hybrid-southeast-asia",
"name": "Hybrid statistical-dynamic downscaling based on multi-model ensembles in Southeast Asia",
"description": "GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be ",
"managed_by": "[PREP-NexT Lab](https://github.com/PREP-NexT)",
"update_frequency": "Update when needed.",
"tags": "climate,netcdf,precipitation,aws-pds",
"license": "All the code in this repository is [MIT](https://choosealicense.com/licenses/mit/) licensed, but we request that you please provide attribution if reusing any of our digital content (graphics, logo, copy, etc.).",
"deprecated": false
},
{
"slug": "cmip6",
"name": "Coupled Model Intercomparison Project 6",
"description": "The sixth phase of global coupled ocean-atmosphere general circulation model ensemble. \n
\n",
"managed_by": "ESGF and Pangeo",
"update_frequency": "Core CMIP6 datasets are added as soon as they are available.",
"tags": "aws-pds,agriculture,atmosphere,climate,earth observation,environmental,model,oceans,simulations,weather",
"license": "See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation.html) for further info",
"deprecated": false
},
{
"slug": "cmsdesynpuf-omop",
"name": "CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) in OMOP Common Data Model",
"description": "DE-SynPUF is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,300,000 persom (2.3m) data sets in the [OMOP Common Data Model](https://www.ohdsi.org/data-standardization/) format. The DE-SynPUF was created with the goal of providing a realistic set of claims data in the public domain while providing the very highest degree of protection to the Medicare beneficiaries’ protected health information. The purposes of the DE-SynPUF are to: \n1. allow data entrepreneurs to develop and",
"managed_by": "[Amazon Web Sevices](https://aws.amazon.com/)",
"update_frequency": "Not updated",
"tags": "amazon.science,bioinformatics,health,life sciences,natural language processing,us",
"license": "https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/Downloads/SynPUF_DUG.pdf",
"deprecated": false
},
{
"slug": "coawst",
"name": "USGS COAWST (Coupled Ocean Atmosphere Wave and Sediment Transport) Forecast Model Archive, US East and Gulf Coasts",
"description": "The COAWST modeling system has been used to simulate ocean, wave and sediment transport processes along the of US East Coast and Gulf of Mexico. The grid has a horizontal resolution of approximately 5km and is resolved with 16 vertical terrain following levels. The model has been executed on a daily basis since August 2009 with outputs written every hour. This archive contains model output from 2009-08-21 to 2022-06-17.",
"managed_by": "Fathom Science",
"update_frequency": "None",
"tags": "aws-pds,oceans",
"license": "CC0",
"deprecated": false
},
{
"slug": "cobra",
"name": "COBRA",
"description": "This page describes the COBRA (Classification Of Basal cell carcinoma, Risky skin cancers and Abnormalities) skin pathology dataset, which comprises over 7000 histopathology whole-slide-images related to the diagnosis of basal cell carcinoma skin cancer, the most commonly diagnosed cancer. The dataset includes biopsies and excisions and is divided into four groups. The first group contains about 2,500 BCC biopsies with subtype labels, while the second group includes 2,500 non-BCC biopsies with d",
"managed_by": "Radboud University Medical Center",
"update_frequency": "As required",
"tags": "aws-pds,life sciences,cancer,computational pathology,deep learning,histopathology,computer vision",
"license": "CC BY-SA-NC 4.0",
"deprecated": false
},
{
"slug": "code-mixed-ner",
"name": "Multilingual Name Entity Recognition (NER) Datasets with Gazetteer",
"description": "Name Entity Recognition datasets containing short sentences and queries with low-context,\nincluding LOWNER, MSQ-NER, ORCAS-NER and Gazetteers (1.67 million entities).\nThis release contains the multilingual versions of the datasets in [Low Context Name Entity Recognition (NER) Datasets with Gazetteer](https://registry.opendata.aws/lowcontext-ner-gaz/).\n",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "N/A",
"tags": "amazon.science,natural language processing",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "colorado-elevation-data",
"name": "State of Colorado Elevation Data",
"description": "The State of Colorado has gathered public historical elevation data.",
"managed_by": "State of Colorado Governors Office of Information Technology OIT GIS team",
"update_frequency": "Periodically",
"tags": "aws-pds,geospatial,imaging,mapping",
"license": "https://creativecommons.org/publicdomain/zero/1.0/legalcode",
"deprecated": false
},
{
"slug": "colorado-imagery",
"name": "State of Colorado Imagery",
"description": "The State of Colorado has gathered public historical imagery ranging from 2005 to 2021.",
"managed_by": "State of Colorado, Governor's Office of Information Technology OIT GIS team",
"update_frequency": "Periodically",
"tags": "aws-pds,aerial imagery,geospatial,imaging,mapping",
"license": "https://creativecommons.org/publicdomain/zero/1.0/legalcode",
"deprecated": false
},
{
"slug": "commoncrawl",
"name": "Common Crawl",
"description": "A corpus of web crawl data composed of over 300 billion web pages.",
"managed_by": "[Common Crawl](https://commoncrawl.org/)",
"update_frequency": "Monthly",
"tags": "aws-pds,encyclopedic,natural language processing,internet,web archive",
"license": "This data is available for anyone to use under the [Common Crawl Terms of Use](https://commoncrawl.org/terms-of-use/)",
"deprecated": false
},
{
"slug": "comonscreens",
"name": "Common Screens",
"description": "A corpus of web screenshot and metadata data composed of over 70 million websites.",
"managed_by": "[Common Screens](https://commonscreens.com/)",
"update_frequency": "Monthly",
"tags": "aws-pds,encyclopedic,natural language processing,internet",
"license": "[Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "copernicus-dem",
"name": "Copernicus Digital Elevation Model (DEM)",
"description": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-90 provides worldwide coverage at 90 meters. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that in both cases ocean areas do not ",
"managed_by": "[Sinergise](https://www.sinergise.com/)",
"update_frequency": "None, except GLO-30 Public can be updated if the public tile list changes.",
"tags": "aws-pds,agriculture,elevation,earth observation,satellite imagery,geospatial,disaster response,cog",
"license": "GLO-30 Public and GLO-90 are available on a free basis for the general public under the terms and conditions of the Licence found on [here](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM).\n",
"deprecated": false
},
{
"slug": "coralreef-image-classification-training",
"name": "Community coral reef image classification training data",
"description": "Community-sourced repository of coral reef image classification training data, including continually updated confirmed annotations from [MERMAID](https://datamermaid.org/)",
"managed_by": "[MERMAID](https://datamermaid.org/)",
"update_frequency": "Each partner organization updates on their own cadence. MERMAID updates once per day.",
"tags": "aws-pds,coastal,conservation,coral reef,csv,global,machine learning,marine,parquet,survey",
"license": "[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)",
"deprecated": false
},
{
"slug": "cord-19",
"name": "COVID-19 Open Research Dataset (CORD-19)",
"description": "Full-text and metadata dataset of COVID-19 and coronavirus-related research articles optimized for machine readability.",
"managed_by": "Allen Institute for AI",
"update_frequency": "Weekly",
"tags": "aws-pds,COVID-19,coronavirus,life sciences,SARS,MERS",
"license": "Open (see license file for details)",
"deprecated": false
},
{
"slug": "cornell-eas-data-lake",
"name": "Cornell EAS Data Lake",
"description": "Earth & Atmospheric Sciences at Cornell University has created a public data lake of climate data. The data is stored in columnar storage formats (ORC) to make it straightforward to query using standard tools like Amazon Athena or Apache Spark. The data itself is originally intended to be used for building decision support tools for farmers and digital agriculture. The first dataset is the historical NDFD / NDGD data distributed by NCEP / NOAA / NWS. The NDFD (National Digital Forecast Database)",
"managed_by": "Not currently managed",
"update_frequency": "Hourly",
"tags": "agriculture,aws-pds,climate,earth observation,elevation,environmental,geospatial,mapping,meteorological,weather",
"license": "https://datalake.eas.cornell.edu/license.txt",
"deprecated": true
},
{
"slug": "cotonoha-dic",
"name": "Japanese Tokenizer Dictionaries",
"description": "Japanese Tokenizer Dictionaries for use with MeCab.",
"managed_by": "Cotonoha",
"update_frequency": "Infrequently (typically less than once a year)",
"tags": "aws-pds,natural language processing,csv,japanese",
"license": "Versions of Unidic offered here are available under the GPL/LGPL/BSD license.\n\nIPADic is offered under a unique BSD-like license. See below. \n\n https://github.com/polm/ipadic-py/blob/master/ipadic/dicdir/COPYING\n",
"deprecated": false
},
{
"slug": "covers-br",
"name": "CoversBR",
"description": "CoversBR is the first large audio database with, predominantly, Brazilian music for the tasks of Covers Song\nIdentification (CSI) and Live Song Identifications (LSI). Due to copyright restrictions audios of\nthe songs cannot be made available, however metadata and files of features have public access. Audio\nstreamings captured from radio and TV channels for the live song identification task will be made public.\nCoversBR is composed of metadata and features extracted from 102298 songs, distributed",
"managed_by": "Dirceu G Silva",
"update_frequency": "New metadata, songs features files and audio streamings for live song identification will be added as soon as available.",
"tags": "aws-pds,copyright monitoring,cover song identification,live song identification,music,music features dataset,music information retrieval,music recognition",
"license": "The code in this repository is licensed under Apache 2.0\n\nThe metadata and the pre-extracted features are licensed under CC BY-NC-SA 4.0\n",
"deprecated": false
},
{
"slug": "cptac-2",
"name": "Clinical Proteomic Tumor Analysis Consortium 2 (CPTAC-2)",
"description": "The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the\nunderstanding of the molecular basis of cancer through the application of large-scale proteome and\ngenome analysis, or proteogenomics. CPTAC-2 is the Phase II of the CPTAC Initiative (2011-2016).\nDatasets contain open RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression\nQuantification, and miRNA Expression Quantification data.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences,transcriptomics,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "cptac-3",
"name": "Clinical Proteomic Tumor Analysis Consortium 3 (CPTAC-3)",
"description": "The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the\nunderstanding of the molecular basis of cancer through the application of large-scale proteome and\ngenome analysis, or proteogenomics. CPTAC-3 is the Phase III of the CPTAC Initiative. The dataset\ncontains open RNA-Seq Gene Expression Quantification data.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences,transcriptomics,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "craam-open-vlf",
"name": "Open VLF: Scientific Open Data Initiative for CRAAM's SAVNET and AWESOME VLF Data.",
"description": "This platform is maintained by [CRAAM](https://www.mackenzie.br/centro-de-radio-astronomia-e-astrofisica-mackenzie) (Mackenzie Radio Astronomy and Astrophysics Center), a research center operated by [UPM](https://www.mackenzie.br/) (Mackenzie Presbyterian University) and [INPE](https://www.gov.br/inpe) (National Institute for Space Research), to provide public and free access for researchers, students, and the interested public to VLF (Very Low Frequency) data from CRAAM's antenna systems. Amazo",
"managed_by": "[CRAAM Mackenzie](https://www.mackenzie.br/centro-de-radio-astronomia-e-astrofisica-mackenzie)",
"update_frequency": "Various. Data since 2006, and still updated. Follow the announcements and what is new on the project website [Open VLF](https://open-vlf.web.app).",
"tags": "archives,astronomy,atmosphere,aws-pds,global,open source software,signal processing,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "cropland_partitioining",
"name": "IWMI DIWASA Rainfed and Irrigated Cropland Map for Africa",
"description": "A framework integrating the Budyko model has been developed to distinguish between rainfed and irrigated cropland areas across Africa. This expands on remote sensing land cover products available for agricultural water studies in Africa and thereby helps address the need for deeper insights into cropland patterns. Validation against an independent dataset revealed an overall accuracy of 73% with high precision and specificity scores. These results validate the framework’s effectiveness in identi",
"managed_by": "[IWMI](https://www.iwmi.org/)",
"update_frequency": "None",
"tags": "cropland partitioning,irrigated cropland,rainfed cropland,agriculture,land use,land cover",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "cryoet-data-portal",
"name": "CryoET Data Portal",
"description": "Cryo-electron tomography (cryoET) is a powerful technique for visualizing 3D structures of cellular macromolecules at near atomic resolution in their native environment. Observing the inner workings of cells in context enables better understanding about the function of healthy cells and the changes associated with disease. However, the analysis of cryoET data remains a significant bottleneck, particularly the annotation of macromolecules within a set of tomograms, which often requires a laboriou",
"managed_by": "[Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/)",
"update_frequency": "New releases are published on a rolling basis. Please contact the team via email for any questions.",
"tags": "cryo electron tomography,electron tomography,cell biology,structural biology,machine learning,segmentation,czi,life sciences",
"license": "CC0",
"deprecated": false
},
{
"slug": "cse-cic-ids2018",
"name": "A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018)",
"description": "This dataset is the result of a collaborative project between the Communications Security Establishment (CSE) and The Canadian Institute for Cybersecurity (CIC) that use the notion of profiles to generate cybersecurity dataset in a systematic manner. It incluides a detailed description of intrusions along with abstract distribution models for applications, protocols, or lower level network entities. The dataset includes seven different attack scenarios, namely Brute-force, Heartbleed, Botnet, Do",
"managed_by": "Canadian Institute for Cybersecurity",
"update_frequency": "Annually",
"tags": "aws-pds,network traffic,internet,intrusion detection,cyber security,aws-pds",
"license": "http://www.unb.ca/cic/datasets/ids-2018.html",
"deprecated": false
},
{
"slug": "csiro-cafe60",
"name": "CAFE60 reanalysis",
"description": "The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) provides a large ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatio-temporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmos",
"managed_by": "[CSIRO](http://csiro.au/)",
"update_frequency": "6 Monthly (Approx)",
"tags": "aws-pds,climate,sustainability",
"license": "Creative Commons Attribution-ShareAlike 4.0 International Licence",
"deprecated": false
},
{
"slug": "ctrees-amazon-canopy-height",
"name": "Canopy Tree Height Map for the Amazon Forest (mean height composite 2020-2024) by CTrees.org",
"description": "Mean canopy Tree Height for the Amazon Forest on the period 2020-2024 at 4.78 m of spatial resolution. Created using a deep learning model on high-resolution Planet imagery from the Norway's International Climate and Forest Initiative (NICFI) Satellite Data Program. From the original research paper https://doi.org/10.48550/arXiv.2501.10600\n",
"managed_by": "[CTrees](https://ctrees.org/)",
"update_frequency": "TBD",
"tags": "aws-pds,cog,earth observation,land cover,deep learning,lidar,satellite imagery,image processing,environmental,conservation",
"license": "Our canopy height map is a derivative product of Planet-NICFI and follows the same licence: [NICFI licensing agreement](https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024)",
"deprecated": false
},
{
"slug": "ctrees-california-vhr-tree-height",
"name": "Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org",
"description": "Canopy Tree Height maps for California in 2020. Created using a deep learning model on very-high-resolution airborne imagery from the National Agriculture Imagery Program (NAIP) by United States Department of Agriculture (USDA).\n",
"managed_by": "[CTrees](https://ctrees.org/)",
"update_frequency": "TBD",
"tags": "aws-pds,cog,earth observation,land cover,deep learning,aerial imagery,image processing,environmental,conservation,geospatial",
"license": "https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "ctsp-dlbcl",
"name": "Clinical Trial Sequencing Project - Diffuse Large B-Cell Lymphoma",
"description": "The goal of the project is to identify recurrent genetic alterations (mutations, deletions,\namplifications, rearrangements) and/or gene expression signatures. National Cancer Institute (NCI)\nutilized whole genome sequencing and/or whole exome sequencing in conjunction with transcriptome\nsequencing. The samples were processed and submitted for genomic characterization using pipelines\nand procedures established within The Cancer Genome Analysis (TCGA) project.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences,transcriptomics,whole genome sequencing,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "cwa_opendata",
"name": "Central Weather Administration OpenData",
"description": "Various kinds of weather raw data and charts from Central Weather Administration.",
"managed_by": "[Central Weather Administration](https://www.cwa.gov.tw/)",
"update_frequency": "Data is updated as soon as newer one is available.",
"tags": "aws-pds,climate,earth observation,earthquakes,satellite imagery,weather",
"license": "http://data.gov.tw/license",
"deprecated": false
},
{
"slug": "cwb_opendata",
"name": "Central Weather Bureau OpenData",
"description": "Various kinds of weather raw data and charts from Central Weather Bureau.",
"managed_by": "[Central Weather Bureau](https://www.cwb.gov.tw/)",
"update_frequency": "Data is updated as soon as newer one is available.",
"tags": "aws-pds,climate,earth observation,earthquakes,satellite imagery,weather",
"license": "http://data.gov.tw/license",
"deprecated": false
},
{
"slug": "czb-opencell",
"name": "OpenCell on AWS",
"description": "The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins\nusing high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome.\nThis dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library. \nThese images can be interpreted both individually, to determine the localization of particular proteins of interest,\nand in aggregate, by training machine lea",
"managed_by": "[Chan Zuckerberg Biohub](https://www.czbiohub.org/)",
"update_frequency": "This is the final version of the dataset.",
"tags": "aws-pds,biology,cell biology,life sciences,imaging,cell imaging,fluorescence imaging,microscopy,computer vision,machine learning",
"license": "CC BY-SA 4.0",
"deprecated": false
},
{
"slug": "czi-benchmarking",
"name": "CZ Grand Challenges - Model Benchmarking",
"description": "This dataset includes data and models relevant to benchmarking multimodal biological models. The data has been sourced and curated by a team of experts at CZI and is provided as part of these datasets only when it is not publicly available or requires transformation to support effective model benchmarking.",
"managed_by": "[Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/)",
"update_frequency": "Update cadence may vary for each resource",
"tags": "benchmark,biology,biomolecular modeling,czi,life sciences,machine learning,model,cell biology",
"license": "CC0 1.0",
"deprecated": false
},
{
"slug": "czi-cellxgene-census",
"name": "CZ CELLxGENE Discover Census",
"description": "CZ CELLxGENE Discover ([cellxgene.cziscience.com](https://cellxgene.cziscience.com/)) is a free-to-use platform for the exploration, analysis, and retrieval of single-cell data. CZ CELLxGENE Discover hosts the largest aggregation of standardized single-cell data from the major human and mouse tissues, with modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics.\nThis year, CZ CELLxGENE Discover has made available all of its human and mouse R",
"managed_by": "[Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/)",
"update_frequency": "New releases are published weekly. Long-term supported (LTS) releases are published every 6 months.",
"tags": "aws-pds,single-cell transcriptomics,transcriptomics,cell biology,bioinformatics,life sciences",
"license": "CC BY license",
"deprecated": false
},
{
"slug": "czi-imaging-bsd",
"name": "CZ Grand Challenges - Imaging BSD licensed data and models",
"description": "This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.",
"managed_by": "[Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/)",
"update_frequency": "Update cadence may vary for each resource",
"tags": "microscopy,cell imaging,machine learning,model,czi,imaging,biodiversity,bioinformatics,biology,biomolecular modeling",
"license": "[BSD-3-Clause License](https://opensource.org/license/bsd-3-clause)",
"deprecated": false
},
{
"slug": "czi-imagining-mit",
"name": "CZ Grand Challenges - Imaging MIT Licensed data and models",
"description": "This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.",
"managed_by": "[Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/)",
"update_frequency": "Update cadence may vary for each resource",
"tags": "microscopy,cell imaging,machine learning,model,czi,imaging,biodiversity,bioinformatics,biology,biomolecular modeling",
"license": "[MIT License](https://opensource.org/license/mit)",
"deprecated": false
},
{
"slug": "czi-transcriptomics-mit",
"name": "CZ Grand Challenges - Transcriptomic MIT Licensed data and models",
"description": "This dataset contains a transcriptomics biological data and models. The models embed transcriptomic data and facilitate transcriptomic analysis. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.",
"managed_by": "[Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/)",
"update_frequency": "Update cadence may vary for each resource",
"tags": "transcriptomics,machine learning,model,czi,hdf5,biodiversity,biology,biomolecular modeling,cell biology,life sciences",
"license": "[MIT License](https://opensource.org/license/mit)",
"deprecated": false
},
{
"slug": "dandiarchive",
"name": "Distributed Archives for Neurophysiology Data Integration (DANDI)",
"description": "DANDI is a public archive of neurophysiology datasets, including raw and processed data,\nand associated software containers. Datasets are shared according to Creative Commons\nCC0 or CC-BY licenses. This US BRAIN Initiative supported archive provides a broad range\nof cellular neurophysiology data including intracellular and extracellular electrophysiology,\noptophysiology, calcium imaging, fiber photometry, behavioral time-series, and images from\nimmunostaining experiments, from over 20 species.\n\n",
"managed_by": "[DANDI Archive](https://about.dandiarchive.org/team)",
"update_frequency": "New datasets deposited every month",
"tags": "aws-pds,biology,calcium imaging,cell imaging,electrophysiology,hdf5,life sciences,neuroimaging,neurophysiology,neuroscience",
"license": "CC0, CC-BY",
"deprecated": false
},
{
"slug": "darpa-invisible-headlights",
"name": "DARPA Invisible Headlights Dataset",
"description": "\"The DARPA Invisible Headlights Dataset is a large-scale multi-sensor dataset annotated for autonomous, off-road navigation in challenging off-road environments. It features simultaneously collected off-road imagery from multispectral, hyperspectral, polarimetric, and broadband sensors spanning wave-lengths from the visible spectrum to long-wave infrared and provides aligned LIDAR data for ground-truth shape. Camera calibrations, LiDAR registrations, and traversability annotations for a subset o",
"managed_by": "[Kitware](http://www.kitware.com/)",
"update_frequency": "No updates anticipated at this time.\n",
"tags": "aws-pds,autonomous vehicles,broadband,computer vision,lidar,machine learning,segmentation,us",
"license": "This work is licensed under CC BY 4.0. This dataset was developed with funding from the Defense Advanced Research Projects Agency (DARPA). Distribution Statement A: Approved for public release. Distribution is unlimited. We hope this dataset can facilitate future research. If you find this data use",
"deprecated": false
},
{
"slug": "data-to-science",
"name": "Data to Science Catalog",
"description": "A user-generated geospatial data collection maintained by the Data to Science platform. Contributions vary by project, but typically include cloud-optimized datasets such as Cloud-Optimized GeoTIFFs (COGs) and Cloud-Optimized Point Clouds (COPCs), designed for efficient streaming, visualization, and analysis in modern geospatial applications.\n",
"managed_by": "Geospatial Data Science Lab at Purdue University",
"update_frequency": "None",
"tags": "aws-pds,aerial imagery,agriculture,cog,dsm,dtm,earth observation,geospatial,high-throughput imaging,image processing",
"license": "CC-BY-SA-4.0 (https://creativecommons.org/licenses/by-sa/4.0/)",
"deprecated": false
},
{
"slug": "dataforgood-fb-forests",
"name": "High Resolution Canopy Height Maps by WRI and Meta",
"description": "Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery. \n",
"managed_by": "[Meta](https://dataforgood.fb.com/)",
"update_frequency": "TBD",
"tags": "aws-pds,cog,earth observation,climate,land cover,agriculture,machine learning,aerial imagery,satellite imagery,image processing",
"license": "https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dataforgood-fb-forestsv2",
"name": "Version 2 High Resolution Canopy Height Maps by WRI and Meta",
"description": "Version 2 Global and regional Canopy Height Maps (CHMv2). Created using machine learning models on high-resolution worldwide Vantor satellite imagery. \n",
"managed_by": "[Meta](https://dataforgood.fb.com/)",
"update_frequency": "TBD",
"tags": "aws-pds,cog,earth observation,climate,land cover,agriculture,machine learning,aerial imagery,satellite imagery,image processing",
"license": "https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dataforgood-fb-hrsl",
"name": "High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta",
"description": "Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV\nand Cloud-optimized GeoTIFF files. This refines [CIESIN’s Gridded Population of the World](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4)\nusing machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census\ndata are allocated to blocks where imagery appears to contain buildings.\n",
"managed_by": "[Meta](https://dataforgood.fb.com/)",
"update_frequency": "Quarterly",
"tags": "population,demographics,machine learning,aerial imagery,satellite imagery,image processing,geospatial,disaster response,aws-pds",
"license": "https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "daylight-osm",
"name": "Daylight Map Distribution of OpenStreetMap",
"description": "Daylight is a complete distribution of global, open map data that’s freely available with support from community and professional mapmakers. Meta combines the work of global contributors to projects like OpenStreetMap with quality and consistency checks from Daylight mapping partners to create a free, stable, and easy-to-use street-scale global map.
\nThe Daylight Map Distribution contains a validated subset of the OpenStreetMap database. In addition to the standard OpenStreetMap PBF fo",
"managed_by": "[Meta](https://dataforgood.fb.com/)",
"update_frequency": "Quarterly",
"tags": "geospatial,osm,mapping,disaster response,aws-pds",
"license": "[Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/)\n",
"deprecated": false
},
{
"slug": "dc-lidar-2015",
"name": "District of Columbia - Classified Point Cloud LiDAR",
"description": "\"LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3.\nThis dataset, managed by the Office of the Chief Technology Officer (OCTO), through the\ndirection of the District of Columbia GIS program, contains tiled point cloud data for\nthe entire District along with associated metadata.\"\n",
"managed_by": "[Washington DC government](https://dc.gov/)",
"update_frequency": "The most recent data is from 2018 and 2015 data is available as well. A new data acquisition is planned for 2020.",
"tags": "aws-pds,geospatial,cities,us-dc,disaster response",
"license": "See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use)",
"deprecated": true
},
{
"slug": "dc-lidar",
"name": "District of Columbia - Classified Point Cloud LiDAR",
"description": "LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3.\nThis dataset, managed by the Office of the Chief Technology Officer (OCTO), through the\ndirection of the District of Columbia GIS program, contains tiled point cloud data for\nthe entire District along with associated metadata.\n",
"managed_by": "[Washington DC government](https://dc.gov/)",
"update_frequency": "The most recent data is from 2018 and 2015 data is available as well. A new data acquisition is planned for 2020.",
"tags": "aws-pds,geospatial,cities,us-dc,disaster response",
"license": "See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use)",
"deprecated": false
},
{
"slug": "dcc",
"name": "The MIT Supercloud Dataset",
"description": "Collection of parsed datacenter logs and time series data of hardware utilization from the MIT Supercloud system.",
"managed_by": "Siddharth Samsi",
"update_frequency": "Data will be updated annually",
"tags": "datacenter,HPC,cloud computing,workload analysis,energy,aws-pds",
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"deprecated": false
},
{
"slug": "deafrica-alos-jers",
"name": "Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1",
"description": "The ALOS/PALSAR annual mosaic is a global 25 m resolution dataset that combines data from many images captured by JAXA’s PALSAR and PALSAR-2 sensors on ALOS-1 and ALOS-2 satellites respectively. This product contains radar measurement in L-band and in HH and HV polarizations. It has a spatial resolution of 25 m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2020 (ALOS-2/PALSAR-2).\nThe JERS annual mosaic is generated from images acquired by the SAR sensor on the Japanese Ear",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "As available, generally annually.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,synthetic aperture radar,deafrica,stac",
"license": "Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html).\n",
"deprecated": false
},
{
"slug": "deafrica-chirps",
"name": "Digital Earth Africa CHIRPS Rainfall",
"description": "Digital Earth Africa (DE Africa) provides free and open access to a copy of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) monthly and daily products over Africa. The CHIRPS rainfall maps are produced and provided by the Climate Hazards Center in collaboration with the US Geological Survey, and use both rain gauge and satellite observations.\nThe CHIRPS-2.0 Africa Monthly dataset is regularly indexed to DE Africa from the CHIRPS monthly data. The CHIRPS-2.0 Africa Dai",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "Monthly.",
"tags": "aws-pds,agriculture,climate,earth observation,food security,geospatial,meteorological,satellite imagery,sustainability,deafrica",
"license": "To the extent possible under the law, Pete Peterson has waived all copyright and related or neighboring rights to CHIRPS. CHIRPS data is in the public domain as registered with Creative Commons.\n",
"deprecated": false
},
{
"slug": "deafrica-clgm-lwq",
"name": "Digital Earth Africa - Copernicus Global Land Service - Lake Water Quality",
"description": "The Copernicus Global Land Service – Lake Water Quality products offer a comprehensive, satellite-derived monitoring system for assessing key water quality indicators in major large lakes, typically those greater than 50 hectares. These datasets are generated using optical satellite sensors, primarily Sentinel-2 MSI and Sentinel-3 OLCI, with earlier archives derived from Envisat MERIS. Spanning multiple spatial resolutions (100 m and 300 m) and temporal scales (10-day composites), they support b",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New scene-level data is added regularly, as the Lake Water Quality (LWQ) datasets are updated every 10 days (dekadal composites), with near-real-time versions typically available within 3 to 4 days after satellite acquisition.",
"tags": "aws-pds,agriculture,disaster response,earth observation,geospatial,natural resource,satellite imagery,water,deafrica,stac",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-coastlines",
"name": "Digital Earth Africa Coastlines",
"description": "Africa's long and dynamic coastline is subject to a wide range of pressures, including extreme weather and climate, sea level rise and human development. Understanding how the coastline responds to these pressures is crucial to managing this region, from social, environmental and economic perspectives.\nThe Digital Earth Africa Coastlines (provisional) is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline from 2000 to the present.\n",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "To be defined.",
"tags": "aws-pds,climate,coastal,earth observation,geospatial,satellite imagery,sustainability,deafrica",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-crop-extent",
"name": "Digital Earth Africa Cropland Extent Map (2019)",
"description": "Digital Earth Africa's cropland extent map (2019) shows the estimated location of croplands in Africa for the period January to December 2019. Cropland is defined as: \"a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvest-able at least once within the 12 months after the sowing/planting date.\" This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. \n",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "To be defined.",
"tags": "aws-pds,agriculture,earth observation,food security,geospatial,satellite imagery,sustainability,deafrica,stac,cog",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-fractional-cover",
"name": "Digital Earth Africa Fractional Cover",
"description": "Fractional cover (FC) describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The product is derived from Landsat satellite data, using an algorithm developed by the [Joint Remote Sensing Research Program](https://www.jrsrp.org.au/). \nDigital Earth Africa's FC service has two com",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New scene-level data is added as new Landsat data is available. New summaries are available soon after data is available for a year.",
"tags": "aws-pds,agriculture,disaster response,earth observation,geospatial,natural resource,satellite imagery,sustainability,deafrica,stac",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-geomad",
"name": "Digital Earth Africa GeoMAD",
"description": "GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes.\nThe geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual ",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods are available.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,deafrica,stac,cog",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-landsat",
"name": "Digital Earth Africa Landsat Collection 2 Level 2",
"description": "Digital Earth Africa (DE Africa) provides free and open access to a copy of Landsat Collection 2 Level-2 products over Africa. These products are produced and provided by the United States Geological Survey (USGS).\nThe Landsat series of Earth Observation satellites, jointly led by USGS and NASA, have been continuously acquiring images of the Earth’s land surface since 1972. DE Africa provides data from Landsat 5, 7 and 8 satellites, including historical observations dating back to late 1980s and",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New Landsat data are added regularly, usually within a few hours of them being available in the usgs-landsat bucket.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,deafrica,stac,cog",
"license": "There are no restrictions on Landsat data downloaded from the USGS; it can be used or \nredistributed as desired. USGS request that you include a [statement of the data source](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con) \n",
"deprecated": false
},
{
"slug": "deafrica-mangrove",
"name": "Digital Earth Africa Global Mangrove Watch",
"description": "The Global Mangrove Watch (GMW) dataset is a result of the collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). The primary objective of producing this dataset is to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss ",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "To be defined.",
"tags": "aws-pds,natural resource,earth observation,coastal,geospatial,satellite imagery,sustainability,deafrica,stac,cog",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-ndvi_anomaly",
"name": "Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly",
"description": "Digital Earth Africa’s Monthly NDVI Anomaly service provides estimate of vegetation condition, for each caldendar month, against the long-term baseline condition measured for the month from 1984 to 2020 in the NDVI Climatology.\n\n A standardised anomaly is calculated by subtracting the long-term mean from an observation of interest and then dividing the result by the long-term standard deviation. Positive NDVI anomaly values indicate vegetation is greener than average conditions, and are usually",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "From September 2022, the Monthly NDVI Anomaly is generated as a low latency product, i.e. anomaly for a month is generated on the 5th day of the following month. This ensures data is available shortly after the end of a month and all Landat 9 and Sentinel-2 observations are included. Not all landsat 8 observations for the month will be used, because the Landsat 8 Surface Refelectance product from USGS has a latency of over 2 weeks ([see Landsat Collection 2 Generation Timeline (https://www.usgs.gov/media/images/landsat-collection-2-generation-timeline)).",
"tags": "aws-pds,agriculture,disaster response,earth observation,geospatial,natural resource,satellite imagery,deafrica,stac,cog",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-ndvi_climatology_ls",
"name": "Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology",
"description": "Digital Earth Africa’s NDVI climatology product represents the long-term average baseline condition of vegetation for every Landsat pixel over the African continent. Both mean and standard deviation NDVI climatologies are available for each calender month.\n\nSome key features of the product are:\n\n - NDVI climatologies were developed using harmonized Landsat 5,7,and 8 satellite imagery.\n - Mean and standard deviation NDVI climatologies are produced for each calender month, using a temporal basel",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "N/A.",
"tags": "aws-pds,agriculture,disaster response,earth observation,geospatial,natural resource,agriculture,satellite imagery,deafrica,stac",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-sentinel-1-mosaic",
"name": "Sentinel-1 Monthly Mosaic",
"description": "Synthetic Aperture Radar (SAR) sensor have the advantage of operating at wavelengths not impeded by cloud cover and can acquire data over a site during the day or night. The Sentinel-1 mission, part of the Copernicus joint initiative by the European Commission (EC) and the European Space Agency (ESA), provides reliable and repeated wide-area monitoring using its SAR instrument.Sentinel-1 Monthly Mosaics are analysis-ready product of individual Sentinel-1 acquisitions. Sentinel-1 monthly mosaics ",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "N/A.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,deafrica,stac,cog",
"license": "Access to S1 Monthly Mosaic data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "deafrica-sentinel-1",
"name": "Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected",
"description": "DE Africa’s Sentinel-1 backscatter product is developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specifications.\nThe Sentinel-1 mission, composed of a constellation of two C-band Synthetic Aperture Radar (SAR) satellites, are operated by European Space Agency (ESA) as part of the Copernicus Programme. The mission currently collects data every 12 days over Africa at a spatial resolution of approximately 20 m.\nRadar backscatter measures the amount of microwave radiation ",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New Sentinel-1 data are added regularly.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,deafrica,stac,cog",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "deafrica-sentinel-2-c1",
"name": "Digital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1",
"description": "The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product.\nTogether, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days.\nSentinel-",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New Sentinel-2 scenes are added regularly, usually within few hours after they are available on Copernicus OpenHub.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,deafrica,stac,cog",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "deafrica-sentinel-2",
"name": "Digital Earth Africa Sentinel-2 Level-2A",
"description": "The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product.\nTogether, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days.\nSentinel-",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New Sentinel-2 scenes are added regularly, usually within few hours after they are available on Copernicus OpenHub.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,deafrica,stac,cog",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "deafrica-waterbodies",
"name": "DE Africa Waterbodies Monitoring Service",
"description": "The Digital Earth Africa continental Waterbodies Monitoring Service identifies more than 700,000 water bodies from over three decades of satellite observations. This service maps persistent and seasonal water bodies and the change in their water surface area over time. Mapped water bodies may include, but are not limited to, lakes, ponds, man-made reservoirs, wetlands, and segments of some river systems.On a local, regional, and continental scale, this service helps improve our understanding of ",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "Single historical extent derived from the full temporal range.",
"tags": "aws-pds,agriculture,disaster response,earth observation,geospatial,natural resource,satellite imagery,water,deafrica,stac",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deafrica-wofs",
"name": "Digital Earth Africa Water Observations from Space",
"description": "Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite.\nThey are generated using the WOfS classification algorithm on Landsat satellite",
"managed_by": "[Digital Earth Africa](https://www.digitalearthafrica.org/)",
"update_frequency": "New scene-level data is added as new Landsat data is available. New summaries are available soon after data is available for a year.",
"tags": "aws-pds,agriculture,disaster response,earth observation,geospatial,natural resource,satellite imagery,water,deafrica,stac",
"license": "DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/.\n",
"deprecated": false
},
{
"slug": "deepdrug-dpeb",
"name": "DeepDrug Protein Embeddings Bank (DPEB)",
"description": "DPEB is a multimodal database of human protein embeddings integrating four biologically complementary representations—AlphaFold2, BioEmbeddings, ESM-2, and ProtVec—designed for enhanced protein-protein interaction prediction and functional classification.",
"managed_by": "Louisiana State University",
"update_frequency": "Initial release; maintained for at least 2 years with updates planned based on new embedding models and protein coverage.",
"tags": "bioinformatics,protein,structural biology,machine learning,life sciences,aws-pds",
"license": "MIT",
"deprecated": false
},
{
"slug": "dendritic-consortium",
"name": "Dendritic Consortium Multimodal Dataset",
"description": "The Dendritic Consortium provides a multimodal dataset integrating calcium and voltage imaging, electrophysiology, electron microscopy, proteomics, and computational models of Baz1a pyramidal neurons in the mouse primary visual cortex (V1).",
"managed_by": "Dendritic Consortium",
"update_frequency": "Continuously updated as new experimental and computational data are generated.",
"tags": "brain images,brain models,electrophysiology,electron microscopy,imaging,life sciences,Mus musculus,neuroscience,neurobiology,neuroimaging",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "dep-coastlines",
"name": "Pacific Coastlines Change",
"description": "Pacific Coastlines beta version product includes coastline change detection\nsince the year 2000 for Pacific Island Country and Territories (PICTs).\nThis product will provide ongoing monitoring of coastline change detection.\nThis provides insights into processes including erosion (where landmass\narea decreases) and accretion or deposition (where landmass area increases).\n",
"managed_by": "[Pacific Community (SPC)](https://www.spc.int/)",
"update_frequency": "Annually",
"tags": "earth observation,environmental,coastal,geoscience,geospatial",
"license": "Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dep-ls-geomads",
"name": "Landsat Geometric Median and Absolute Deviations (GeoMAD) over the Pacific.",
"description": "The GeoMAD is derived from Landsat surface reflectance data.\nThe data are masked for cloud, shadows and other image artefacts\nusing the associated pixel quality product to help provide as\nclear a set of observations as possible from which to calculate the medians.\n",
"managed_by": "[Pacific Community (SPC)](https://www.spc.int/)",
"update_frequency": "Annually",
"tags": "earth observation,geoscience,geospatial",
"license": "Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dep-mangroves",
"name": "Digital Earth Pacific Mangroves Extent and Density",
"description": "Pacific Mangroves beta version product is an extension of\nthe Global Mangrove Watch (GMV v3, 2020). which shows the\nextent of mangrove ecosystems across Pacific Island Countries\nand Territories (PICTs). The changes in mangroves extent was\nfurther classified into three categories of closed (high-density),\nopen (lower density) and non-mangrove. This was used as the\nbaseline training layer where mangrove categories between\n2016 and 2022 were analysed.\n",
"managed_by": "[Pacific Community (SPC)](https://www.spc.int/)",
"update_frequency": "Annually",
"tags": "earth observation,environmental,climate,geoscience,geospatial",
"license": "Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dep-s1-annual-mosaics",
"name": "Sentinel-1 Mean and Median Annual Mosaic",
"description": "Sentinel-1 carries a Synthetic Aperture RADAR (SAR) that\noperates on the C-band. This platform offers SAR data\nday and night and in all-weather conditions.\n",
"managed_by": "[Pacific Community (SPC)](https://www.spc.int/)",
"update_frequency": "Annually",
"tags": "earth observation,environmental,climate,geoscience,geospatial",
"license": "Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dep-s2-geomads",
"name": "Sentinel-2 Geometric Median and Absolute Deviations (GeoMAD) over the Pacific",
"description": "The Geometric Median and Absolute Deviations (GeoMAD) product is\na cloud-free annual mosaic that uses a more robust method of\ndetermining the median observation than a simple median. Along\nwith the median observation, the GeoMAD produces three measures\nof variance, or absolute deviations, which helps to understand\nhow the data over the time period changes. For example, some areas,\nsuch as desert, will change very little. Whereas crop land will change\nmore. All ofthese values are useful in unders",
"managed_by": "[Pacific Community (SPC)](https://www.spc.int/)",
"update_frequency": "Annually",
"tags": "earth observation,geoscience,geospatial",
"license": "Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "dep-wofs",
"name": "Digital Earth Pacific Water Observatins from Space (WOfS)",
"description": "Water Observations from Space (WOfS) beta version product for Water\nObservations from Space (WOfS) is an annual summary of the temporal\nand spatial extent of surface water over landscapes. In essence, this\nhighlights where water is usually or where it is rarely. The results\nare visualised to compare points in time spanning over a year, a season\nor multiple years. The dataset extends back historically to 2013.\n",
"managed_by": "[Pacific Community (SPC)](https://www.spc.int/)",
"update_frequency": "Annually",
"tags": "earth observation,environmental,water,geoscience,geospatial",
"license": "Digital Earth Pacific Data is available under the Creative Commons\nAttribution 4.0 International license https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "depmap-omics-ccle",
"name": "The Cancer Dependency Map (DepMap) Cancer Cell Line Encyclopedia (CCLE) Dataset",
"description": "This dataset consists of whole genome sequencing (WGS), whole exome sequencing (WES), and RNA sequencing files generated from ~1000 cancer cell lines described in Ghandi et al., 2019.",
"managed_by": "[Broad Institute](https://www.broadinstitute.org/)",
"update_frequency": "occasionally (as additional sequencings are generated for publicly-releasible CCLE models)",
"tags": "aws-pds,bam,biology,bioinformatics,cancer,genetic,genomic,Homo sapiens,life sciences,short read sequencing",
"license": "By downloading this data you agree to our [Terms and Conditions](https://depmap.org/portal/terms/)",
"deprecated": false
},
{
"slug": "deutsche-boerse-pds",
"name": "Deutsche Börse Public Dataset",
"description": "The Deutsche Börse Public Data Set consists of trade data aggregated to one minute intervals from the Eurex and Xetra trading systems. It provides the initial price, lowest price, highest price, final price and volume for every minute of the trading day, and for every tradeable security. If you need higher resolution data, including untraded price movements, please refer to our historical market data product [here](http://www.eurexchange.com/exchange-en/market-data/historical-data). Also, be s",
"managed_by": "Not currently managed",
"update_frequency": "The data is updated every minute during trading hours.",
"tags": "aws-pds,market data,financial markets,trading",
"license": "Non-commercial (NC) - licensees may copy, distribute, display, and perform the work and make derivative works and remixes based on it only for non-commercial purposes.",
"deprecated": true
},
{
"slug": "dharani-brain-dataset",
"name": "DHARANI Developing Human-Brain Atlas",
"description": "We introduce DHARANI, the first online platform with three-dimensional (3D) histological reconstructions of the developing human brain from 14 to 24 gestational weeks (GW) across the five fetal brains. DHARANI features 5132 Nissl, hematoxylin and eosin stained, 20 µm coronal and sagittal sections, postmortem MRI, and a neuroanatomical atlas with 466 annotated sections covering ∼500 brain structures. It is accessible online at https://brainportal.humanbrain.in/publicview/index.html. The 3D recons",
"managed_by": "[Sudha Gopalakrishnan Brain Centre, IIT Madras](http://iitm.humanbrain.in/)",
"update_frequency": "Never",
"tags": "life sciences,brain images,microscopy,neurobiology,segmentation,computer vision",
"license": "CC-by-4.0",
"deprecated": false
},
{
"slug": "dialoglue",
"name": "DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue",
"description": "This bucket contains the checkpoints used to reproduce the baseline results reported in the DialoGLUE benchmark hosted\non EvalAI (https://evalai.cloudcv.org/web/challenges/challenge-page/708/overview). The associated scripts for using the checkpoints are located here:\nhttps://github.com/alexa/dialoglue. The associated paper describing the benchmark and checkpoints is here: https://arxiv.org/abs/2009.13570.\nThe provided checkpoints include the CONVBERT model, a BERT-esque model trained on a large",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,machine learning,conversation data",
"license": "[CDLA-Sharing](https://cdla.io/sharing-1-0/)",
"deprecated": false
},
{
"slug": "dig-open-analysis-data",
"name": "Knowledge Portal Network Bottom-line Genetic Associations",
"description": "At the Knowledge Portal Network, we aggregate and analyze genetic association results for a wide range of diseases and traits. For any given disease, a large number of individual genetic association datasets may have been generated. To make these results more interpretable, we meta-analyze all datasets for each phenotype, using a method that we term \"bottom-line integrative analysis\". Here we provide the bottom-line summary statistic files for public download.",
"managed_by": "Jason Flannick lab, Broad Institute of MIT and Harvard",
"update_frequency": "Three times per year",
"tags": "genome wide association study,life sciences,genetic",
"license": "https://a2f.hugeamp.org/policies.html",
"deprecated": false
},
{
"slug": "digital-globe-open-data",
"name": "Maxar Open Data Program",
"description": "Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. Also includes crowdsourced damage assessments for major, sudden onset disasters.\n",
"managed_by": "[Maxar](https://www.maxar.com/)",
"update_frequency": "New data is released in response to activations.",
"tags": "earth observation,disaster response,geospatial,satellite imagery,cog",
"license": "Creative Commons Attribution Non Commercial 4.0",
"deprecated": true
},
{
"slug": "digitalcorpora",
"name": "DigitalCorpora",
"description": "Disk images, memory dumps, network packet captures, and files for use in digital forensics research and education. All of this information is accessible through the digitalcorpora.org website, and made available at s3://digitalcorpora/. Some of these datasets implement scenarios that were performed by students, faculty, and others acting *in persona*. As such, the information is synthetic and may be used without prior authorization or IRB approval. Details of these datasets can be found at http:",
"managed_by": "[Simson Garfinkel](https://simson.net/)",
"update_frequency": "Quarterly",
"tags": "aws-pds,computer security,cyber security,CSI,information retrieval,computer forensics,digital forensics,machine learning,machine translation,image processing",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "dmi-danra-05",
"name": "Danish Meteorological Institute (DMI) Reanalysis dataset v0.5",
"description": "DANRA is a high-resolution meteorological reanalysis dataset for Denmark and Northwestern Europe covering the period September 1990 to December 2023",
"managed_by": "[Danish Meteorological Institute](https://www.dmi.dk/)",
"update_frequency": "Not updated",
"tags": "aws-pds,air temperature,atmosphere,geospatial,global,land,meteorological,near-surface air temperature,near-surface relative humidity,near-surface specific humidity",
"license": "DMI Reanalysis dataset v0.5 is distributed under the [Creative Commons License CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en)",
"deprecated": false
},
{
"slug": "dmi-opendata",
"name": "Danish Meteorological Institute (DMI) Open Data Forecasts",
"description": "DMI forecast data consist of various models where each model contains different set of parameters relating to a specific domain like ocean (WAM), storm flooding (DKSS) or weather (HARMONIE)",
"managed_by": "[Danish Meteorological Institute](https://www.dmi.dk/)",
"update_frequency": "Every hour, 3 hours or 6 hours depending on model",
"tags": "aws-pds,air temperature,atmosphere,forecast,meteorological,near-surface air temperature,near-surface relative humidity,near-surface specific humidity,model,ocean circulation",
"license": "DMI's Open Data are distributed under the [Creative Commons License CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en)",
"deprecated": false
},
{
"slug": "dmspssj",
"name": "Defense Meteorology Satellite Program (DMSP) Auroral Particle Flux",
"description": "The United States Air Force (USAF) Defense Meteorological Satellite Program (DMSP) SSJ precipitating particle instrument measures in-situ total flux and energy distribution of electrons and ions at low earth orbit. These precipitating particles are of interest for space weather operations and research, in part because they produce aurora during normal and very strong geomagnetic storms. This dataset contains both sensor-level raw data (as detailed in Redmon et al. 2017) and a high-level machine-",
"managed_by": "Space Weather Technology, Research and Education Center (TREC) at University of Colorado, Boulder",
"update_frequency": "Infrequent",
"tags": "aws-pds,solar,space weather,geospatial,earth observation",
"license": "This data is in the '[public domain](https://creativecommons.org/publicdomain/zero/1.0/)'",
"deprecated": false
},
{
"slug": "dnastack-covid-19-sra-data",
"name": "DNAStack COVID19 SRA Data",
"description": "The [Sequence Read Archive (SRA)](https://www.ncbi.nlm.nih.gov/sra/) is the primary archive of high-throughput sequencing data, hosted by the National Institutes of Health (NIH). The SRA represents the largest publicly available repository of SARS-CoV-2 sequencing data. This dataset was created by DNAstack using SARS-CoV-2 sequencing data sourced from the SRA. Where possible, raw sequence data were processed by DNAstack through a unified bioinformatics pipeline to produce genome assemblies and v",
"managed_by": "[DNAstack](https://dnastack.com/)",
"update_frequency": "Rolling",
"tags": "aws-pds,bam,bioinformatics,coronavirus,COVID-19,fasta,fastq,global,genetic,genomic",
"license": "[DNAstack terms of use](https://dnastack.com/terms-of-use/)",
"deprecated": false
},
{
"slug": "dynamical-ecmwf-aifs-single",
"name": "ECMWF AIFS Single - dynamical.org Icechunk Zarr",
"description": "\n The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast\n model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).\n This is the non-ensemble configuration of AIFS that produces a single forecast trace.\n AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical\n weather prediction (NWP) analyses.\n
\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by dyn",
"managed_by": "[dynamical.org](https://dynamical.org)",
"update_frequency": "ECMWF AIFS Single forecast: Forecasts initialized every 6 hours",
"tags": "aws-pds,weather,atmosphere,meteorological,climate,forecast,zarr",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "dynamical-ecmwf-ifs-ens",
"name": "ECMWF IFS ENS - dynamical.org Icechunk Zarr",
"description": "\n The Integrated Forecasting System (IFS) is a global forecast model developed \n by ECMWF. ENS is an ensemble configuration of IFS, containing 51 ensemble members.\n IFS consists of a numerical model of the Earth system, which includes\n an atmospheric model at its heart, coupled with models of other Earth system \n components such as the ocean. The data assimilation system combines \n the latest weather observations with a recent forecast to obtain the best \n possible estimate of t",
"managed_by": "[dynamical.org](https://dynamical.org)",
"update_frequency": "ECMWF IFS ENS Forecast, 15 day, 0.25 degree: Forecasts initialized every 24 hours",
"tags": "aws-pds,weather,atmosphere,meteorological,climate,forecast,zarr",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "dynamical-noaa-gefs",
"name": "NOAA GEFS - dynamical.org Icechunk Zarr",
"description": "
\n The Global Ensemble Forecast System (GEFS) is a National Oceanic and\n Atmospheric Administration (NOAA) National Centers for Environmental\n Prediction (NCEP) weather forecast model. GEFS creates 31 separate\n forecasts (ensemble members) to describe the range of forecast uncertainty.\n
\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.
\nWhen Icechunk 2.0 is released, these datasets will be update",
"managed_by": "[dynamical.org](https://dynamical.org)",
"update_frequency": "NOAA GEFS forecast, 35 day: Forecasts initialized every 24 hours; NOAA GEFS analysis: 3.0 hours",
"tags": "aws-pds,weather,atmosphere,meteorological,climate,forecast,zarr",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "dynamical-noaa-gfs",
"name": "NOAA GFS - dynamical.org Icechunk Zarr",
"description": "
\n The Global Forecast System (GFS) is a National Oceanic and Atmospheric\n Administration (NOAA) National Centers for Environmental Prediction\n (NCEP) weather forecast model that generates data for dozens of\n atmospheric and land-soil variables, including temperatures, winds,\n precipitation, soil moisture, and atmospheric ozone concentration. The\n system couples four separate models (atmosphere, ocean model, land/soil\n model, and sea ice) that work together to depict weather conditions.",
"managed_by": "[dynamical.org](https://dynamical.org)",
"update_frequency": "NOAA GFS analysis: 1 hour; NOAA GFS forecast: Forecasts initialized every 6 hours",
"tags": "aws-pds,weather,atmosphere,meteorological,climate,forecast,zarr",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "dynamical-noaa-hrrr",
"name": "NOAA HRRR - dynamical.org Icechunk Zarr",
"description": "
\n The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution,\n hourly updated, cloud-resolving, convection-allowing atmospheric model,\n initialized by 3km grids with 3km radar assimilation. Radar data is\n assimilated in the HRRR every 15 min over a 1-h period adding further\n detail to that provided by the hourly data assimilation from the 13km\n radar-enhanced Rapid Refresh.\n
\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by \n The NOAA Multi-Radar/Multi-Sensor System (MRMS) integrates data from\n multiple radars and radar networks, surface observations, numerical\n weather prediction (NWP) models, and climatology to generate seamless,\n high spatio-temporal resolution mosaics at low latency focused on hail,\n wind, tornado, quantitative precipitation estimations, convection, icing,\n and turbulence.\n
\nThese datasets have been translated to cloud-optimized Icechunk Zarr format by
[Creative Commons Zero](https://creativecommons.org/publicdomain/z",
"deprecated": false
},
{
"slug": "epa-2022-modeling-platform",
"name": "OSAP 2022 Modeling Platform ",
"description": "The data are part of the 2022 Modeling Platform used to support regulatory actions and technical analyses conducted by the EPA's Office of State Air Partnerships (OSAP). Specifically, this data includes Weather Research and Forecasting Model (v4.4.2) conducted at a 12-km resolution over the Continental United States (12US). MCIP-processed files and wrfcamx-processed (12US1 domain) are also available as part of this dataset to assist in the use of emissions processing and photochemical modeling",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "As needed",
"tags": "aws-pds,air quality,regulatory,weather,meteorological,environmental",
"license": "These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public",
"deprecated": false
},
{
"slug": "epa-ccte-httr",
"name": "U.S. Environmental Protection Agency (EPA) Center for Computational Toxicology and Exposure High Throughput Transcriptomics Data",
"description": "High-throughput transcriptomics (HTTr) data generated by US EPA Office of Research and Development, Center for Computational Toxicology and Exposure (CCTE), Biomolecular and Computational Toxicology Division. All data is generated using TempO-Seq targeted RNA-seq technology from in vitro cell culture systems.\n",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "Quarterly",
"tags": "aws-pds,transcriptomics,gene expression,fastq,bioinformatics",
"license": "These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. 105. More details on the U.S. Public",
"deprecated": false
},
{
"slug": "epa-edde-v1",
"name": "EPA Dynamically Downscaled Ensemble (EDDE) Version 1",
"description": "The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 1. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 1 datasets cover the contiguous United States at a horizontal grid spacing of 36 kilometers at hourly increments. EDDE Version 1 includes simulations that have been dynamically downscaled from multiple global climate models (GCMs) under both mid",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "Quarterly",
"tags": "aws-pds,weather,climate,climate model,climate projections,CMIP5,CMIP6,us,atmosphere,environmental",
"license": "These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public",
"deprecated": false
},
{
"slug": "epa-edde-v2",
"name": "EPA Dynamically Downscaled Ensemble (EDDE) Version 2",
"description": "The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 2. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 2 datasets cover the contiguous United States at a horizontal grid spacing of 12 kilometers at hourly increments. EDDE Version 2 will include simulations that have been dynamically downscaled from multiple global climate models (GCMs) under mult",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "Quarterly",
"tags": "aws-pds,weather,climate,climate model,climate projections,CMIP5,CMIP6,us,atmosphere,environmental",
"license": "These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public",
"deprecated": false
},
{
"slug": "epa-equates-v1",
"name": "Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from the EPA's Air Quality Time Series (EQUATES) Project",
"description": "The data are part of EPA’s Air Quality Time Series (EQUATES) Project. The data consist of hourly gridded pollutant concentrations estimates by the Community Multiscale Air Quality (CMAQ) model version 5.3.2 (https://doi.org/10.15139/S3/F2KJSK) for January 1 – December 31, 2019. Model data is provided for two spatial domains : the Northern Hemisphere (108 km x 108km horizontal grid spacing) and the Contiguous United States including parts of Canada and Mexico (12km x 12km horizontal grid spaci",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "Annual",
"tags": "aws-pds,air quality,atmosphere,model",
"license": "These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public",
"deprecated": false
},
{
"slug": "epa-hourly-prognostic-meteorology",
"name": "EPA Hourly Prognostic Meteorological Data ",
"description": "The data are hourly outputs from the Weather Research and Forecasting (WRF) model generated by the EPA's Office of State Air Partnerships (OSAP), Air Quality Assessment Division, Air Quality Modeling Branch. These data were generated at a 12-km resolution over the Continental United States (12US), beginning for the year 2021 and continuing annually through 2023. These files are intended for use in a broad range of air quality applications, but specifically may be used in dispersion modeling app",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "Annually",
"tags": "aws-pds,air quality,regulatory,weather,meteorological,environmental",
"license": "These datasets are products of the U.S. Government and are intended for public access and use. Unless otherwise specified, all data produced by the U.S EPA is, by default, in the public domain and are not subject to domestic copyright protection under 17 U.S.C. § 105. More details on the U.S. Public",
"deprecated": false
},
{
"slug": "epa-rsei-pds",
"name": "EPA Risk-Screening Environmental Indicators",
"description": "Detailed air model results from EPA’s Risk-Screening Environmental Indicators (RSEI) model.",
"managed_by": "U.S. Environmental Protection Agency (https://www.epa.gov)",
"update_frequency": "Updated infrequently",
"tags": "aws-pds,environmental",
"license": "US Government work",
"deprecated": false
},
{
"slug": "epilepsy-science",
"name": "Epilepsy.Science",
"description": "Epilepsy.Science comprise a set of datasets focused on Epilepsy Research that\nspan both Clinical Data and Pre-clinical data. Datasets are contributed by the \nEpilepsy Research community and published using a standardized structure and \nmetadata. Clinical datasets include de-identified subject information, EEG, and\nclinical imaging.\n",
"managed_by": "[Pennsieve Labs](https://discover.pennsieve.io/)",
"update_frequency": "Continuously as new data is published",
"tags": "aws-pds,life sciences,bioinformatics,neuroscience,medicine,electrophysiology",
"license": "Creative Commons BY 4.0",
"deprecated": false
},
{
"slug": "epoch-of-reionization",
"name": "Epoch of Reionization Dataset",
"description": "The data are from observations with the Murchison Widefield Array (MWA) which is a\nSquare Kilometer Array (SKA) precursor in Western Australia. This particular\ndataset is from the Epoch of Reionization project which is a key science driver\nof the SKA. Nearly 2PB of such observations have been recorded to date, this is\na small subset of that which has been exported from the MWA data archive in\nPerth and made available to the public on AWS. The data were taken to detect\nsignatures of the first s",
"managed_by": "University of washington Radio Cosmology Group",
"update_frequency": "Irregularly",
"tags": "astronomy,aws-pds",
"license": "BSD 2-Clause",
"deprecated": false
},
{
"slug": "era5-for-wrf",
"name": "ERA5-for-WRF Open Data on AWS",
"description": "ERA5 reanalysis data on AWS, preprocessed for use with the Weather Research and Forecasting (WRF) model.",
"managed_by": "[Veer Renewables](http://www.veer.eco/)",
"update_frequency": "Monthly.",
"tags": "aws-pds,weather,sustainability,atmosphere,electricity,meteorological,model",
"license": "CC BY-SA 4.0",
"deprecated": false
},
{
"slug": "esa-worldcover-vito-composites",
"name": "ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites",
"description": "The WorldCover 10m Annual Composites were produced, as part of the European Space Agency (ESA) WorldCover project, from the yearly Copernicus Sentinel-1 and Sentinel-2 archives for both years 2020 and 2021. These global mosaics consists of four products composites. A Sentinel-2 RGBNIR yearly median composite for bands B02, B03, B04, B08. A Sentinel-2 SWIR yearly median composite for bands B11 and B12. A Sentinel-2 NDVI yearly percentiles composite (NDVI 90th, NDVI 50th NDVI 10th percentiles). A ",
"managed_by": "[VITO](https://vito.be)",
"update_frequency": "Not updated.",
"tags": "aws-pds,earth observation,agriculture,satellite imagery,geospatial,natural resource,sustainability,cog,disaster response,mapping",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "esa-worldcover-vito",
"name": "ESA WorldCover",
"description": "The European Space Agency (ESA) WorldCover product provides global land cover maps for 2020 & 2021 at 10 m resolution based on Copernicus Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency. A first version of the product (v100), containing the 2020 map was released in October 2021. The 2021 map was r",
"managed_by": "[VITO](https://vito.be)",
"update_frequency": "Yearly.",
"tags": "aws-pds,earth observation,agriculture,satellite imagery,geospatial,natural resource,sustainability,cog,disaster response,mapping",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "esa-worldcover",
"name": "ESA WorldCover",
"description": "The European Space Agency (ESA) WorldCover is a global land cover map with 11 different land cover classes produced at 10m resolution based on combination of both Sentinel-1 and Sentinel-2 data. In areas where Sentinel-2 images are covered by clouds for an extended period of time, Sentinel-1 data then provides complimentary information on the structural characteristics of the observed land cover. Therefore, the combination of Sentinel-1 and Sentinel-2 data makes it possible to update the land co",
"managed_by": "[Sinergise on behalf of VITO](https://vito.be)",
"update_frequency": "Not yet defined.",
"tags": "aws-pds,earth observation,agriculture,satellite imagery,geospatial,natural resource,cog,disaster response,mapping,synthetic aperture radar",
"license": "CC-BY 4.0, for Citation see [Citation section](https://esa-worldcover.org/en/data-access) / [DOI](https://doi.org/10.5281/zenodo.5571936)\n",
"deprecated": true
},
{
"slug": "euclid-q1",
"name": "Euclid Quick Release 1 (Q1)",
"description": "Euclid launched in July 2023 as a European Space Agency (ESA) mission with involvement by NASA. The primary science goals of Euclid are to better understand the composition and evolution of the dark Universe. The Euclid mission will provide space-based imaging and spectroscopy as well as supporting ground-based imaging to achieve these primary goals. These data will be archived by multiple global repositories, including IRSA, where they will support transformational work in many areas of astroph",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "Euclid Q1 has been finalized and will not be updated. The data may be presented in new ways as the products become available.",
"tags": "astronomy,imaging,object detection,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "euro-cordex",
"name": "EURO-CORDEX - European component of the Coordinated Regional Downscaling Experiment",
"description": "The EURO-CORDEX dataset contains regional climate model data for Europe, for use in impacts, decision-making, and climate science. \nCurrently, the bucket contains monthly datasets of 2m air temperature downscaled from CMIP5 global model datasets using different\nregional climate models.\n",
"managed_by": "[Helmholtz Centre Hereon / GERICS](https://www.climate-service-center.de)",
"update_frequency": "We will add more datasets on demand.",
"tags": "aws-pds,climate,model,climate model,atmosphere,geospatial,zarr",
"license": "https://is-enes-data.github.io/cordex_terms_of_use.pdf",
"deprecated": false
},
{
"slug": "exceptional-responders",
"name": "Exceptional Responders Initiative",
"description": "The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional respo",
"managed_by": "“[Center for Translational Data Science at The University of Chicago] (https://ctds.uchicago.edu/)”",
"update_frequency": "Genomic Data Commons is the source of truth for this dataset and it offers monthly releases, although this particular dataset may not be updated at every release",
"tags": "aws-pds,STRIDES,cancer,life sciences,genomic,whole genome sequencing,whole exome sequencing,transcriptomics,epigenomics",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "fashionlocaltriplets",
"name": "FashionLocalTriplets",
"description": "Fine-grained localized visual similarity and search for fashion.",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not updated",
"tags": "amazon.science,computer vision,machine learning",
"license": "Creative Commons Non-Commercial 4.0",
"deprecated": false
},
{
"slug": "fast-ai-coco",
"name": "COCO - Common Objects in Context - fast.ai datasets",
"description": "COCO is a large-scale object detection, segmentation, and captioning dataset.\nThis is part of the fast.ai datasets collection hosted by AWS for convenience\nof fast.ai students. If you use this dataset in your research please cite\narXiv:1405.0312 [cs.CV].\n",
"managed_by": "[fast.ai](http://www.fast.ai/)",
"update_frequency": "As required",
"tags": "aws-pds,deep learning,computer vision,machine learning",
"license": "Creative Commons http://cocodataset.org/#termsofuse",
"deprecated": false
},
{
"slug": "fast-ai-imageclas",
"name": "Image classification - fast.ai datasets",
"description": "Some of the most important datasets for image classification research, including\nCIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets,\nand Stanford-Cars. This is part of the fast.ai datasets collection hosted by\nAWS for convenience of fast.ai students. See documentation link for citation and\nlicense details for each dataset.\n",
"managed_by": "[fast.ai](http://www.fast.ai/)",
"update_frequency": "As required",
"tags": "aws-pds,deep learning,computer vision,machine learning",
"license": "Varies by dataset - see documentation link",
"deprecated": false
},
{
"slug": "fast-ai-imagelocal",
"name": "Image localization - fast.ai datasets",
"description": "Some of the most important datasets for image localization research, including\nCamvid and PASCAL VOC (2007 and 2012). This is part of the fast.ai datasets\ncollection hosted by AWS for convenience of fast.ai students. See\ndocumentation link for citation and license details for each dataset.\n",
"managed_by": "[fast.ai](http://www.fast.ai/)",
"update_frequency": "As required",
"tags": "aws-pds,deep learning,computer vision,machine learning",
"license": "Varies by dataset - see documentation link",
"deprecated": false
},
{
"slug": "fast-ai-nlp",
"name": "NLP - fast.ai datasets",
"description": "Some of the most important datasets for NLP, with a focus on classification, including\nIMDb, AG-News, Amazon Reviews (polarity and full), Yelp Reviews (polarity and\nfull), Dbpedia, Sogou News (Pinyin), Yahoo Answers, Wikitext 2 and Wikitext\n103, and ACL-2010 French-English 10^9 corpus. This is part of the\nfast.ai datasets collection hosted by AWS for convenience of fast.ai\nstudents. See documentation link for citation and license details for each\ndataset.\n",
"managed_by": "[fast.ai](http://www.fast.ai/)",
"update_frequency": "As required",
"tags": "aws-pds,deep learning,natural language processing,machine learning",
"license": "Varies by dataset - see documentation link",
"deprecated": false
},
{
"slug": "fcp-indi",
"name": "International Neuroimaging Data-Sharing Initiative (INDI)",
"description": "This bucket contains multiple neuroimaging datasets that are part of the International Neuroimaging Data-Sharing Initiative. Raw human and non-human primate neuroimaging data include 1) Structural MRI; 2) Functional MRI; 3) Diffusion Tensor Imaging; 4) Electroencephalogram (EEG)\nIn addition to the raw data, preprocessed data is also included for some datasets.\nA complete list of the available datasets can be seen in the documentation lonk provided below. \n",
"managed_by": "[Child Mind Institute](https://childmind.org/our-research/)",
"update_frequency": "Each dataset within INDI has its own release schedule. See release date and frequency for [each dataset](http://fcon_1000.projects.nitrc.org/indi/IndiPro.html)",
"tags": "aws-pds,life sciences,imaging,neuroimaging,neuroscience,magnetic resonance imaging,Homo sapiens",
"license": "ODC-By v1.0 for imaging data and BSD 3-Clause for CPAC, unless otherwise specified",
"deprecated": false
},
{
"slug": "flab",
"name": "FLAb: Fitness Landscapes for Antibodies",
"description": "FLAb is the largest publicly available therapeutic antibody dataset designed to train and benchmark protein AI models. It provides open-access, high-quality developability data on diverse therapeutic properties, including expression, thermostability, immunogenicity, aggregation, polyreactivity, binding affinity, and pharmacokinetics.",
"managed_by": "[Jeffrey Gray Lab, Johns Hopkins University](https://graylab.jhu.edu/)",
"update_frequency": "Any new public release of antibody developabilty data is deposited into FLAb",
"tags": "protein,protein template,machine learning,life sciences,aws-pds",
"license": "https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "fm-ad",
"name": "Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)",
"description": "The Foundation Medicine Adult Cancer Clinical Dataset (FM-AD) is a study conducted by Foundation\nMedicine Inc (FMI). Genomic profiling data for approximately 18,000 adult patients with a diverse\narray of cancers was generated using FoundationeOne, FMI's commercially available, comprehensive\ngenomic profiling assay. This dataset contains open Clinical and Biospecimen data.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is the source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "fmi-radar",
"name": "Finnish Meteorological Institute Weather Radar Data",
"description": "The up-to-date weather radar from the FMI radar network is available as Open Data. The data contain both single radar data along with composites over Finland in GeoTIFF and HDF5-formats. Available composite parameters consist of radar reflectivity (DBZ), rainfall intensity (RR), and precipitation accumulation of 1, 12, and 24 hours. Single radar parameters consist of radar reflectivity (DBZ), radial velocity (VRAD), rain classification (HCLASS), and Cloud top height (ETOP 20). Raw volume data fr",
"managed_by": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)",
"update_frequency": "5 minutes",
"tags": "aws-pds,agriculture,earth observation,weather,meteorological",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "foldingathome-covid19",
"name": "Foldingathome COVID-19 Datasets",
"description": "[Folding@home](http://foldingathome.org) is a massively distributed computing project that uses biomolecular simulations to investigate the [molecular origins of disease](https://foldingathome.org/diseases/) and accelerate the discovery of new therapies. Run by the [Folding@home Consortium](https://foldingathome.org/about/the-foldinghome-consortium/), a worldwide network of research laboratories focusing on a variety of different diseases, Folding@home seeks to address problems in human health o",
"managed_by": "Folding@home",
"update_frequency": "Datasets will be updated periodically as additional simulations are completed.",
"tags": "alchemical free energy calculations,aws-pds,biomolecular modeling,coronavirus,COVID-19,foldingathome,health,life sciences,molecular dynamics,protein",
"license": "[CC0](https://creativecommons.org/share-your-work/public-domain/cc0/)",
"deprecated": false
},
{
"slug": "fomo-norlab",
"name": "FoMo - A Multi-Season Dataset for Robot Navigation in Forêt Montmorency",
"description": "The FoMo dataset is a multi-season collection recorded in a boreal forest environment, featuring deep snow, off-road terrain, steep slopes, and highly variable weather. It provides synchronized multi-modal sensor data—including two lidars (RoboSense and Leishen), an FMCW radar (Navtech), stereo and monocular cameras, dual IMUs, wheel odometry, power data, calibration sequences, and precise ground-truth trajectories via GNSS-PPK fusion.\nDesigned to support research on robust robot autonomy under ",
"managed_by": "[Norlab, Université Laval](https://norlab.ulaval.ca)",
"update_frequency": "The dataset is considered complete and stable. Minor updates or corrections may occur, but they are expected to be infrequent.",
"tags": "aws-pds,robotics,autonomous vehicles,localization,mapping,perception,benchmark,lidar,radar,IMU",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0). See https://creativecommons.org/licenses/by/4.0/\n",
"deprecated": false
},
{
"slug": "ford-multi-av-seasonal",
"name": "Ford Multi-AV Seasonal Dataset",
"description": "This research presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles The vehicles were manually driven on an average route of 66 km in Michigan that included a mix of driving scenarios like the Detroit Airport, freeways, city-centres, university campus and suburban neighbourhood, etc. Each vehicle used in this data collection is a Ford Fusion outfitted with an Applanix POS-LV inertial measurem",
"managed_by": "[Ford Motor Company](https://avdata.ford.com)",
"update_frequency": "New data will be added until the entire dataset is released online.",
"tags": "autonomous vehicles,computer vision,lidar,mapping,robotics,transportation,urban,weather,aws-pds",
"license": "This data is intended for non-commercial academic use only. It is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.",
"deprecated": false
},
{
"slug": "frag-struc",
"name": "RNA structure by fragmentation frequency",
"description": "The fragSTRUC project devises a software to extract RNA secondary structure information from Illumina datasets, based on divalent ions in standard RNA-seq library preparation fragmenting sequences at non-base-paired regions of RNA.",
"managed_by": "The Genome Institute of Singapore (https://www.a-star.edu.sg/gis) and UMass Chan Medical School's RNA Therapeutics Institute (https://www.umassmed.edu/rti/)",
"update_frequency": "Datasets will be updated periodically as additional data is generated.",
"tags": "genomic,transcriptomics,life sciences,bioinformatics,aws-pds",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "fvcom_gom3",
"name": "UMASSD-FVCOM-GOM3-Hindcast",
"description": "The Finite Volume Community Ocean Model (FVCOM) was used to simulate ocean water levels, velocity, temperature and salinity over a multi-decadal period (1984-present) in the waters of the Northeast US including the Gulf of Maine. The model was configured and run by the Dr. Changshen Chen, Director of the Marine Ecosystems Dynamics Modeling Laboratory in the School for Marine Science & Technology at the University of Massachusetts Dartmouth. The triangular mesh has a varying horizontal resoluti",
"managed_by": "Open Science Computing, LLC",
"update_frequency": "None",
"tags": "aws-pds,oceans",
"license": "CC0",
"deprecated": false
},
{
"slug": "gadal",
"name": "Grid Algorithms and Data Analytics Library (GADAL)",
"description": "The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and ",
"managed_by": "[National Renewable Energy Laboratory](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,energy,environmental,sustainability,model",
"license": "Creative Commons Attribution 3.0 United States License",
"deprecated": false
},
{
"slug": "gatk-sv-data",
"name": "GATK Structural Variation (SV) Data",
"description": "This dataset holds the data needed to run a [structural variation discovery pipeline](https://github.com/broadinstitute/gatk-sv)\nfor Illumina short-read whole-genome sequencing (WGS) data in AWS.\n",
"managed_by": "[Loka Inc.](https://loka.com/)",
"update_frequency": "Every 3 months",
"tags": "aws-pds,biology,bioinformatics,genetic,genomic,life sciences,structural variation,gatk-sv,cromwell",
"license": "https://github.com/LokaHQ/aws-open-data/blob/main/LICENSE",
"deprecated": false
},
{
"slug": "gatk-test-data",
"name": "GATK Test Data",
"description": "The GATK test data resource bundle is a collection of files for resequencing human genomic data with the\nBroad Institute's [Genome Analysis Toolkit (GATK)](https://software.broadinstitute.org/gatk/).\n",
"managed_by": "Broad Institute",
"update_frequency": "Every 3 months",
"tags": "aws-pds,biology,bioinformatics,cancer,genetic,genomic,life sciences",
"license": "CC0 1.0 Universal (CC0 1.0) Public Domain Dedication",
"deprecated": false
},
{
"slug": "gbif",
"name": "Global Biodiversity Information Facility (GBIF) Species Occurrences",
"description": "The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure funded by the world's governments providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences, growing daily. The GBIF occurrence dataset combines data from a wide array of sources including specimen-related data from natural history museums, observations from citizen science networks and environment recordi",
"managed_by": "The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org))",
"update_frequency": "Snapshots of GBIF are taken on a monthly basis",
"tags": "aws-pds,earth observation,biodiversity,bioinformatics,conservation,life sciences",
"license": "[CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [terms of use](https://www.gbif.org/terms).",
"deprecated": false
},
{
"slug": "gcbo-dataset",
"name": "Global Carbon Budget Data",
"description": "The Global Carbon Budget (GCB) is recognised globally as the most comprehensive report on global carbon emissions and sinks. This dataset, updated every year, includes estimates of land and ocean carbon fluxes from the suite of models used in the report.",
"managed_by": "Global Carbon Budget Office at the University of Exeter, UK",
"update_frequency": "Annual",
"tags": "climate,land,oceans",
"license": "Open Data. There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "gdelt",
"name": "Global Database of Events, Language and Tone (GDELT)",
"description": "This project monitors the world's broadcast, print,\nand web news from nearly every corner of every country in\nover 100 languages and identifies the people, locations,\norganizations, counts, themes, sources, emotions,\nquotes, images and events driving our global society every\nsecond of every day.\n",
"managed_by": "Unmanaged",
"update_frequency": "Not currently being updated",
"tags": "aws-pds,events,disaster response",
"license": "http://www.gdeltproject.org/about.html#termsofuse",
"deprecated": false
},
{
"slug": "gdr-data-lake",
"name": "Department of Energy’s Geothermal Data Repository (GDR) Data Lake",
"description": "Data released from projects funded by the Department of Energy's Geothermal \nTechnologies Office (DOE GTO) that are too large or complex to be conveniently \naccessed by traditional means. The GDR data lake aims to improve and automate \naccess of high-value geothermal data sets, making data actionable and discoverable \nby researchers and industry to accelerate analysis and advance innovation. \nThis data lake is a sister-data lake to the Department of Energy’s Open Energy Data \nInitiative (OEDI) D",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,energy,geothermal",
"license": "Creative Commons Attribution 4.0 United States License",
"deprecated": false
},
{
"slug": "genomeark",
"name": "Genome Ark",
"description": "The Genome Ark hosts genomic information for the Vertebrate Genomes Project (VGP) and other related projects. The VGP is an international collaboration that aims to generate complete and near error-free reference genomes for all extant vertebrate species. These genomes will be used to address fundamental questions in biology and disease, to identify species most genetically at risk for extinction, and to preserve genetic information of life.",
"managed_by": "The Genome10K community of scientists",
"update_frequency": "Data will be continually updated as it is generated.",
"tags": "aws-pds,biodiversity,bioinformatics,biology,conservation,genetic,genomic,life sciences",
"license": "[G10K Data Use Policy](https://genome10k.soe.ucsc.edu/data-use-policies/)",
"deprecated": false
},
{
"slug": "genomekit",
"name": "GenomeKit genomic data",
"description": "GenomeKit is Deep Genomics’ Python library for fast and easy access to genomic resources such as sequence, data tracks, and annotations. The goal is to let machine learning researchers build data sets easily, and to be creative about how those data sets are designed. Out of the box, GenomeKit provides access to pre-built optimized genomic data files that are required for its operation.",
"managed_by": "Deep Genomics",
"update_frequency": "Data is updated when popular new genome versions (assemblies or annotations) are released",
"tags": "genome,genomic,Homo sapiens,Mus musculus,non-human primate,Rattus norvegicus,variant annotation,bioinformatics,life sciences,open source software",
"license": "Apache License Version 2.0 https://www.apache.org/licenses/LICENSE-2.0",
"deprecated": false
},
{
"slug": "geo_tide_geojsons",
"name": "GeoJSON Files for Geo-TIDE",
"description": "GeoJSON files for the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer",
"managed_by": "MIT Climate & Sustainability Consortium",
"update_frequency": "Quarterly",
"tags": "aws-pds,electricity,energy,environmental,geospatial,supply chain,sustainability,transportation",
"license": "Creative Commons Attribution 4.0 International",
"deprecated": false
},
{
"slug": "geoglows-v2",
"name": "GEOGLOWS Hydrological Model Version 2",
"description": "GEOGLOWS is the Group on Earth Observation's Global Water Sustainability Program. It coordinates efforts from public \nand private entities to make application ready river data more accessible and sustainably available to underdeveloped \nregions. The GEOGLOWS Hydrological Model provides a retrospective and daily forecast of global river discharge at 7 \nmillion river sub-basins. The stream network is a hydrologically conditioned subset of the TDX-Hydro streams and \nbasins data produced by the Unit",
"managed_by": "Riley Hales",
"update_frequency": "Monthly",
"tags": "aws-pds,hydrology,hydrologic model,simulations,zarr,hydrography,geopackage",
"license": "[Creative Commons BY 4 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "geonet",
"name": "GeoNet Aotearoa New Zealand Data",
"description": "GeoNet provides geological hazard information for Aotearoa New Zealand. This dataset contains data and products recorded by the GeoNet sensor network.
GNSS (Global Navigation Satellite System) data include raw data in proprietary and Receiver Independent Exchange Format (RINEX) and local tie-in survey conducted during equipment changes, more details can be found on [the GeoNet geodetic page](https://www.geonet.org.nz/data/types/geodetic) website.
Coastal gauge data include r",
"managed_by": "[GeoNet](https://www.geonet.org.nz/)",
"update_frequency": "Daily for majority of datasets.",
"tags": "aws-pds,broadband,coastal,Continuously Operating Reference Station (CORS),earthquakes,geophysics,geoscience,geoscience,GNSS,GPS",
"license": "GeoNet data are made available free of charge under '[CC3 licence](https://creativecommons.org/licenses/by/3.0/nz/)' to facilitate research and risk assessment. Please acknowledge the GeoNet programme and its sponsors when using the data, see '[GeoNet Data Policy](https://www.geonet.org.nz/policy)'.",
"deprecated": false
},
{
"slug": "geoschem-input-data",
"name": "GEOS-Chem Input Data",
"description": "Input data for the GEOS-Chem Chemical Transport Model, includes NASA/GMAO MERRA-2 and GEOS-FP [meteorological products](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#met), [chemistry input data](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#chemistry-input-data), [emissions input data](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#emis-inputs), and other smaller datasets such as mode",
"managed_by": "[GEOS-Chem Support Team](https://geoschem.github.io/support-team.html)",
"update_frequency": "New meteorological and emission data will be added when available.",
"tags": "aws-pds,climate,weather,meteorological,environmental,air quality,chemistry,atmosphere,model",
"license": "https://geoschem.github.io/license.html",
"deprecated": false
},
{
"slug": "geoschem-nested-input-data",
"name": "GEOS-Chem Nested Input Data",
"description": "Input data for nested-grid simulations using the GEOS-Chem Chemical Transport Model. This includes the NASA/GMAO MERRA-2 and GEOS-FP [meteorological products](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#met), the [HEMCO emission inventories](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/input-overview.html#emis-inputs), and other small data such as [model initial conditions](https://geos-chem.readthedocs.io/en/latest/gcclassic-user-guide/",
"managed_by": "[GEOS-Chem Support Team](https://geoschem.github.io/support-team.html)",
"update_frequency": "New meteorological and emission data will be added when available.",
"tags": "aws-pds,climate,weather,meteorological,environmental,air quality,chemistry,atmosphere,model",
"license": "http://geos-chem.org/license",
"deprecated": false
},
{
"slug": "giab",
"name": "Genome in a Bottle on AWS",
"description": "Several reference genomes to enable translation of whole human genome sequencing to clinical practice. On 11/12/2020 these data were updated to reflect the most [up to date GIAB release](https://www.nist.gov/programs-projects/genome-bottle).",
"managed_by": "[National Institute of Standards and Technology](https://www.nist.gov/)",
"update_frequency": "New data are added as soon as they are available.",
"tags": "aws-pds,genetic,genomic,life sciences,reference index,vcf",
"license": "There are no restrictions on the use of this data. More information on citation is available [here](https://www.nist.gov/programs-projects/genome-bottle).\n",
"deprecated": false
},
{
"slug": "glad-landsat-ard",
"name": "GLAD Landsat ARD",
"description": "The Landsat Analysis Ready Data (ARD) created by the Global Land Analysis and Discovery Lab (GLAD) at the University of Maryland serves as a spatially and temporally consistent input for land cover mapping and change detection at global to local scales. The GLAD ARD represents a 16-day time series of globally consistent, tiled Landsat normalized surface reflectance from 1997 to the present operationally updated every 16 days. Only data from 2020 to present available on the AWS, older data is ava",
"managed_by": "[Global Land Analysis and Discovery Lab](https://glad.umd.edu/)",
"update_frequency": "New 16-day composites are added monthly.",
"tags": "agriculture,earth observation,satellite imagery,geospatial,natural resource,cog",
"license": "The GLAD ARD imposes no restrictions on subsequent redistribution or use, provided proper citation is given following the Creative Commons Attribution License (CC BY).",
"deprecated": false
},
{
"slug": "glo-30-hand",
"name": "Global 30m Height Above Nearest Drainage (HAND)",
"description": "Height Above Nearest Drainage (HAND) is a terrain model that normalizes topography to the relative heights along the drainage network and is used to describe the relative soil gravitational potentials or the local drainage potentials. Each pixel value represents the vertical distance to the nearest drainage. The HAND data provides near-worldwide land coverage at 30 meters and was produced from the 2021 release of the Copernicus GLO-30 Public DEM as distributed in the [Registry of Open Data on AW",
"managed_by": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)",
"update_frequency": "None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://registry.opendata.aws/copernicus-dem/) dataset is updated.\n",
"tags": "aws-pds,elevation,hydrology,agriculture,disaster response,satellite imagery,geospatial,cog,stac",
"license": "Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus WorldDEM™-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The use of the HAND data falls under the terms and conditions of",
"deprecated": false
},
{
"slug": "global-drought-flood-catalogue",
"name": "A Global Drought and Flood Catalogue from 1950 to 2016",
"description": "Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production, among others. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to",
"managed_by": "[PREP-NexT Lab](https://github.com/PREP-NexT)",
"update_frequency": "No future updates planned.",
"tags": "aws-pds,floods,global,netcdf,precipitation,near-surface specific humidity,near-surface air temperature",
"license": "GDFC archive is made available under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n",
"deprecated": false
},
{
"slug": "gmsdata",
"name": "The Genome Modeling System",
"description": "The Genome Institute at Washington University has developed a high-throughput, fault-tolerant analysis information management system called the Genome Modeling System (GMS), capable of executing complex, interdependent, and automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. GMS includes a full system image with software and services, expandable from one workstation to a ",
"managed_by": "Genome Institute at the Washington University School of Medicine in St. Louis",
"update_frequency": "Not updated",
"tags": "aws-pds,genetic,genomic,life sciences",
"license": "[GNU Lesser General Public License v3.0](https://github.com/genome/gms/blob/ubuntu-12.04/LICENSE)",
"deprecated": false
},
{
"slug": "gnomad-data-lakehouse-ready",
"name": "Genome Aggregation Database (gnomAD) - Data Lakehouse Ready",
"description": "The [Genome Aggregation Database (gnomAD)](https://gnomad.broadinstitute.org/) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects\nSign up for the gnomAD mailing list [here](http://broad.io/gnomad_list). This dataset was derived from summary data from gnomAD release 3.1, available on the [Registry of Open Data on AWS](https://registry.opendata.aws/broad-gnomad/)",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Not updated",
"tags": "biology,bioinformatics,biotech blueprint,genomic,genetic,life sciences,parquet,population genetics,vcf,whole genome sequencing",
"license": "[MIT](https://github.com/broadinstitute/gnomad_methods/blob/master/LICENSE); [terms of use](https://gnomad.broadinstitute.org/terms)",
"deprecated": true
},
{
"slug": "gnss-ro-opendata",
"name": "Earth Radio Occultation",
"description": "This is an updating archive of radio occultation (RO) data using the transmitters of the Global Navigation Satellite Systems (GNSS) as generated and processed by the COSMIC DAAC (ucar), the Jet Propulsion Laboratory (jpl) of the California Institute of Technology, and the Radio Occultation Meteorology Satellite Application Facility (romsaf). The contributions for ucar and romsaf are currently active.
This dataset is funded by the NASA Earth Science Data Systems and the Advancing Collaborativ",
"managed_by": "Verisk Atmospheric and Environmental Research, Inc.",
"update_frequency": "The dataset is updated monthly for UCAR and ROM SAF contributions only. The update frequency for the JPL contribution is to be determined.",
"tags": "aws-pds,atmosphere,climate,earth observation,global,signal processing,weather",
"license": "A Creative Commons open-use licence (https://www.ucar.edu/terms-of-use/data) applies to the data contributed by UCAR; the EUMETSAT Data Policy (https://www.eumetsat.int/eumetsat-data-licensing) applies to the data contributed by the ROM SAF; and a creative commons license (https://creativecommmons.o",
"deprecated": false
},
{
"slug": "google-brain-genomics-public",
"name": "Google Brain Genomics Sequencing Dataset for Benchmarking and Development",
"description": "To facilitate benchmarking and development, the Google Brain group has sequenced 9 human samples covering the Genome in a Bottle truth sets on different sequencing instruments, sequencing modalities (Illumina short read and Pacific BioSciences long read), sample preparation protocols, and for whole genome and whole exome capture. The original source of these data are [gs://google-brain-genomics-public](https://console.cloud.google.com/storage/browser/brain-genomics-public/research/sequencing;tab",
"managed_by": "Amazon Web Services",
"update_frequency": "Occasionally as new derived files (alignment files or variant call files) are generated",
"tags": "amazon.science,bioinformatics,life sciences,genetic,genomic,fastq,short read sequencing,long read sequencing,whole exome sequencing,whole genome sequencing",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "google-ngrams",
"name": "Google Books Ngrams",
"description": "N-grams are fixed size tuples of items. In this case the items are words extracted from the Google Books corpus. The n specifies the number of elements in the tuple, so a 5-gram contains five words or characters. The n-grams in this dataset were produced by passing a sliding window of the text of books and outputting a record for each new token.",
"managed_by": "Not managed",
"update_frequency": "Not updated",
"tags": "amazon.science,natural language processing",
"license": "Creative Commons Attribution 3.0 Unported License",
"deprecated": false
},
{
"slug": "graf-reforecast",
"name": "GRAF Reforecast",
"description": "A zarr-formatted dataset of 1836 reforecast cases (approx. 5 years) from The Weather Company GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Center for Atmospheric Research (NCAR) Model for Predictions Across Scales (MPAS). GRAF is global, but the configuration for this reforecast had a mesh refinement to approx. 4 km over the US, Caribbean Basin, and Europe, and 15 km elsewhere. This model was designed to run much of its computation on graphical processing",
"managed_by": "[The Weather Company](https://www.weathercompany.com/)",
"update_frequency": "One time push only",
"tags": "atmosphere,forecast,geoscience,geospatial,model,near-surface air temperature,near-surface relative humidity,precipitation,wind speeds,cloud amount",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "green_et",
"name": "IWMI DIWASA Green ET for Africa",
"description": "Green evapotranspiration (Green ET) is the portion of ET derived from green water, which includes soil moisture and rainfall used by vegetation. It represents a key component of green water fluxes in water accounting. Green ET consists of evaporation from soil moisture in non-irrigated areas, transpiration from rainfed crops and natural vegetation, and interception losses from precipitation on vegetation. It plays a crucial role in rainfed agriculture, drought monitoring, and sustainable water m",
"managed_by": "[IWMI](https://www.iwmi.org/)",
"update_frequency": "None",
"tags": "soil moisture,rainfed cropland,interception loss,evapotranspiration,water",
"license": "Creative commons open license",
"deprecated": false
},
{
"slug": "gretel-synthetic-safety-alignment-en-v1",
"name": "Gretel Synthetic Safety Alignment Dataset",
"description": "A comprehensive dataset designed for aligning language models with safety and ethical guidelines. Contains 8,361 curated triplets of prompts, responses, and safe responses across various risk categories. Each entry includes safety scores, judge reasoning, and harm probability assessments, making it valuable for model alignment, testing, and benchmarking.",
"managed_by": "[Gretel.ai](https://gretel.ai)",
"update_frequency": "Static dataset, version 1.0 (Released December 2024)",
"tags": "machine learning,natural language processing,ai safety,synthetic data",
"license": "Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0)",
"deprecated": false
},
{
"slug": "grillo-openeew",
"name": "OpenEEW",
"description": "Grillo has developed an IoT-based earthquake early-warning system,\nwith sensors currently deployed in Mexico, Chile, Puerto Rico and Costa Rica,\nand is now opening its entire archive of unprocessed accelerometer\ndata to the world to encourage the development of new algorithms\ncapable of rapidly detecting and characterizing earthquakes in\nreal time.\n",
"managed_by": "[Grillo](https://grillo.io/)",
"update_frequency": "Approximately every 5 minutes",
"tags": "disaster response,earth observation,earthquakes,aws-pds",
"license": "https://github.com/openeew/openeew#license",
"deprecated": false
},
{
"slug": "gtgseq",
"name": "Garvan Institute Long Read Sequencing Benchmark Data",
"description": "The dataset contains reference samples that will be useful for benchmarking and comparing bioinformatics tools for genome analysis. Examples include: NA12878 (HG001) and NA24385 (HG002) sequenced on an Oxford Nanopore Technologies (ONT) PromethION using the latest R10.4.1 flowcells; and, UHR RNA (direct-RNA) on an ONT PromethION using the latest RNA004 flowcells. Raw signal data output by the sequencer is provided for these datasets in BLOW5 format, and can be rebasecalled when basecalling softw",
"managed_by": "Genomic Technologies Group, Garvan Institute of Medical Research (https://www.garvan.org.au/research/labs-groups/genomic-technologies-lab)",
"update_frequency": "We plan to extend this open dataset with additional samples, including sequencing runs from vendors other than ONT. We will continue to provide updated basecalls when there is a major update to the basecalling software.",
"tags": "genomic,life sciences,long read sequencing,bioinformatics,aws-pds",
"license": "[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "gulfwide-avian-monitoring",
"name": "Gulfwide Avian Colony Monitoring Survey Photos",
"description": "For this project, The Water Institute (the Institute) and subcontractor Colibri Ecological Consulting, LLC (Colibri) utilized established methods and protocols capable of assessing changes of colonial waterbird populations and their important habitats within individual states and the broader northern Gulf of Mexico region. Data collection activities included:\n Aerial Photographic Nest Surveys: Implementation of fixed-wing aircraft surveys intended to assess waterbird colonies and document as",
"managed_by": "[CPRA](https://coastal.la.gov/) and [The Water Institute](https://thewaterinstitute.org/)",
"update_frequency": "~2 years",
"tags": "biology,conservation,ecosystems,object detection,labeled,environmental,aws-pds",
"license": "Creative Commons BY-SA",
"deprecated": false
},
{
"slug": "guys-breast-cancer-lymph-nodes",
"name": "Guy's Breast Cancer Lymph Nodes (GRAPE)",
"description": "This is a retrospective dataset of 1523 H&E-stained whole slide images (WSI) of lymph nodes from breast cancer patients. The cohort consisted of 177 patients (122 LN-positive - metastasis was reported in at least 1 LN - and 55 LN-negative patients) with invasive breast carcinoma treated between 1984 and 2002 at Guy’s Hospital London, UK. Slides were scanned and digitised at 40x magnification (0.23 µm/pixel), NanoZoomer H.T2.0 2.0-HT (Hamamatsu Photonics UK, Ltd, Welwyn Garden City, UK). WSIs are",
"managed_by": "http://www.cancerbioinformatics.co.uk",
"update_frequency": "New data will be added as soon as it's available",
"tags": "aws-pds,biology,cancer,life sciences,breast cancer,computational pathology,histopathology",
"license": "This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.",
"deprecated": false
},
{
"slug": "hail-vep-pipeline",
"name": "Variant Effect Predictor (VEP) and the Loss-Of-Function Transcript Effect Estimator (LOFTEE) Plugin",
"description": "VEP determines the effect of genetic variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions. The European Bioinformatics Institute produces the VEP tool/db and releases updates every 1 - 6 months. The latest release contains 267 genomes from 232 species containing 5567663 protein coding genes. This dataset hosts the last 5 releases for human, rat, and zebrafish. Also, it hosts the required reference files for",
"managed_by": "[Tennex](https://www.tennex.io/)",
"update_frequency": "New packages are added as soon as they are available and confirmed to work with recent versions of Hail.",
"tags": "aws-pds,genome wide association study,genomic,life sciences,vep,loftee",
"license": "[VEP](https://uswest.ensembl.org/info/about/publications.html) use is governed by the Apache 2.0 licenses, and [LOFTEE](https://github.com/konradjk/loftee/blob/master/LICENSE) use is governed by the MIT license.\n",
"deprecated": false
},
{
"slug": "hcmi-cmdc",
"name": "Human Cancer Models Initiative (HCMI) Cancer Model Development Center",
"description": "The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel,\nnext-generation, tumor-derived culture models annotated with genomic and clinical data.\nHCMI-developed models and related data are available as a community resource. The NCI is\ncontributing to the initiative by supporting four Cancer Model Development Centers (CMDCs). CMDCs\nare tasked with producing next-generation cancer models from clinical samples. The cancer models\ninclude tumor types that are",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences,whole genome sequencing,STRIDES",
"license": "NIH Genomic Data Sharing Policy https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "hcp-openaccess",
"name": "The Human Connectome Project",
"description": "The Human Connectome Project (HCP Young Adult, HCP-YA) is mapping the healthy human connectome by collecting and freely distributing neuroimaging and behavioral data on 1,200 normal young adults, aged 22-35.",
"managed_by": "[Connectome Coordination Facility](https://www.humanconnectome.org/ccf-staff)",
"update_frequency": "Uncertain",
"tags": "aws-pds,biology,imaging,neurobiology,neuroimaging,neuroscience,life sciences",
"license": "[HCP Data Use Agreement](https://www.humanconnectome.org/storage/app/media/data_use_terms/DataUseTerms-HCP-Open-Access-26Apr2013.pdf)",
"deprecated": false
},
{
"slug": "hecatomb",
"name": "Hecatomb Databases",
"description": "Preprocessed databases for use with the Hecatomb pipeline for viral and phage sequence annotation.",
"managed_by": "[Washington University in St. Louis](https://wustl.edu/)",
"update_frequency": "Every 6 to 12 months",
"tags": "aws-pds,life sciences,genetic,genomic,metagenomics,bioinformatics,whole genome sequencing,virus",
"license": "[MIT](https://opensource.org/licenses/MIT)",
"deprecated": false
},
{
"slug": "helpful-sentences-from-reviews",
"name": "Helpful Sentences from Reviews",
"description": "A collection of sentences extracted from customer reviews labeled with their helpfulness score.",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not updated",
"tags": "amazon.science,information retrieval,natural language processing,text analysis,json",
"license": "This data is available for anyone to use under the terms of the CDLA-Sharing license, which is available [here](https://cdla.dev/sharing-1-0/)",
"deprecated": false
},
{
"slug": "hirlam",
"name": "HIRLAM Weather Model",
"description": "HIRLAM (High Resolution Limited Area Model) is an operational synoptic and mesoscale weather prediction model managed by the Finnish Meteorological Institute.",
"managed_by": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)",
"update_frequency": "The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corresponding model runs are available roughly five hours after analysis time (~ after model run has started).",
"tags": "aws-pds,agriculture,earth observation,climate,weather,meteorological",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "hpgp-data",
"name": "Human PanGenomics Project",
"description": "This dataset includes sequencing data, assemblies, and analyses for the offspring of ten parent-offspring trios.",
"managed_by": "(Human Pangenome Reference Consortium)[https://humanpangenome.org/]",
"update_frequency": "Data will be added and updated as technologies improve or new data uses are encountered.",
"tags": "aws-pds,genomic,genetic,life sciences,fastq,fast5,cram",
"license": "[Creative Commons CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/deed.en)",
"deprecated": false
},
{
"slug": "hprc-epigenome",
"name": "Epigenomes of the Human Pangenome Reference Consortium (HPRC) Release 2",
"description": "The Human Pangenome Reference Consortium (HPRC) Release 2 represents a landmark achievement in genomics, providing high-quality phased genome assemblies from over 200 individuals with comprehensive functional genomics data. The HPRC Epigenome Browser provides researchers a way to explore all epigenomics data generated by release 2. The HPRC Epigenome Browser (HPRCEB) is a modern, interactive web portal that democratizes access to HPRC Release 2 epigenomics data through an intuitive interface sup",
"managed_by": "Ting Wang Lab (https://wang.wustl.edu/)",
"update_frequency": "Annual. The repository will be updated with each new batch of data as it is generated and released under the next HPRC yearly cycle.",
"tags": "biology,bioinformatics,genetic,genomic,epigenomics,life sciences,aws-pds",
"license": "External data users may freely download, analyze, and publish results based on any HPRC data provided here without restrictions.",
"deprecated": false
},
{
"slug": "hsip-lidar-us-cities",
"name": "Homeland Security and Infrastructure US Cities",
"description": "The U.S. Cities elevation data collection program supported the US Department of Homeland Security Homeland Security and Infrastructure Program (HSIP). As part of the HSIP Program, there were 133+ U.S. cities that had imagery and LiDAR collected to provide the Homeland Security, Homeland Defense, and Emergency Preparedness, Response and Recovery (EPR&R) community with common operational, geospatially enabled baseline data needed to analyze threat, support critical infrastructure protection and e",
"managed_by": "[Hobu, Inc.](https://hobu.co)",
"update_frequency": "Periodically",
"tags": "aws-pds,elevation,disaster response,geospatial,lidar",
"license": "US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map",
"deprecated": false
},
{
"slug": "huj-herbarium",
"name": "National Herbarium of Israel",
"description": "Our collection encompasses approximately one million vascular plant specimens from the Mediterranean and Middle East biodiversity hotspot, representing flora from Israel, Jordan, Hermon, Sinai, Egypt, the Caucasus, Arabia, North Africa, and throughout the Mediterranean basin. This scientifically significant repository includes published voucher specimens, original specimens used for \"Flora Palaestina\" illustrations, and critical references for the Israeli gene bank collections. The ongoing digit",
"managed_by": "National Natural History Collections, The Hebrew University of Jerusalem",
"update_frequency": "Monthly",
"tags": "biology,life sciences,biodiversity,environmental,climate,digital preservation,imaging,image processing,aws-pds",
"license": "CC-BY-SA 4.0",
"deprecated": false
},
{
"slug": "human-microbiome-project",
"name": "The Human Microbiome Project",
"description": "The NIH-funded Human Microbiome Project (HMP) is a collaborative effort of over 300 scientists from more than 80 organizations to comprehensively characterize the microbial communities inhabiting the human body and elucidate their role in human health and disease. To accomplish this task, microbial community samples were isolated from a cohort of 300 healthy adult human subjects at 18 specific sites within five regions of the body (oral cavity, airways, urogenital track, skin, and gut). Targeted",
"managed_by": "[The National Institutes of Health Office of Strategic Coordination - The Common Fund](https://commonfund.nih.gov/hmp)",
"update_frequency": "Uncertain",
"tags": "aws-pds,life sciences,genetic,genomic,metagenomics,microbiome,fasta,amino acid,fastq",
"license": "The data is publicly available to the community free of charge.",
"deprecated": false
},
{
"slug": "humancellatlas",
"name": "Human Cell Atlas",
"description": "The Human Cell Atlas (HCA) is a collaborative community of international scientists. Our mission is to create comprehensive reference maps of all the cells in the human body as a basis for both understanding human health and diagnosing, monitoring, and treating disease. The HCA registry has more than one thousand member scientists from hundreds of institutions around the world. The project is steered and governed by an Organizing Committee, co-chaired by Aviv Regev and Sarah Teichmann.",
"managed_by": "UC Santa Cruz Genomics Institute, University of California, Santa Cruz, UCSC",
"update_frequency": "Monthly",
"tags": "life sciences,biology,cell biology,genome,genomic,transcriptomics,gene expression,single-cell transcriptomics,cell imaging,Homo sapiens",
"license": "https://data.humancellatlas.org/about/data-use-agreement",
"deprecated": false
},
{
"slug": "humor-detection",
"name": "Humor Detection from Product Question Answering Systems",
"description": "This dataset provides labeled humor detection from product question answering systems.\nThe dataset contains 3 csv files: [Humorous.csv](https://humor-detection-pds.s3-us-west-2.amazonaws.com/Humorous.csv) \ncontaining the humorous product questions, \n[Non-humorous-unbiased.csv](https://humor-detection-pds.s3-us-west-2.amazonaws.com/Non-humorous-unbiased.csv) containing \nthe non-humorous prodcut questions from the same products as the humorous one, and, \n[Non-humorous-biased.csv](https://humor-det",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,machine learning",
"license": "[CDLA-Sharing](https://cdla.io/sharing-1-0/)",
"deprecated": false
},
{
"slug": "humor-patterns",
"name": "Humor patterns used for querying Alexa traffic",
"description": "Humor patterns used for querying Alexa traffic when creating the taxonomy described in the paper \"“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents\" by Shani C., Libov A., Tolmach S., Lewin-Eytan L., Maarek Y., and Shahaf D. (CHI LBW 2022). These patterns corrospond to the researchers' hypotheses regarding what humor types are likely to appear in Alexa traffic. These patterns were used for querying Alexa traffic to evaluate these hypotheses.",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently updated",
"tags": "amazon.science,dialog,natural language processing,machine learning",
"license": "CDLA-Sharing license",
"deprecated": false
},
{
"slug": "hycom-global-drifters",
"name": "HYCOM-OceanTrack Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset",
"description": "A combined dataset of simulated ocean sea surface height, near-surface velocities, and particle trajectories from a global 1/25th degree HYbrid Coordinate Ocean Model (HYCOM) 1-year run.",
"managed_by": "Shane Elipot",
"update_frequency": "Not updated",
"tags": "aws-pds,drifters,Eulerian,HYCOM,Lagrangian,oceans,ocean simulation,ocean circulation,ocean currents,ocean velocity",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "hycom-gofs-3pt1-reanalysis",
"name": "HYbrid Coordinate Ocean Model Global Ocean Forecast System Reanalysis",
"description": "Global Ocean Forecasting System (GOFS) 3.1 output on the GLBv0.08 grid. The resolution is 0.08° resolution between 40°S and 40°N, 0.04° poleward of these latitudes. The temportal frequenct is 3 hourly. This data was created by the Naval Research Laboratory: Ocean Dynamics and Prediction Branch.",
"managed_by": "[COAPS](https://www.coaps.fsu.edu/)",
"update_frequency": "Static Dataset Covering 1994-01-01 to 2015-12-31",
"tags": "aws-pds,global,oceans",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "ibl-autism",
"name": "IBL Neuropixels Brainwide Map on AWS",
"description": "Electrophysiological recordings of mouse brain activity acquired during a decision making task in multiple autism mice models.",
"managed_by": "[International Brain Laboratory](https://www.internationalbrainlab.com)",
"update_frequency": "TBD",
"tags": "aws-pds,life sciences,neuroscience,neurophysiology,open source software,Mus musculus,autism spectrum disorder",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "ibl-behaviour",
"name": "IBL Behavioral Data on AWS",
"description": "Behavioral data of mice performing a decision-making task, associated with 2020 publication of the IBL.",
"managed_by": "[International Brain Laboratory](https://www.internationalbrainlab.com)",
"update_frequency": "TBD",
"tags": "aws-pds,life sciences,neuroscience,neurophysiology,open source software,Mus musculus",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "ibl-brain-wide-map",
"name": "IBL Neuropixels Brainwide Map on AWS",
"description": "Electrophysiological recordings of mouse brain activity acquired during a decision making task.",
"managed_by": "[International Brain Laboratory](https://www.internationalbrainlab.com)",
"update_frequency": "TBD",
"tags": "aws-pds,life sciences,neuroscience,neurophysiology,open source software,Mus musculus",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "ibl-reproducible-ephys",
"name": "IBL Neuropixels Reproducible Ephys Data on AWS",
"description": "Electrophysiological recordings acquired using Neuropixels probes in different mice and labs, targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus).",
"managed_by": "[International Brain Laboratory](https://www.internationalbrainlab.com)",
"update_frequency": "TBD",
"tags": "aws-pds,life sciences,neuroscience,neurophysiology,open source software,Mus musculus",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "iceye-opendata",
"name": "ICEYE Synthetic Aperture Radar (SAR) Open Dataset",
"description": "ICEYE operates the world’s largest constellation of synthetic aperture radar (SAR) satellites, delivering unmatched access to persistent, high-resolution Earth observation data regardless of time of day or weather conditions. The ICEYE Open Dataset makes a curated selection of SAR imagery publicly available to promote research, innovation, and education in the geospatial community. ICEYE’s constellation enables rapid revisit rates and flexible imaging modes, unlocking insights into natural disas",
"managed_by": "[ICEYE](https://www.iceye.com)",
"update_frequency": "New data is added frequently.",
"tags": "aws-pds,synthetic aperture radar,stac,earth observation,satellite imagery,image processing,geospatial,computer vision,disaster response",
"license": "The data is provided under the Creative Commons License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), which gives the user the right to share, copy, and redistribute the material in any medium or format, as well as adapt, remix, transform, and build upon the material for any purpose, ev",
"deprecated": false
},
{
"slug": "icgc",
"name": "ICGC on AWS",
"description": "The International Cancer Genome Consortium (ICGC) coordinates projects with the common aim of accelerating research into the causes and control of cancer. The PanCancer Analysis of Whole Genomes (PCAWG) study is an international collaboration to identify common patterns of mutation in whole genomes from ICGC. More than 2,400 consistently analyzed genomes corresponding to over 1,100 unique ICGC donors are now freely available on Amazon S3 to credentialed researchers subject to ICGC data sharing p",
"managed_by": "[International Cancer Genome Collaboratory](https://dcc.icgc.org/)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,cancer,genetic,genomic,life sciences,bam,vcf",
"license": "Data use is subject to the access and publication polices of the source. Distribution of the data is subject to ICGC Trusted Partner Approval. More information on terms of use is available at https://icgc.org/daco",
"deprecated": false
},
{
"slug": "ichangemycity",
"name": "IChangeMyCity Complaints Data from Janaagraha",
"description": "The [IChangeMyCity](https://www.ichangemycity.com) project provides insight into the complaints raised by citizens from diffent cities of India related to the issues in their neighbourhoods and the resolution of the same by the civic bodies.",
"managed_by": "Not currently managed",
"update_frequency": "Daily",
"tags": "aws-pds,cities,civic,complaints",
"license": "There are no restrictions on the use of data received from IChangeMyCity.com. More information on licensing and IChangeMyCity data citation is available from IChangeMyCity.com",
"deprecated": true
},
{
"slug": "ideam-radares",
"name": "IDEAM - Colombian Radar Network",
"description": "Historical and one-day delay data from the IDEAM radar network.",
"managed_by": "[IDEAM](http://www.ideam.gov.co/)",
"update_frequency": "Updated level II data is added as soon as it is available.",
"tags": "aws-pds,agriculture,earth observation,natural resource,weather,meteorological",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "igvf-consortium",
"name": "The Impact of Variation on Function Consortium (IGVF)",
"description": "The IGVF (Impact of Genomic Variation on Function) Consortium aims to understand how genomic variation affects genome function, \nwhich in turn impacts phenotype. The NHGRI is funding this collaborative program that brings together teams of investigators who \nwill use state-of-the-art experimental and computational approaches to model, predict, characterize and map genome function, how \ngenome function shapes phenotype, and how these processes are affected by genomic variation. These joint effort",
"managed_by": "IGVF Data Administration and Coordination Center at Stanford University",
"update_frequency": "Daily",
"tags": "aws-pds,biology,bioinformatics,genetic,genomic,life sciences",
"license": "CC BY 4.0 - https://creativecommons.org/licenses/by/4.0/ - You are free to share and adapt this data with proper attribution",
"deprecated": false
},
{
"slug": "ihart",
"name": "iHART Whole Genome Sequencing Data Set",
"description": "iHART is the [Hartwell Foundation](http://www.thehartwellfoundation.org/)’s Autism Research and Technology Initiative. This release contains whole genome data from over 1000 families with 2 or more children with autism, of which biomaterials were provided by the Autism Genetic Resource Exchange ([AGRE](http://research.agre.org/)).",
"managed_by": "[Stanford University](https://wall-lab.stanford.edu/projects/ihart/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,autism spectrum disorder,genetic,genomic,life sciences,whole genome sequencing,bam,vcf",
"license": "Data use is subject to the access and publication polices of the iHART. More information on terms of use is available at [iHART website](http://www.ihart.org/)",
"deprecated": false
},
{
"slug": "ilmn-dragen-1kgp",
"name": "1000 Genomes Phase 3 Reanalysis with DRAGEN 3.5, 3.7, 4.0, 4.2, and 4.4",
"description": "# Description\n\n## Overivew\n\nThis dataset contains alignment files and small variant (includes single nucleotide variants (SNV) and indels), copy number variant (CNV), short tandem repeat (*i.e.*, repeat expansion; STR), structural variant (SV) and other variant call files from the [1000 Genomes Project (1KGP) Phase 3 dataset](https://www.internationalgenome.org/) (3,202 individuals, 602 trios) using Illumina DRAGEN v3.5.7b, v3.7.6, v4.0.3, v4.2.7, and v4.4.7 software.\nAll DRAGEN analyses were pe",
"managed_by": "[Illumina, Inc.](https://www.illumina.com/products/by-type/informatics-products/dragen-bio-it-platform.html)",
"update_frequency": "Files may be updated subsequent to changes to the 1000 Genomes Project data set or select new DRAGEN features or offerings.",
"tags": "aws-pds,bam,bioinformatics,biology,cram,genetic,genomic,genotyping,life sciences,machine learning",
"license": "TBD",
"deprecated": false
},
{
"slug": "in-elevation",
"name": "Indiana Statewide Elevation Catalog",
"description": "The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital LiDAR LAS files stored in AWS, dating back to the 2011-2013 collection and including the NRCS-funded 2016-2020 collection. These LiDAR datasets are available as uncompressed LAS files, for cloud storage and access. Each year's data is organized into a tile grid scheme covering the entire geography of Indiana, ensuring easy access and efficient processing. The tiles' naming reflects each til",
"managed_by": "Indiana Geographic Information Office",
"update_frequency": "The State of Indiana has another four-year cycle of collecting orthoimagery and Lidar starting in 2025 and continuing through 2028. The collections are designated by counties in three groups that cover Indiana, South to North. These areas are frequently referred to as Tiers in the other documentation. For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State.",
"tags": "lidar,aws-pds,earth observation,geospatial,imaging,mapping,natural resource,sustainability,agriculture",
"license": "Access to Indiana Geographic Information Office Lidar is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode",
"deprecated": false
},
{
"slug": "in-imagery",
"name": "Indiana Statewide Digital Aerial Imagery Catalog",
"description": "The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital orthophotography dating back to 2005. Every year's worth of imagery is available as Cloud Optimized GeoTIFF (COG) files, original GeoTIFF, and other compressed deliverables such as ECW and MrSID. Additionally, each imagery year is organized into a tile grid scheme covering the entire geography of Indiana. All years of imagery are tiled from a 5,000 ft grid or sub tiles depending upon the",
"managed_by": "Indiana Geographic Information Office",
"update_frequency": "The State of Indiana has had a 4-year cycle collecting imagery. The collections are designated by counties in three groups that cover Indiana, South to North. These areas are frequently referred to as Tiers in the other documentation. For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State.",
"tags": "aerial imagery,aws-pds,earth observation,geospatial,imaging,mapping,cog,natural resource,sustainability,agriculture",
"license": "Access to Indiana Geographic Information Office Orthoimagery is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode",
"deprecated": false
},
{
"slug": "inaturalist-open-data",
"name": "iNaturalist Licensed Observation Images",
"description": "iNaturalist is a community science effort in which participants share observations of living organisms that they encounter and document with photographic evidence, location, and date. The community works together reviewing these images to identify these observations to species. This collection represents the licensed images accompanying iNaturalist observations.",
"managed_by": "[iNaturalist](https://www.inaturalist.org/) is an independent, tax-exempt, 501(c)(3), not-for-profit organization based in the United States of America (EIN/Tax ID: 92-1296468).",
"update_frequency": "Images are posted in real time, and we are currently copying over images from existing observations. Metadata is updated monthly. More information on the metadata can be found in the [documentation](https://github.com/inaturalist/inaturalist-open-data)",
"tags": "aws-pds,earth observation,biodiversity,bioinformatics,conservation,life sciences",
"license": "Creative Commons or Public Domain (CC0), varying by image. More information on how to query and properly treat licenses can be found in the documentation",
"deprecated": false
},
{
"slug": "indian-high-court-judgments",
"name": "Indian High Court Judgments",
"description": "This dataset contains judgements from the Indian High Courts, downloaded from ecourts website. It contains judgments of 25 high courts, along with raw metadata (in json format) and structured metadata (in parquet format). Judgments from the website are further compressed to optimize for size (care has been taken to not have any loss of data either in content or in visual appearance). Tar files are also made available in addition to the individual pdf files to make it easier for bulk download.",
"managed_by": "[Dattam Labs](https://dattam.in)",
"update_frequency": "Quarterly",
"tags": "legal data",
"license": "CC-BY-4.0",
"deprecated": false
},
{
"slug": "indian-supreme-court-judgments",
"name": "Indian Supreme Court Judgments",
"description": "This dataset contains judgements from the Indian Supreme Court, downloaded from ecourts website. It contains judgments from 1950 to 2025, along with raw metadata (in json format) and structured metadata in parquet format. Judgments are available in both English and regional Indian languages in zip format for easier download.",
"managed_by": "[Dattam Labs](https://dattam.in)",
"update_frequency": "Bi-monthly",
"tags": "legal data,aws-pds",
"license": "CC-BY-4.0",
"deprecated": false
},
{
"slug": "inlab-covid-19-images-dataset",
"name": "InRad COVID-19 X-Ray and CT Scans",
"description": "This dataset is a collection of anonymized thoracic radiographs (X-Rays) and computed tomography (CT) scans of patients with suspected COVID-19. Images are acommpanied by a positive or negative diagnosis for SARS-CoV2 infection via RT-PCR. These images were provided by Hospital das Clínicas da Universidade de São Paulo, Hospital Sirio-Libanes, and by Laboratory Fleury.",
"managed_by": "[Faculdade de Medicina da Universidade de São Paulo Institute of Radiology (InRad)](http://inrad.hc.fm.usp.br)",
"update_frequency": "As Necessary",
"tags": "aws-pds,bioinformatics,coronavirus,COVID-19,health,life sciences,medicine,SARS",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "intelinair_agriculture_vision",
"name": "AgricultureVision",
"description": "Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. The original dataset affiliated with the 2020 CVPR paper includes 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substant",
"managed_by": "Intelinair, Inc.",
"update_frequency": "Periodically",
"tags": "aws-pds,aerial imagery,agriculture,computer vision,deep learning,machine learning",
"license": "Provided in the bucket.",
"deprecated": false
},
{
"slug": "intelinair_corn_kernel_counting",
"name": "Corn Kernel Counting Dataset",
"description": "Dataset associated with the March 2021 Frontiers in Robotics and AI paper \"Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels\", DOI: 10.3389/frobt.2021.627009",
"managed_by": "Intelinair, Inc.",
"update_frequency": "Periodically",
"tags": "aws-pds,agriculture,computer vision,machine learning",
"license": "Provided in the bucket.",
"deprecated": false
},
{
"slug": "intelinair_longitudinal_nutrient_deficiency",
"name": "Longitudinal Nutrient Deficiency",
"description": "Dataset associated with the 2021 AAAI Paper- Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery. The dataset contains 3 image sequences of aerial imagery from 386 farm parcels which have been annotated for nutrient deficiency stress.",
"managed_by": "Intelinair, Inc.",
"update_frequency": "Periodically",
"tags": "aws-pds,aerial imagery,agriculture,computer vision,deep learning,machine learning",
"license": "Provided in the bucket.",
"deprecated": false
},
{
"slug": "io-lulc",
"name": "10m Annual Land Use Land Cover (9-class)",
"description": "This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC)\nderived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. \n\nEach map is a composite of LULC predictions for 9 classes throughout the year\nin order to generate a representative snapshot of each year. \n\nThis dataset was generated by Impact Observatory, which used billions of human-labeled pixels \n(curated by the National Geographic Society) to train",
"managed_by": "[Impact Observatory](https://www.impactobservatory.com/)",
"update_frequency": "A new year is made available annually, each January. A new time series was provided July 2023 to reduce anomalous change.",
"tags": "aws-pds,earth observation,environmental,geospatial,satellite imagery,sustainability,stac,cog,land cover,land use",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "irs990",
"name": "IRS 990 Filings",
"description": "**On December 16, 2021 the IRS announced that it would [discontinue updates to the IRS 990 Filings dataset on AWS, starting December 31, 2021](https://www.irs.gov/newsroom/irs-makes-tax-exempt-organization-search-primary-source-to-get-exempt-organization-data).**\n\nThe IRS has requested public inquiries be directed to +1-800-829-1040.\n\nMachine-readable data from certain electronic 990 forms filed with the IRS from 2013 to present.\n",
"managed_by": "The Internal Revenue Service",
"update_frequency": "New filings are added regularly",
"tags": "aws-pds,regulatory,statistics",
"license": "None",
"deprecated": true
},
{
"slug": "isdasoil",
"name": "iSDAsoil",
"description": "iSDAsoil is a resource containing soil property predictions for the entire African continent, generated using machine learning. Maps for over 20 different soil properties have been created at 2 different depths (0-20 and 20-50cm). Soil property predictions were made using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples. Included in this dataset are images of predicted soil properties, model error and satellite covariates used in the map",
"managed_by": "[Innovative Solutions for Decision Agriculture (iSDA)](https://www.isda-africa.com/)",
"update_frequency": "Based upon the availability of new data",
"tags": "agriculture,analytics,aws-pds,biodiversity,conservation,deep learning,food security,geospatial,machine learning,satellite imagery",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "iserv",
"name": "ISERV",
"description": "ISS SERVIR Environmental Research and Visualization System (ISERV) was a fully-automated prototype camera aboard the International Space Station that was tasked to capture high-resolution Earth imagery of specific locations at 3-7 frames per second. In the course of its regular operations during 2013 and 2014, ISERV's camera acquired images that can be used primaliry in use is environmental and disaster management.",
"managed_by": "[Radiant Earth Foundation](https://www.radiant.earth/)",
"update_frequency": "Not updated",
"tags": "aws-pds,geospatial,earth observation,satellite imagery,environmental",
"license": "The data is released under a ODC Public Domain Dedication & License 1.0 ([PDDL-1.0](https://spdx.org/licenses/PDDL-1.0.html)).",
"deprecated": false
},
{
"slug": "isic-archive",
"name": "International Skin Imaging Collaboration (ISIC) Archive",
"description": "A public-access archive of skin lesion images, supporting teaching, research, and the development and evaluation of diagnostic algorithms.",
"managed_by": "International Skin Imaging Collaboration (ISIC)",
"update_frequency": "Upon new data ingest from contributors.",
"tags": "biology,cancer,classification,computational pathology,dicom,grand-challenge.org,health,Homo sapiens,imaging,life sciences",
"license": "Creative Commons licenses (CC-0, CC-BY, or CC-BY-NC) are defined per-image.",
"deprecated": false
},
{
"slug": "its-live-data",
"name": "Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE)",
"description": "The Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project has a singular mission: to accelerate ice sheet and glacier research by producing globally comprehensive, high resolution, low latency, temporally dense, multi-sensor records of land ice and ice shelf change while minimizing barriers between the data and the user.\nITS_LIVE data currently consists of NetCDF Level 2 scene-pair ice flow products posted to a standard 120 m grid derived from Landsat 4/5/7/8/9, Sentine",
"managed_by": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)",
"update_frequency": "Up to daily, as new satellite imagery is made available.",
"tags": "aws-pds,ice,earth observation,satellite imagery,geophysics,geospatial,global,cog,netcdf,zarr",
"license": "[Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "janelia-cosem",
"name": "Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS",
"description": "High resolution images of subcellular structures.",
"managed_by": "[Janelia Research Campus](https://www.janelia.org/)",
"update_frequency": "New datasets and derived data are added as soon as they are available.",
"tags": "aws-pds,cell biology,computer vision,electron microscopy,life sciences,imaging,organelle",
"license": "CC-BY-4.0",
"deprecated": false
},
{
"slug": "janelia-flylight",
"name": "Fly Brain Anatomy: FlyLight Gen1 and Split-GAL4 Imagery",
"description": "This data set, made available by Janelia's FlyLight project, consists of fluorescence images \nof Drosophila melanogaster driver lines, aligned to standard templates, and stored in formats \nsuitable for rapid searching in the cloud. Additional data will be added as it is published. \n",
"managed_by": "[Janelia Research Campus](https://www.janelia.org)",
"update_frequency": "As required",
"tags": "aws-pds,biology,life sciences,imaging,neuroscience,neurobiology,neuroimaging,fluorescence imaging,microscopy,image processing",
"license": "[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "janelia-mouselight",
"name": "Mouse Brain Anatomy: MouseLight Imagery",
"description": "This data set, made available by Janelia's MouseLight project, consists of \nimages and neuron annotations of the Mus musculus brain, stored in formats suitable\nfor viewing and annotation using the HortaCloud cloud-based annotation system.\n",
"managed_by": "[Janelia Research Campus](https://www.janelia.org)",
"update_frequency": "As required",
"tags": "aws-pds,biology,life sciences,imaging,neuroscience,neurobiology,neuroimaging,fluorescence imaging,microscopy,image processing",
"license": "[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "japan_pointcloud",
"name": "Japan Prefectures, 3D Point Cloud Data",
"description": "This dataset comprises high-precision 3D point cloud data that covers all prefectures throughout Japan.\nThe data is produced through aerial laser surveys, airborne laser bathymetry, and mobile mapping systems, representing the culmination of many years of dedicated effort.\nThis data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures, and autonomous vehicle driving.\n",
"managed_by": "[AIGID](https://aigid.jp/)",
"update_frequency": "Currently not scheduled",
"tags": "aws-pds,disaster response,elevation,geospatial,japanese,land,lidar,mapping",
"license": "Creative Commons Attribution 4.0 International (CC-BY 4.0)",
"deprecated": false
},
{
"slug": "jaxa-alos-palsar2-scansar",
"name": "PALSAR-2 ScanSAR CARD4L (L2.2)",
"description": "The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km.\n The SAR imagery was ortho-rectificatied and slope corrected using the ALOS World 3D - 30 m (AW3D30) Digital Surface Model.\n Polarization data are stored as 16-bit digital numbers (DN).\n The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation:\n γ0 = 10*log10(DN2) - 83.0 dB\n CARD4L stands for CEOS Analysis Ready Dat",
"managed_by": "[JAXA](https://www.jaxa.jp/)",
"update_frequency": "Every month after 42 days observed",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,sustainability,disaster response,synthetic aperture radar,deafrica",
"license": "Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html).\n",
"deprecated": false
},
{
"slug": "jaxa-usgs-nasa-kaguya-tc-dtms",
"name": "JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Digital Terrain Models",
"description": "The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "The Kaguya/SELENE mission has completed. At least one update to this dataset is planned to address identified issues with the nominal swath width evening observations.",
"tags": "aws-pds,planetary,elevation,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "jaxa-usgs-nasa-kaguya-tc",
"name": "JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations",
"description": "The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "The Kaguya/SELENE mission has completed. No updates to this dataset are planned.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "jhu-indexes",
"name": "Cloud Indexes for Bowtie, Kraken, HISAT, and Centrifuge",
"description": "Genomic tools use reference databases as indexes to operate quickly and efficiently, analogous to how web search engines use indexes for fast querying. Here, we aggregate genomic, pan-genomic and metagenomic indexes for analysis of sequencing data.",
"managed_by": "Langmead Lab at Johns Hopkins University & Kim Lab at University of Texas Southwestern",
"update_frequency": "As new data becomes available; roughly quarterly",
"tags": "aws-pds,genomic,bioinformatics,biology,whole genome sequencing,medicine,reference index,mapping,life sciences",
"license": "Public Domain",
"deprecated": false
},
{
"slug": "kaiju-indexes",
"name": "Indexes for Kaiju",
"description": "This dataset comprises pre-built indexes for the bioinformatics software Kaiju, which is used for taxonomic classification of metagenomic sequencing data. Various indexes for different source reference databases are available.",
"managed_by": "[Peter Menzel](https://github.com/pmenzel)",
"update_frequency": "roughly yearly",
"tags": "aws-pds,bioinformatics,biology,genomic,life sciences,whole genome sequencing,reference index,metagenomics,microbiome",
"license": "Public Domain",
"deprecated": false
},
{
"slug": "kanagawa_pointcloud",
"name": "Kanagawa, 3D Point Cloud Data",
"description": "This dataset comprises high-precision 3D point cloud data that encompasses the entire Kanagawa prefecture in Japan.\nThe data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort.\nThis data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving.\n",
"managed_by": "[AIGID](https://aigid.jp/)",
"update_frequency": "Currently not scheduled",
"tags": "aws-pds,disaster response,elevation,geospatial,japanese,land,lidar,mapping",
"license": "Creative Commons Attribution 4.0 International (CC-BY 4.0)",
"deprecated": false
},
{
"slug": "kepler",
"name": "Kepler Mission Data",
"description": "The Kepler mission observed the brightness of more than 180,000 stars near the Cygnus constellation at a 30 minute cadence for 4 years in order to find transiting exoplanets, study variable stars, and find eclipsing binaries.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "astronomy,aws-pds",
"license": "Public domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "kids-first",
"name": "Gabriella Miller Kids First Pediatric Research Program (Kids First)",
"description": "The NIH Common Fund's Gabriella Miller Kids First Pediatric Research Program’s (“Kids First”) vision is to “alleviate suffering from childhood cancer and structural birth defects by fostering collaborative research to uncover the etiology of these diseases and by supporting data sharing within the pediatric research community.” The program continues to generate and share whole genome sequence data from thousands of children affected by these conditions, ranging from rare pediatric cancers, such ",
"managed_by": "[The Gabriella Miller Kids First Data Resource Center at the Children's Hospital of Philadelphia](https://kidsfirstdrc.org/)",
"update_frequency": "Data is updated on a rolling basis by the KFDRC to make data available as\nrapidly as possible under the NIH Genomic Data Sharing Policy. \n",
"tags": "aws-pds,life sciences,cancer,genetic,genomic,Homo sapiens,pediatric,structural birth defect,whole genome sequencing,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "kitti",
"name": "KITTI Vision Benchmark Suite",
"description": "Dataset and benchmarks for computer vision research in the context of autonomous driving. The dataset has been recorded in and around the city of Karlsruhe, Germany using the mobile platform AnnieWay (VW station wagon) which has been equipped with several RGB and monochrome cameras, a Velodyne HDL 64 laser scanner as well as an accurate RTK corrected GPS/IMU localization unit. The dataset has been created for computer vision and machine learning research on stereo, optical flow, visual odometry,",
"managed_by": "[Max Planck Campus Tübingen](http://tue.mpg.de/)",
"update_frequency": "Not updated",
"tags": "aws-pds,autonomous vehicles,computer vision,robotics,machine learning,deep learning",
"license": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/",
"deprecated": false
},
{
"slug": "klarna_productpage_dataset",
"name": "The Klarna Product-Page Dataset",
"description": "A collection of 51,701 product pages from 8175 e-commerce websites across 8 markets (US, GB, SE, NL, FI, NO, DE, AT) with 5 manually labelled elements, specifically, the product price, name and image, add-to-cart and go-to-cart buttons.\nThe dataset was collected between 2018 and 2019 and is made available has MHTML and as WebTraversalLibrary-format snapshots.\n",
"managed_by": "Web Automation Research, Klarna",
"update_frequency": "The dataset is not expected to update frequently.",
"tags": "aws-pds,internet,natural language processing,computer vision,commerce,deep learning,machine learning,information retrieval,graph",
"license": "CC BY-NC-SA",
"deprecated": true
},
{
"slug": "kraken2-ncbi-refseq-complete-v205",
"name": "Kraken2 NCBI RefSeq Complete V205 database on AWS",
"description": "Database for use with Kraken2 (taxonomic annotation of metagenomic sequencing reads) including all NCBI RefSeq genomes available in release V205",
"managed_by": "Robyn Wright",
"update_frequency": "This database is currently what was published in our 2023 paper comparing the performance of MetaPhlAn3 and Kraken2, but an updated database may be added in the future.",
"tags": "aws-pds,metagenomics,microbiome,benchmark,bioinformatics,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "kreppref",
"name": "Reference Indexes for krepp",
"description": "krepp is an alignment-free method for estimating distances and phylogenetic placement of individual reads to many thousands of reference genomes in a scalable manner using k-mers. This dataset includes k-mer-based indexes consisting of ultra-large reference genome sets that can be efficiently analyzed using krepp.",
"managed_by": "Mirarab Lab at UC San Diego",
"update_frequency": "Quarterly or as new data becomes available",
"tags": "bioinformatics,metagenomics,microbiome,reference index,aws-pds,life sciences",
"license": "GPL-3.0 license. Use of the data should be cited in the usual way, following https://github.com/bo1929/krepp/tree/master?tab=readme-ov-file#citation.",
"deprecated": false
},
{
"slug": "kyfromabove",
"name": "KyFromAbove on AWS",
"description": "The KyFromAbove initiative is focused on building and maintaining a current basemap for Kentucky that can meet the needs of its users at the state, federal, local, and regional level. A common basemap, including current color leaf-off aerial photography and elevation data (LiDAR), reduces the cost of developing GIS applications, promotes data sharing, and add efficiencies to many business processes. All basemap data acquired through this effort is being made available in the public domain. KyFro",
"managed_by": "[Kentucky Division of Geographic Information](https://kygeonet.ky.gov)",
"update_frequency": "KyFromAbove data are typically updated on an annual basis. Each year, a portion of the state is acquired with an overall update cycle of every three to four years. This update cadance is determined by both funding and the length of leaf-off conditions in a given year. This catalog currently includes imagery and LiDAR data from 2010 through 2024 for most products.",
"tags": "aerial imagery,aws-pds,cog,dtm,disaster response,earth observation,elevation,geopackage,geospatial,lidar",
"license": "Public Domain with Attribution\n",
"deprecated": false
},
{
"slug": "lab41-sri-voices",
"name": "Voices Obscured in Complex Environmental Settings (VOiCES)",
"description": "VOiCES is a speech corpus recorded in acoustically challenging settings,\nusing distant microphone recording. Speech was recorded in real rooms with various\nacoustic features (reverb, echo, HVAC systems, outside noise, etc.). Adversarial noise,\neither television, music, or babble, was concurrently played with clean speech.\nData was recorded using multiple microphones strategically placed\nthroughout the room. The corpus includes audio recordings, orthographic transcriptions,\nand speaker labels.\n",
"managed_by": "[In-Q-Tel](https://www.iqt.org/)",
"update_frequency": "Data from two additional rooms will be added to the corpus Fall 2018.",
"tags": "aws-pds,machine learning,automatic speech recognition,speaker identification,denoising,speech processing",
"license": "Creative Commons BY 4.0 (see [here](htpps://voices.lab41.org) for more details)",
"deprecated": false
},
{
"slug": "ladi",
"name": "Low Altitude Disaster Imagery (LADI) Dataset",
"description": "The Low Altitude Disaster Imagery (LADI) Dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2023. Two key distinctions are the low altitude, oblique perspective of the imagery and disaster-related features, which are rarely featured in computer vision benchmarks and datasets.",
"managed_by": "[MIT Lincoln Laboratory Humanitarian Assistance and Disaster Relief group](https://www.ll.mit.edu/r-d/biotechnology-and-human-systems/humanitarian-assistance-and-disaster-relief-systems)",
"update_frequency": "Periodically",
"tags": "aws-pds,aerial imagery,coastal,computer vision,disaster response,earth observation,earthquakes,geospatial,imaging,image processing",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "landsat-8",
"name": "Landsat 8",
"description": "An ongoing collection of satellite imagery of all land on Earth produced by\nthe Landsat 8 satellite.\n",
"managed_by": "[Planet](https://www.planet.com/)",
"update_frequency": "New Landsat 8 scenes are added regularly as soon as they are available.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response",
"license": "There are no restrictions on the use of data received from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center or NASA's Land Processes Distributed Active Archive Center (LP DAAC), unless expressly identified prior to or at the time of receipt. More information on lice",
"deprecated": true
},
{
"slug": "lei",
"name": "Legal Entity Identifier (LEI) and Legal Entity Reference Data (LE-RD)",
"description": "The Legal Entity Identifier (LEI) is a 20-character, alpha-numeric code based on the ISO 17442 standard developed by the International Organization for Standardization (ISO). It connects to key reference information that enables clear and unique identification of legal entities participating in financial transactions. Each LEI contains information about an entity’s ownership structure and thus answers the questions of 'who is who’ and ‘who owns whom’. Simply put, the publicly available LEI data ",
"managed_by": "[GLEIF](http://www.gleif.org/)",
"update_frequency": "Three times daily (about every 8 hours)",
"tags": "analytics,aws-pds,blockchain,climate,commerce,copyright monitoring,csv,financial markets,governance,government spending",
"license": "Creative Commons (CC0) license",
"deprecated": false
},
{
"slug": "leopard",
"name": "LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge (LEOPARD) Dataset",
"description": "\"This dataset contains the all data for the [LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge or LEOPARD](https://leopard.grand-challenge.org/).Prostate cancer, impacting 1.4 million men annually, is a prevalent malignancy ([H. Sung et al., 2021](https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21660)). A substantial number of these individuals undergo prostatectomy as the primary curative treatment. The efficacy of this surgery is assessed, in par",
"managed_by": "Radboud University Medical Center",
"update_frequency": "As required",
"tags": "aws-pds,life sciences,cancer,computational pathology,grand-challenge.org,histopathology,deep learning,computer vision",
"license": "CC BY NC SA",
"deprecated": false
},
{
"slug": "lgnd-clay-v1-5-sentinel2",
"name": "LGND Clay v1.5 Sentinel-2",
"description": "A global dataset of Clay v1.5 embeddings for Sentinel2.",
"managed_by": "[Source Cooperative](https://source.coop/)",
"update_frequency": "As new data versions become available",
"tags": "aws-pds,satellite imagery,earth observation,machine learning,imaging,computer vision",
"license": "Creative Commons Attribution 4.0 International License",
"deprecated": false
},
{
"slug": "loc-sanborn-maps",
"name": "Sanborn Maps Data Package",
"description": "The dataset contains metadata records for 50,600 maps from the [Sanborn Fire Insurance Maps collection](https://www.loc.gov/collections/sanborn-maps/) and their corresponding 440,048 JPEG images. The Sanborn collection at Library of Congress includes over fifty thousand editions of fire insurance maps comprising almost seven hundred thousand individual sheets. The Library of Congress holdings represent the largest extant collection of maps produced by the Sanborn Map Company.",
"managed_by": "[Library of Congress](https://www.loc.gov/)",
"update_frequency": "As new and significant changes to the underlying digital collection occurs",
"tags": "aws-pds,archives,cities,computer vision,conservation,culture,cultural preservation,demographics,digital assets,geospatial",
"license": "The content of the Library of Congress online Sanborn Maps Collection is in the public domain and is free to use and reuse. For more information, see https://www.loc.gov/collections/sanborn-maps/about-this-collection/rights-and-access/.",
"deprecated": false
},
{
"slug": "lofar-elais-n1",
"name": "LOFAR ELAIS-N1 cycle 2 observations on AWS",
"description": "These data correspond to the [International LOFAR Telescope](http://www.lofar.org/) observations of the sky field ELAIS-N1 (16:10:01 +54:30:36) during the cycle 2 of observations. There are 11 runs of about 8 hours each plus the corresponding observation of the calibration targets before and after the target field. The data are measurement sets ([MS](https://casa.nrao.edu/Memos/229.html)) containing the cross-correlated data and metadata divided in 371 frequency sub-bands per target centred at ~",
"managed_by": "Institute for Astronomy, University of Edinburgh",
"update_frequency": "Not updated",
"tags": "astronomy,aws-pds,survey,imaging",
"license": "The data are considered \"LOFAR data in the public domain\" and their use must adhere to the [LOFAR data policy](https://www.astron.nl/radio-observatory/observing-proposals/lofar-data-policy/lofar-data-policy). Any publication based on these data must contain the following acknowledgement text: \"This ",
"deprecated": false
},
{
"slug": "longbench",
"name": "LongBench - cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches",
"description": "LongBench is a comprehensive benchmark dataset of the latest long-read transcriptomics technologies from Oxford Nanopore (ON) and Pacific Biosciences, alongside a comparison with next-generation sequencing from Illumina. We generated bulk and single-cell libraries from lung cancer cell lines which include different cancer subtypes to capture real biological variation. To further compare and assess sequencing platform performance, Sequins and SIRVs (Set 4) synthetic spike-ins have been included.\n",
"managed_by": "Richie Lab, Walter and Eliza Hall Institute of Medical Research",
"update_frequency": "New data will be added as soon as they are available.",
"tags": "benchmark,long read sequencing,single-cell transcriptomics,short read sequencing,bioinformatics,fastq,bam,vcf,cancer,life sciences",
"license": "CC BY-4.0",
"deprecated": false
},
{
"slug": "lowcontext-ner-gaz",
"name": "Low Context Name Entity Recognition (NER) Datasets with Gazetteer",
"description": "See https://lowcontext-ner-gaz.s3.amazonaws.com/readme.html",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "N/A",
"tags": "amazon.science,natural language processing",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "ltrf-cqa-dataset",
"name": "Learning to Rank and Filter - community question answering",
"description": "This dataset provides product related questions and answers, including answers' quality labels, as as part of the paper 'IR Evaluation and Learning in the Presence of Forbidden Documents'.",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated.",
"tags": "amazon.science,natural language processing,machine learning",
"license": "[CDLA-Permissive](https://cdla.dev/permissive-1-0/)",
"deprecated": false
},
{
"slug": "luad-eagle",
"name": "Integrative Analysis of Lung Adenocarcinoma in Environment and Genetics Lung cancer Etiology (Phase 2)",
"description": "We performed whole genome sequencing and whole exome sequencing of 31 lung adenocarcinoma (LUAD) samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. The EAGLE study is made available on AWS via the NIH STRIDES Initiative (https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases, although this dataset may not be updated at every release.\n",
"tags": "cancer,whole exome sequencing,whole genome sequencing,aws-pds,life sciences,STRIDES,genomic,epigenomics",
"license": "(NIH Genomic Data Sharing Policy)[https://gdc.cancer.gov/access-data/data-access-policies]",
"deprecated": false
},
{
"slug": "lwi-model-data",
"name": "Louisiana Watershed Initiative (LWI) Model Data",
"description": "Geographic (land cover, land elevation, etc.), meteorologic (pluvial, wind, etc.), hydrologic (fluvial, tidal, etc.), hydrodynamic (water surface elevations, flow velocities), and built environment (structures, levees, floodgates, culverts) data used as inputs to and outputs from numerical modeling software for the prediction of flood risk in stochastic and probabilistic frameworks. This data was collected from open sources, such as from the National Oceanographic and Atmospheric Administrati",
"managed_by": "The Water Institute",
"update_frequency": "yearly",
"tags": "forecast,bathymetry,climate,coastal,disaster response,elevation,floods,geospatial,hydrologic model,hydrology",
"license": "https://creativecommons.org/licenses/by/4.0/ with attribution to Louisiana Watershed Council",
"deprecated": false
},
{
"slug": "m3ed",
"name": "Multi-robot, Multi-Sensor, Multi-Environment Event Dataset (M3ED)",
"description": "M3ED is the first multi-sensor event camera (EC) dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized data from multiple platforms (car, legged robot, UAV), operating in challenging conditions such as off-road trails, dense forests, and performing aggressive flight maneuvers. M3ED also covers demanding operational scenarios for EC, such as high egomotion and multiple independently moving objects. M3ED includes high-resolution stereo EC (",
"managed_by": "[Daniilidis Group](https://www.grasp.upenn.edu/people/kostas-daniilidis/), [KumarRobotics](https://www.kumarrobotics.org/)",
"update_frequency": "The dataset will be uploaded sporadically, when bugs are found and new features are implemented (see [updates](https://m3ed.io/#updates)).",
"tags": "aws-pds,autonomous vehicles,computer vision,deep learning,event camera,global shutter camera,GNSS,GPS,h5,hdf5",
"license": "Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)",
"deprecated": false
},
{
"slug": "maf-genome",
"name": "Golden Retriever Lifetime Study: Whole genome genotyping of Golden Retrievers on Axiom HD Arrays",
"description": "Morris Animal Foundation’s Golden Retriever Lifetime Study is a longitudinal, prospective study following 3044 golden retrievers. The Study’s purpose is to identify the nutritional, environmental, lifestyle and genetic risk factors for cancer and other diseases. The Golden Oldie’s study enrolled an additional cohort of golden retrievers that had reached the age of 12 years or older and had not yet been diagnosed with a malignant cancer. This population can be used as a control group for conditio",
"managed_by": "[Morris Animal Foundation](https://www.morrisanimalfoundation.org/)",
"update_frequency": "Static",
"tags": "aws-pds,genome,genotyping,golden retriever lifetime study,morris animal foundation,life sciences",
"license": "CC-BY-SA",
"deprecated": false
},
{
"slug": "man-truckscenes",
"name": "MAN TruckScenes",
"description": "A large scale multimodal dataset for Autonomous Trucking. Sensor data was recorded with a heavy truck from MAN equipped with 6 lidars, 6 radars, 4 cameras and a high-precision GNSS. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time.\nIt comprises more than 740 scenes of 20s each within a multitude of different environmental conditions. Bounding boxes",
"managed_by": "[MAN Truck and Bus SE](https://www.man.eu)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "autonomous vehicles,radar,lidar,IMU,GPS,computer vision,machine learning,deep learning,perception,object detection",
"license": "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)",
"deprecated": false
},
{
"slug": "mapping-africa",
"name": "High resolution, annual cropland and landcover maps for selected African countries",
"description": "High resolution, annual cropland and landcover maps for selected African countries developed by [Clark University](https://clarku.edu)'s [Agricultural Impacts Research Group](https://agroimpacts.info/) using various machine learning approaches applied to Planet imagery, including field boundary and cultivated frequency maps, as well as multi-class land cover.\n",
"managed_by": "[The Agricultural Impacts Research Group](https://agroimpacts.info/)",
"update_frequency": "New maps are added as developed",
"tags": "aws-pds,agriculture,land cover,satellite imagery,machine learning,deep learning,cog,labeled",
"license": "[Planet NICFI participant license agreement](https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf)",
"deprecated": false
},
{
"slug": "marine-energy-data",
"name": "Department of Energy's Marine Energy Data Lake",
"description": "Data released from projects funded by the Department of Energy's Water Power Technologies Office (DOE WPTO) \nthat are too large or complex to be conveniently accessed by traditional means. The Marine Energy data lake \naims to improve and automate access of high-value MHK data sets, making data actionable and discoverable by \nresearchers and industry to accelerate analysis and advance innovation. \nThis data lake is a sister-data lake to the Department of Energy’s Open Energy Data Initiative (OEDI",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,energy,marine,water",
"license": "Creative Commons Attribution 4.0 United States License",
"deprecated": false
},
{
"slug": "mast-gaia-dr3",
"name": "Gaia DR3",
"description": "[Gaia DR3 data](https://www.cosmos.esa.int/web/gaia/dr3) were originally released by the European Space Agency in December 2020. This [HATS](https://hats.readthedocs.io/en/stable)-formatted catalog was produced by the LSST Interdisciplinary Network for Collaboration and Computing. The GAIA HATS Datasets are specifically designed for efficient spatial cross-matching with other HATS-format catalogs, whether within the same archive or across distributed archive data centers. This enables astronomer",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "astronomy",
"license": "Attribution required.",
"deprecated": false
},
{
"slug": "mast-galex",
"name": "Galaxy Evolution Explorer Satellite (GALEX)",
"description": "The Galaxy Evolution Explorer Satellite (GALEX) was a NASA mission led by the California Institute of Technology, whose primary goal was to investigate how star formation in galaxies evolved from the early universe up to the present. GALEX used microchannel plate detectors to obtain direct images in the near-UV (NUV) and far-UV (FUV), and a grism to disperse light for low resolution spectroscopy.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "astronomy,aws-pds",
"license": "Public Domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "mast-hst",
"name": "Hubble Space Telescope",
"description": "The Hubble Space Telescope (HST) is one of the most productive scientific instruments ever created. This dataset contains calibrated and raw data for all currently active instruments on HST: ACS, COS, STIS, WFC3, and FGS.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Hourly",
"tags": "astronomy,aws-pds",
"license": "Public domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "mast-jades",
"name": "The JWST Advanced Extragalactic Survey JADES",
"description": "JADES is an infrared imaging and multi-object spectroscopy survey focused on two deep fields: the Hubble Deep Field (GOODS-N) \nand Hubble Ultra Deep Field (GOODS-S). JADES conducted NIRCam imaging in 8-10 bands, covering about 42 square arcminutes to very \ndeep limits (fainter than 30th magnitude) with an average of about 100 hours of total exposure time, and then another 167 square arcminutes\nto a typical exposure time of 25 hrs. Coordinated parallels with the MIRI instrument extend this imagin",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "aws-pds,astronomy",
"license": "All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).",
"deprecated": false
},
{
"slug": "mast-jwst",
"name": "James Webb Space Telescope (JWST)",
"description": "The James Webb Space Telescope (JWST) is the world's next flagship infrared observatory led by NASA with its partners, ESA (European Space Agency), and CSA (Canadian Space Agency). JWST offers scientists the opportunity to observe galaxy evolution, the formation of stars and planets, exoplanetary systems, and our own solar system, in ways never before possible.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Hourly",
"tags": "astronomy,aws-pds",
"license": "Public domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "mast-k2",
"name": "K2 Mission Data",
"description": "The K2 mission observed 100 square degrees for 80 days each across 20 different pointings along the ecliptic, collecting high-precision photometry for a selection of targets within each field. The mission began when the original Kepler mission ended due to loss of the second reaction wheel in 2013.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "astronomy,aws-pds",
"license": "Public domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "mast-kepler",
"name": "Kepler Mission Data",
"description": "The Kepler mission observed the brightness of more than 180,000 stars near the Cygnus constellation at a 30 minute cadence for 4 years in order to find transiting exoplanets, study variable stars, and find eclipsing binaries.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "astronomy,aws-pds",
"license": "Public domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "mast-panstarrs",
"name": "Pan-STARRS PS1 Survey",
"description": "The PS1 surveys used a 1.8 meter telescope and its 1.4 Gigapixel camera to image the sky in five broadband filters. The largest of these surveys provides coverage of the entire sky north of -30 degrees declination, with approximately 10 observation epochs across 3 years in each filter.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Never",
"tags": "aws-pds,astronomy",
"license": "STScI hereby grants the non-exclusive, royalty-free, non-transferable, worldwide right and license to use, reproduce, and publicly display in all media data from the PS1 surveys.",
"deprecated": false
},
{
"slug": "mast-tess-spoc",
"name": "TESS-SPOC",
"description": "The data products for the TESS-SPOC FFI targets are the same as for the [TESS](https://archive.stsci.edu/missions-and-data/tess)\ntwo-minute cadence targets: calibrated target pixel files, simple aperture photometry (SAP) flux time series, presearch data conditioning\ncorrected (PDCSAP) flux time series, and cotrending basis vectors (CBV). Since TESS-SPOC relies on FFIs, data are sampled at the FFI cadence.\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Monthly",
"tags": "aws-pds,astronomy",
"license": "All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).",
"deprecated": false
},
{
"slug": "mast-tess",
"name": "Transiting Exoplanet Survey Satellite (TESS)",
"description": "The Transiting Exoplanet Survey Satellite (TESS) is a multi-year survey that has discovered exoplanets in orbit around bright stars across the entire sky using high-precision photometry. The survey also enables a wide variety of stellar astrophysics, solar system science, and extragalactic variability studies. More information about TESS is available at [MAST](https://archive.stsci.edu/missions-and-data/tess) and the [TESS Science Support Center](https://heasarc.gsfc.nasa.gov/docs/tess/).\n",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Monthly",
"tags": "astronomy,aws-pds",
"license": "Public domain. Attribution required for refereed scientific papers.",
"deprecated": false
},
{
"slug": "mast-tglc",
"name": "TESS-GAIA Light Curve (TGLC)",
"description": "TESS-Gaia Light Curve (TGLC) is a PSF-based TESS full-frame image (FFI) light curve product. Using Gaia DR3 as priors, the team forward models the FFIs with the effective point spread function to remove contamination from nearby stars. The resulting light curves show a photometric precision closely tracking the pre-launch prediction of the noise level: TGLC's photometric precision consistently reaches ≲2% at 16th TESS magnitude even in crowded fields, demonstrating excellent decontamination and ",
"managed_by": "[Space Telescope Science Institute](http://www.stsci.edu/)",
"update_frequency": "Monthly",
"tags": "astronomy,aws-pds",
"license": "All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/).",
"deprecated": false
},
{
"slug": "materials-project",
"name": "Materials Project Data",
"description": "Materials Project is an open database of computed materials properties aiming to accelerate materials science research. The resources in this OpenData dataset contain the raw, parsed, and build data products.",
"managed_by": "[Materials Project](https://materialsproject.org)",
"update_frequency": "New versions and objects added as we continuously calculate, parse and build new materials and their properties.",
"tags": "aws-pds,chemistry,cloud computing,data assimilation,digital assets,digital preservation,energy,environmental,free software,genome",
"license": "[Materials Project Terms of Use](https://materialsproject.org/about/terms)",
"deprecated": false
},
{
"slug": "maxar-open-data",
"name": "Maxar Open Data Program",
"description": "Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, \nmonitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated\nusing the [Maxar ARD](https://ard.maxar.com/docs) pipeline, tiled on an organized grid in analysis-ready\ncloud-optimized formats.\n",
"managed_by": "[Maxar](https://www.maxar.com/)",
"update_frequency": "New data is released in response to activations. Older data may be migrated to the ARD format as needed.",
"tags": "aws-pds,earth observation,disaster response,geospatial,satellite imagery,cog,stac",
"license": "Creative Commons Attribution Non Commercial 4.0",
"deprecated": false
},
{
"slug": "mbers-open-data",
"name": "Marginal Build Emissions Rates (MBERs) for Electricity",
"description": "The Climate TRACE coalition has developed and maintains free global hourly Build Margin data, also known as MBERs, that are compliant with the Greenhouse Gas Protocol's Project Protocol electricity sector guidance, Guidelines for Grid-Connected Electricity Projects (\"GHGP Guidelines\").",
"managed_by": "Climate TRACE",
"update_frequency": "Annually",
"tags": "aws-pds,carbon,climate,csv,electricity,energy,energy modeling,environmental",
"license": "All data are free and provided without license restrictions.",
"deprecated": false
},
{
"slug": "mcrpc",
"name": "Genomic Characterization of Metastatic Castration Resistant Prostate Cancer",
"description": "Biopsies of castration resistant prostate cancer metastases were subjected to whole genome sequencing (WGS), along with RNA-sequencing (RNA-Seq). The overarching goal of the study is to illuminate molecular mechanisms of acquired resistance to therapeutic agents, and particularly androgen signaling inhibitors, in the treatment of metastatic castration resistant prostate cancer (mCRPC). This study is made available on AWS via the NIH STRIDES Initiative.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases, although this dataset may not be updated at every release.\n",
"tags": "aws-pds,life sciences,cancer,genomic,whole genome sequencing,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "megascenes",
"name": "MegaScenes",
"description": "The MegaScenes Dataset is an extensive collection of around 430k scenes, featuring over 100k structure-from-motion reconstructions and over 2 million registered images. MegaScenes includes a diverse array of scenes, such as minarets, building interiors, statues, bridges, towers, religious buildings, and natural landscapes. The images of these scenes are captured under varying conditions, including different times of day, various weather and illumination, and from different devices with distinct ",
"managed_by": "Cornell University (https://www.cs.cornell.edu/~snavely/)",
"update_frequency": "The dataset will be updated periodically.",
"tags": "internet,benchmark,computer vision,deep learning",
"license": "Creative Commons Attribution 4.0 International License. The photos have their own licenses.",
"deprecated": false
},
{
"slug": "met-office-global-deterministic",
"name": "Met Office Global Deterministic 10km on a 2-year rolling archive",
"description": "THIS DATASET IS CHANGING
\n\nFiles uploaded from late January 2026 onward will contain changes including:\n- precision changes\n- new parameters\n- changes to existing parameters e.g. adding vertical levels and timesteps\n- the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels
\n\nPlease check your systems are prepared for these changes.
\n\nA numerical weather prediction forecast for the whole globe, with a resolution of approximately ",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).
\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.
\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n",
"tags": "aws-pds,air temperature,atmosphere,forecast,geoscience,geospatial,model,near-surface air temperature,near-surface relative humidity,netcdf",
"license": "British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-global-ensemble",
"name": "Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive",
"description": "THIS DATASET IS CHANGING
\n\nFiles uploaded from late January 2026 onward will contain changes including:\n- precision changes\n- new parameters\n- changes to existing parameters e.g. adding vertical levels and timesteps\n- the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels
\n\nPlease check your systems are prepared for these changes.
\n\nA numerical weather prediction model that produces forecasts for the whole globe up to a week ah",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The MOGREPS-G model runs four times per day at 00:00, 06:00, 12:00 and 18:00 UTC.",
"tags": "aws-pds,air temperature,atmosphere,forecast,geoscience,geospatial,global,meteorological,model,near-surface air temperature",
"license": "British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-global-ocean",
"name": "Met Office Global Ocean model on a 2-year rolling archive",
"description": "The Global Ocean component of the Met Office Global Coupled Atmosphere-Land-Ocean-Ice system which has been running in operations since May 2022. The system provides a global physical analysis and coupled forecast products providing 3D daily mean fields of temperature and salinity, zonal and meridional velocities; 2D daily mean fields of sea surface height, bottom temperature, mixed layer depth, sea ice fraction, sea ice thickness and sea ice zonal and meridional velocities; and instantaneous ho",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The Global Ocean model runs once daily. T+00h is the midnight prior to production, the following are produced. T-48h to T-24h best estimate; T-24h to T+00h analyses; 7 day forecast (from T+00h).",
"tags": "aws-pds,forecast,geoscience,geospatial,global,marine,model,netcdf,ocean sea surface height,oceans",
"license": "British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-global-wave",
"name": "Met Office Global Wave model on a 2-year rolling archive",
"description": "The Met Office runs global wave forecast models to support marine safety and operational decision making. Met Office configurations are developed to be run using the community wave model WAVEWATCH IIITM. The global wave configuration is designed to generate accurate forecasts for open waters of the world’s oceans and larger seas. The Met Office wave models are forced using wind data from the Met Office Global Atmospheric Hi-Res Model. The global wave model is run to provide a five day outlook fo",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The Global Wave model runs at 00:00, 12:00 (UTC) to T+144h, and at 06:00, 18:00 (UTC) to T+66h.",
"tags": "aws-pds,forecast,geoscience,geospatial,global,marine,model,netcdf,ocean sea surface height,oceans",
"license": "British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-nws-ocean",
"name": "Met Office NWS Ocean model on a 2-year rolling archive",
"description": "The Northwest European continental shelf physical ocean model predicts temperature, salinity and circulation for waters surrounding the UK. \nOcean physics analysis provides a 6-day forecast for the North-West European Atlantic shelf at 1.5km resolution:
\n - 33 depth levels
\n - Currents
\n - Salinity
\n - Temperature
\n - Mixing Layer Thickness\n",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The NWS Ocean model runs once daily.",
"tags": "aws-pds,forecast,geoscience,geospatial,marine,model,netcdf,ocean sea surface height,oceans,weather",
"license": "British Crown copyright 2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-nws-wave",
"name": "Met Office NWS Wave model on a 2-year rolling archive",
"description": "Northwest European continental shelf regional wave model predicting sea-state and various sea and swell wave characteristics for waters surrounding the UK.\n\nThe Met Office runs global and regional wave forecast models to support marine safety and operational decision making.\nMet Office configurations are developed to be run using the community wave model WAVEWATCH IIITM.\nThe global wave configuration is designed to generate accurate forecasts for open waters of the world's oceans and larger seas",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The model runs at 00:00, 06:00, 12:00, 18:00 (UTC) from T-48h to T+143h",
"tags": "aws-pds,forecast,geoscience,geospatial,marine,model,netcdf,ocean sea surface height,oceans,weather",
"license": "British Crown copyright 2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-uk-deterministic",
"name": "Met Office UK Deterministic (UKV)2km on a 2-year rolling archive",
"description": "THIS DATASET IS CHANGING
\n\nFiles uploaded from late January 2026 onward will contain changes including:\n- precision changes\n- new parameters\n- changes to existing parameters e.g. adding vertical levels and timesteps\n- the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels
\n\nPlease check your systems are prepared for these changes.
\n\nA high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, proje",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "There are three lengths of model run, each with its own update frequency:\n- Nowcast: forecasts the next 12 hours and runs at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC.\n- Short: forecasts the next 54 hours and runs at 0000, 0600, 0900, 1200, 1800 and 2100 UTC.\n- Medium: forecasts the next 120 hours and runs at 0300 and 1500 UTC.\n",
"tags": "aws-pds,air temperature,atmosphere,forecast,geoscience,geospatial,model,near-surface air temperature,near-surface relative humidity,netcdf",
"license": "British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-uk-ensemble",
"name": "Met Office Global and Regional Ensemble Prediction System - UK (MOGREPS-UK) on a 30-day rolling archive",
"description": "THIS DATASET IS CHANGING
\n\nFiles uploaded from late January 2026 onward will contain changes including:\n- precision changes\n- new parameters\n- changes to existing parameters e.g. adding vertical levels and timesteps\n- the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels
\n\nPlease check your systems are prepared for these changes.
\n\nA numerical weather prediction model that produces forecasts for the UK for the next 5 days. Par",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "The MOGREPS-UK model runs 24 times per day.",
"tags": "aws-pds,air temperature,atmosphere,forecast,geoscience,geospatial,global,meteorological,model,near-surface air temperature",
"license": "British Crown copyright 2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-uk-land-observations",
"name": "Met Office UK Land Surface Observations",
"description": "Land surface weather observations for 31 parameters from over 250 locations across the Met Office UK land observation network. The data is available as CSV files. You can use it to monitor the latest weather affecting a specific location so you can plan for your business or operations.\n
\nThe observations are produced every minute and transmitted to the Amazon Registry of Open Data every hour. They’re available for a rolling 7-day period (168 hours).\n
\nAll locations in the observation n",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "60 minutes of new data for all locations is ingested into the Amazon Registry of Open Data every hour. For example, at 12:00 new data will arrive for observations collected between 10:00-1100. This is due to the quality checks required to verify data for public consumption.",
"tags": "aws-pds,atmosphere,geospatial,csv,precipitation,weather",
"license": "British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-uk-marine-observations",
"name": "Met Office UK Marine Observations",
"description": "Marine surface weather observations for 32 parameters from 69 locations across the Met Office marine observation network. Observations are available for a rolling 7-day period (168 hours). The data is available as CSV files.
\nThe data comes from moored buoys, light vessels and ships with automatic weather stations onboard. Buoys and light vessels are static and you can view their locations on the [Met Office Marine Observations page](https://weather.metoffice.gov.uk/specialist-forecasts/c",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "Every hour, at 15 minutes past the hour, we retrieve a single set of parameter values for each location. For example, at 12:15 new data is retrieved for observations collected between 11:00 and 12:00. This is due to the quality checks required to verify data for public consumption and global network latency.",
"tags": "aws-pds,atmosphere,geospatial,csv,precipitation,weather",
"license": "British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-uk-radar-observations",
"name": "Met Office UK Radar Observations on a 2-year rolling archive",
"description": "The United Kingdom Composite, Surface Rain Rate Estimate is an international radar composite produced by Met Office (UK). This is a composite, radar reflectivity derived, surface rain rate estimate product in HDF5 code from stations covering the United Kingdom.",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "Four images per hour (every 15 minutes). The data is available within 20 minutes of the validity time of the product.",
"tags": "aws-pds,atmosphere,geospatial,h5,hdf5,precipitation,radar,weather",
"license": "British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
"deprecated": false
},
{
"slug": "met-office-ukesm1-arise",
"name": "Met Office UK Earth System Model (UKESM1) ARISE-SAI geoengineering experiment data",
"description": "Data from the UK Earth System Model (UKESM1) ARISE-SAI experiment. The UKESM1 ARISE-SAI experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere in order to keep global mean surface air temperature near 1.5 C above the pre-industrial climate. Data includes a five member ensemble of simulations with SO2 injection plus a five member ensemble of SSP2-4.5 simulations from CMIP6 to serve as a reference data set",
"managed_by": "[Met Office](https://www.metoffice.gov.uk)",
"update_frequency": "Rare once complete",
"tags": "climate,model,climate model,atmosphere,oceans,land,ice,geospatial,aws-pds,sustainability",
"license": "CMIP6 data included is licensed under CC-BY 4.0 (see [here](https://wcrp-cmip.github.io/CMIP6_CVs/docs/CMIP6_source_id_licenses.html). Additional ARISE-SAI data is licenced under the [Open Government Licence v3](https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/)",
"deprecated": false
},
{
"slug": "metagraph",
"name": "MetaGraph Sequence Indexes",
"description": "The MetaGraph Sequence Indexes dataset comprises full-text searchable index files for raw sequencing data hosted in major public repositories. These include the European Nucleotide Archive (ENA) managed by the European Bioinformatics Institute (EMBL-EBI), the Sequence Read Archive (SRA) maintained by the National Center for Biotechnology Information (NCBI), and the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA).\n\nAll index files can be used with the MetaGraph framework for sequence se",
"managed_by": "[Biomedical Informatics Lab, ETH Zurich, Switzerland](https://bmi.inf.ethz.ch)",
"update_frequency": "Continuously as new sequencing data becomes available.",
"tags": "biodiversity,bioinformatics,biology,analysis ready data,fasta,genome,genomic,graph,information retrieval,life sciences",
"license": "[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)",
"deprecated": false
},
{
"slug": "meteo-france-models",
"name": "Atmospheric Models from Météo-France",
"description": "Global and high-resolution regional atmospheric models from Météo-France.\n\n- ARPEGE World covers the entire world at a base horizontal resolution of 0.5° (~55km) between grid points, it predicts weather out up to 114 hours in the future.\n\n- ARPEGE Europe covers Europe and North-Africa at a base horizontal resolution of 0.1° (~11km) between grid points, it predicts weather out up to 114 hours in the future.\n\n- AROME France covers France at a base horizontal resolution of 0.025° (~2.5km) between g",
"managed_by": "[OpenMeteoData](https://openmeteodata.com)",
"update_frequency": "Every 6 hours",
"tags": "aws-pds,agriculture,climate,disaster response,earth observation,environmental,meteorological,model,weather",
"license": "https://mf-models-on-aws.org/en/doc/license",
"deprecated": false
},
{
"slug": "mevadata",
"name": "Multiview Extended Video with Activities (MEVA)",
"description": "The Multiview Extended Video with Activities (MEVA) dataset consists\nvideo data of human activity, both scripted and unscripted,\ncollected with roughly 100 actors over several weeks. The data was\ncollected with 29 cameras with overlapping and non-overlapping\nfields of view. The current release consists of about 328 hours\n(516GB, 4259 clips) of video data, as well as 4.6 hours (26GB) of\nUAV data. Other data includes GPS tracks of actors, camera models,\nand a site map. We have also released annot",
"managed_by": "[Kitware](http://www.kitware.com/)",
"update_frequency": "We anticipate two or three updates as more\nvideo data is released to the public.\n",
"tags": "aws-pds,computer vision,urban,us,video",
"license": "http://mevadata.org/resources/MEVA-data-license.txt",
"deprecated": false
},
{
"slug": "mimic-iv-demo",
"name": "MIMIC-IV Clinical Database Demo",
"description": "The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops&n",
"managed_by": "[PhysioNet](https://physionet.org/)",
"update_frequency": "Not updated",
"tags": "aws-pds",
"license": "Open Data Commons Open Database License v1.0",
"deprecated": false
},
{
"slug": "mimic-iv-ecg",
"name": "MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset",
"description": "The MIMIC-IV-ECG module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. These diagnostic ECGs use 12 leads and are 10 seconds in length. They are sampled at 500 Hz. This subset contains all of the ECGs for patients who appear in the MIMIC-IV Clinical Database. When a cardiologist report is available for a given ECG, we provide the needed information to link the waveform to the report. The patients in MIMIC-IV-ECG have been matched against the M",
"managed_by": "[PhysioNet](https://physionet.org/)",
"update_frequency": "Not updated",
"tags": "aws-pds",
"license": "Open Data Commons Open Database License v1.0",
"deprecated": false
},
{
"slug": "mimiciii",
"name": "MIMIC-III (‘Medical Information Mart for Intensive Care’)",
"description": "MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, \nsingle-center database comprising information relating to patients \nadmitted to critical care units at a large tertiary care hospital. \nData includes vital signs, medications, laboratory measurements, \nobservations and notes charted by care providers, fluid balance, \nprocedure codes, diagnostic codes, imaging reports, hospital length \nof stay, survival data, and more. The database supports applications \nincluding academic and",
"managed_by": "[MIT Laboratory for Computational Physiology](https://lcp.mit.edu/)",
"update_frequency": "Not updated",
"tags": "aws-pds,bioinformatics,health,life sciences,natural language processing,us",
"license": "https://physionet.org/content/mimiciii/view-license/1.4/",
"deprecated": false
},
{
"slug": "mirrulations",
"name": "mirrulations",
"description": "The regulations.gov website allows users to view proposed rules and supporting documents for the federal rule-making process. In addition, users can post and view comments about those proposed rules. The site contains about 27 million pieces of text and binary data, but the API that provides access only allows a user to obtain one thousand items per hour. As a result, it would take approximately 3 years to download all the data.\nMirrulations (MIRRor of regULATIONS.gov) is a system that uses a",
"managed_by": "[Moravian University Computer Science](https://www.moravian.edu/computer-science)",
"update_frequency": "Hourly",
"tags": "government records",
"license": "https://creativecommons.org/publicdomain/mark/1.0/deed.en",
"deprecated": false
},
{
"slug": "mmid",
"name": "The Massively Multilingual Image Dataset (MMID)",
"description": "MMID is a large-scale, massively multilingual dataset of images paired with the words they represent collected at the [University of Pennsylvania](https://upenn.edu).\nThe dataset is doubly parallel: for each language, words are stored parallel to images that represent the word, _and_ parallel to the word's translation into English (and corresponding images.)\n",
"managed_by": "[Penn NLP](https://github.com/penn-nlp)",
"update_frequency": "Language data is added as it is ready for distribution.",
"tags": "aws-pds,computer vision,machine learning,machine translation,natural language processing",
"license": "See citation instructions at http://multilingual-images.org",
"deprecated": false
},
{
"slug": "mmrf-commpass",
"name": "CoMMpass from the Multiple Myeloma Research Foundation",
"description": "The Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study is the Multiple Myeloma Research Foundation (MMRF)’s landmark personalized medicine initiative. CoMMpass is a\nlongitudinal observation study of around 1000 newly diagnosed myeloma patients receiving various\nstandard approved treatments. The MMRF’s vision is to track the treatment and results for each\nCoMMpass patient so that someday the information can be used to guide decisions for newly\ndiagnose",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,genetic,whole genome sequencing,STRIDES,life sciences",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "modis-astraea",
"name": "MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4",
"description": "Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), managed by\nthe U.S. Geological Survey and NASA. Five products are included:\nMCD43A4 (MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid),\nMOD11A1 (MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid),\nMYD11A1 (MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid),\nMOD13A1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid),\nand MYD13",
"managed_by": "[Astraea](https://astraea.earth/)",
"update_frequency": "New scenes are added daily.",
"tags": "aws-pds,agriculture,geospatial,satellite imagery,natural resource,disaster response",
"license": "There are no restrictions on the use of data, unless expressly identified prior to or at the time of receipt.",
"deprecated": false
},
{
"slug": "modis",
"name": "MODIS",
"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.",
"managed_by": "Not currently managed",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,geospatial,satellite imagery,natural resource,disaster response",
"license": "There are no restrictions on the use of data, unless expressly identified prior to or at the time of receipt.",
"deprecated": true
},
{
"slug": "mogreps",
"name": "UK Met Office Global and Regional Weather Forecasts",
"description": "Data from two models is available: MOEGREPS-UK, a high resolution weather forecast covering the United Kingdom, and MOGREPS-G, a global weather forecast.",
"managed_by": "[UK Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "Not currently updated",
"tags": "aws-pds,agriculture,earth observation,climate,weather,meteorological",
"license": "This data is free to use for non-commercial research purposes only under the terms of the [Non-Commercial Government Licence](http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/non-commercial-government-licence.htm), and not for profit making applications. You must ensure that ",
"deprecated": true
},
{
"slug": "molssi-covid19-hub",
"name": "COVID-19 Molecular Structure and Therapeutics Hub",
"description": "Aggregating critical information to accelerate drug discovery for the molecular modeling and simulation community.\nA community-driven data repository and curation service for molecular structures, models, therapeutics, and\nsimulations related to computational research related to therapeutic opportunities for COVID-19\n(caused by the SARS-CoV-2 coronavirus).\n",
"managed_by": "[Molecular Sciences Software Institute (MolSSI)](https://molssi.org/) and [BioExcel](https://bioexcel.eu/)",
"update_frequency": "Data contributions come from external researchers and groups at a roughly weekly cadence.",
"tags": "aws-pds,biology,bioinformatics,coronavirus,COVID-19,life sciences,molecular docking,pharmaceutical",
"license": "Most data will be in an open license provided by the contributing individual(s). Neither MolSSI nor BioExcel\nhold the license(s) for data in the data set unless explicitly noted.\n",
"deprecated": false
},
{
"slug": "monkey",
"name": "MONKEY",
"description": "This dataset contains the training data for the [Machine learning for Optimal detection of iNflammatory cells in the KidnEY or MONKEY](https://monkey.grand-challenge.org/) challenge. The MONKEY challenge focuses on the automated detection and classification of inflammatory cells, specifically monocytes and lymphocytes, in kidney transplant biopsies using Periodic acid-Schiff (PAS) stained whole-slide images (WSI). It contains 80 WSI, collected from 4 different pathology institutes, with annotate",
"managed_by": "Radboud University Medical Center",
"update_frequency": "As required",
"tags": "aws-pds,life sciences,cancer,computational pathology,deep learning,grand-challenge.org,histopathology,computer vision,digital pathology,medical image computing",
"license": "CC BY-NC-SA 4.0",
"deprecated": false
},
{
"slug": "mosaic",
"name": "Meta-Organized Stimuli And fMRI Imaging data for Computational modeling (MOSAIC)",
"description": "This extensible dataset, MOSAIC, aggregates individual functional magnetic resonance imaging (fMRI) datasets by leveraging a shared preprocessing pipeline and stimulus curation procedure. This dataset aggregation procedure achieves the scale necessary for neural network training and the diversity needed for generalizable results.",
"managed_by": "Massachusetts Institute of Technology, Georgia Tech",
"update_frequency": "New data is uploaded as researchers preprocess their fMRI data according to MOSAIC format and submit.",
"tags": "aws-pds,brain images,brain models,hdf5,neuroimaging,neuroscience,machine learning",
"license": "CC BY 4.0",
"deprecated": false
},
{
"slug": "motional-nuplan",
"name": "nuPlan",
"description": "nuPlan is the world's first large-scale planning benchmark for autonomous driving.",
"managed_by": "[Motional, Inc.](https://motional.com)",
"update_frequency": "Finalized",
"tags": "aws-pds,autonomous vehicles,lidar,robotics,transportation,urban",
"license": "[Commercial](https://www.nuscenes.org/terms-of-use)",
"deprecated": false
},
{
"slug": "motional-nuscenes",
"name": "nuScenes",
"description": "Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car.",
"managed_by": "[Motional, Inc.](https://motional.com)",
"update_frequency": "Finalized",
"tags": "aws-pds,autonomous vehicles,computer vision,lidar,robotics,transportation,urban",
"license": "[Commercial](https://www.nuscenes.org/terms-of-use)",
"deprecated": false
},
{
"slug": "mp2prt",
"name": "Molecular Profiling to Predict Response to Treatment (phs001965)",
"description": "The Molecular Profiling to Predict Response to Treatment (MP2PRT) program is part of the NCI's Cancer Moonshot Initiative. The aim of this program is the retrospective characterization and analysis of biospecimens collected from completed NCI-sponsored trials of the National Clinical Trials Network and the NCI Community Oncology Research Program. This study, titled \"Identification of Genetic Changes Associated with Relapse and/or Adaptive Resistance in Patients Registered as Favorable Histology ",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,life sciences,cancer,genomic,whole genome sequencing,STRIDES",
"license": "[NIH Genomic Data Sharing Policy](https://gdc.cancer.gov/access-data/data-access-policies)",
"deprecated": false
},
{
"slug": "mrkr",
"name": "Emory Knee Radiograph (MRKR) dataset",
"description": "The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of\n503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset\nprovides imaging data in DICOM format along with detailed clinical information, including patient-\nreported pain scores, diagnostic codes, and procedural codes, which are not commonly available in\nsimilar datasets. The MRKR dataset also features imaging metadata such as image laterality, view type,\nand presence o",
"managed_by": "Health Innovation and Translational Informatics Lab at Emory University (hitilab.com)",
"update_frequency": "New data is added as soon as it is available.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,health,imaging,biology,bioinformatics",
"license": "CC-BY-SA",
"deprecated": false
},
{
"slug": "msd",
"name": "Medical Segmentation Decathlon",
"description": "With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalisability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML, would have a tremendous impact on healthcare. The field of medical imaging is a",
"managed_by": "[MONAI Development Team](https://github.com/Project-MONAI/MONAI)",
"update_frequency": "This is a static dataset; however, tutorials and resources will be updated as they are developed.",
"tags": "aws-pds,health,life sciences,medicine,imaging,magnetic resonance imaging,nifti,computed tomography,segmentation",
"license": "[CC-BY-SA 4.0 International](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "multi-token-completion",
"name": "Multi Token Completion",
"description": "This dataset provides masked sentences and multi-token phrases that were masked-out of these sentences.\nWe offer 3 datasets: a general purpose dataset extracted from the Wikipedia and Books corpora, and 2 additional datasets extracted from pubmed abstracts.\nAs for the pubmed data, please be aware that the dataset does not reflect the most current/accurate data available from NLM (it is not being updated).\nFor these datasets, the columns provided for each datapoint are as follows:\ntext- the origi",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,machine learning",
"license": "Datasets are published under [CC-NC-SA-3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/).\nHuman evaluation is published under [CC-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n",
"deprecated": false
},
{
"slug": "multiconer",
"name": "MultiCoNER Datasets",
"description": "MultiCoNER 1 is a large multilingual dataset (11 languages) for Named Entity Recognition. It is designed to represent some of the contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities such as movie titles, and long-tail entity distributions. MultiCoNER 2 is a large multilingual dataset (12 languages) for fine grained Named Entity Recognition. Its fine-grained taxonomy contains 36 NE classes, representing real-world challenges for",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "N/A",
"tags": "natural language processing",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "multimedia-commons",
"name": "Multimedia Commons",
"description": "The Multimedia Commons is a collection of audio and visual features computed for the nearly 100 million Creative Commons-licensed Flickr images and videos in the YFCC100M dataset from Yahoo! Labs, along with ground-truth annotations for selected subsets. The International Computer Science Institute (ICSI) and Lawrence Livermore National Laboratory are producing and distributing a core set of derived feature sets and annotations as part of an effort to enable large-scale video search capabilities",
"managed_by": "[Multimedia Commons](http://mmcommons.org/)",
"update_frequency": "Not updated.",
"tags": "aws-pds,computer vision,machine learning,multimedia,video",
"license": "The International Computer Science Institute and Lawrence Livermore National Laboratory have released the feature corpus and annotations under Creative Commons 0 (public domain), so there are no restrictions on use. More information on licensing and citation of the original metadata and the underlyi",
"deprecated": false
},
{
"slug": "mur",
"name": "Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST)",
"description": "A global, gap-free, gridded, daily 1 km Sea Surface Temperature (SST) dataset created by merging multiple Level-2 satellite SST datasets. Those input datasets include the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 (AMSR-2) on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on sever",
"managed_by": "[Farallon Institute](https://faralloninstitute.org)",
"update_frequency": "The temporal extent of the Zarr store is 2002-06-01 to 2020-01-20.",
"tags": "aws-pds,earth observation,environmental,natural resource,oceans,satellite imagery,climate,water,weather",
"license": "There are no restrictions on the use of these data.",
"deprecated": false
},
{
"slug": "mwis-vr-instances",
"name": "MWIS VR Instances",
"description": "Large-scale node-weighted conflict graphs for maximum weight independent set solvers",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Infrequent",
"tags": "amazon.science,traffic,transportation,graph",
"license": "MIT-0",
"deprecated": false
},
{
"slug": "naip",
"name": "NAIP on AWS",
"description": "The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. This \"leaf-on\" imagery andtypically ranges from 30 centimeters to 100 centimeters in resolution and is available from the naip-analytic Amazon S3 bucket as 4-band (RGB + NIR) imagery in MRF format, on naip-source Amazon S3 bucket as 4-band (RGB + NIR) in uncompressed Raw GeoTiff format and naip-visualization as 3-band (RGB) Cloud Optimized GeoTiff format. More d",
"managed_by": "[Esri](https://www.esri.com/en-us/home)",
"update_frequency": "NAIP data is provided state by state at varying time intervals. Each year, a variable number of states are updated with an overall update cycle of every two to three years for each state. This catalog currently includes NAIP imagery from 2010 through 2023 for most products.",
"tags": "aws-pds,agriculture,earth observation,aerial imagery,geospatial,natural resource,regulatory,cog",
"license": "Public Domain with Attribution\n",
"deprecated": false
},
{
"slug": "nanopore",
"name": "Nanopore Reference Human Genome",
"description": "This dataset includes the sequencing and assembly of a reference standard human genome (GM12878) using the MinION nanopore sequencing instrument with the R9.4 1D chemistry.",
"managed_by": "Nanopore Whole Genome Sequencing Consortium",
"update_frequency": "Data will be added as methodology improves or new data uses are encountered.",
"tags": "aws-pds,genetic,genomic,life sciences,whole genome sequencing",
"license": "Nanopore Human Reference data is released under the Creative Commons CC-BY license and allows free, full and open access to all. For more details please refer to the data reuse and license section of the documentation.",
"deprecated": false
},
{
"slug": "napierone",
"name": "NapierOne Mixed File Dataset",
"description": "NapierOne is a modern cybersecurity mixed file data set, primarily aimed at, but not limited to, ransomware detection and forensic analysis. The dataset contains over 500,000 distinct files, representing 44 distinct popular file types. It was designed to address the known deficiency in research reproducibility and improve consistency by facilitating research replication and repeatability. The data set was inspired by the Govdocs1 data set and it is intended that ‘NapierOne’ be used as a compleme",
"managed_by": "[School of Computing at Edinburgh Napier University](https://www.napier.ac.uk/about-us/our-schools/school-of-computing/)",
"update_frequency": "Data will be added as methodology improves or new common or required file types are encountered.",
"tags": "computer forensics,computer security,cyber security,digital forensics,ransomware,malware,mixed file dataset,aws-pds",
"license": "NapierOne is released under the Edinburgh Napier University License Agreement and allows free, full and open access to all. For more details please refer to the License and Attribution section of the documentation. The license agreement means that you are free to Copy, publish, distribute and transm",
"deprecated": false
},
{
"slug": "nara-1940-census",
"name": "1940 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions",
"description": "The 1940 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1940, although some persons were missed. The 1940 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 2, 2012.\nThe 1940 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tr",
"managed_by": "National Archives and Records Administration (NARA)",
"update_frequency": "Not updated",
"tags": "nara,census,archives,1940 census,demography,aws-pds",
"license": "US Government work",
"deprecated": false
},
{
"slug": "nara-1950-census",
"name": "1950 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions",
"description": "The 1950 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1950, although some persons were missed. The 1950 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 1, 2022.\nThe 1950 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tr",
"managed_by": "National Archives and Records Administration (NARA)",
"update_frequency": "Not updated",
"tags": "nara,census,archives,1950 census,demography,aws-pds",
"license": "US Government work",
"deprecated": false
},
{
"slug": "nara-national-archives-catalog",
"name": "National Archives Catalog",
"description": "The National Archives Catalog dataset contains all of the descriptions; authority records; digitized and electronic records; and tags, transcriptions and comments for NARA’s archival holdings available in the Catalog.",
"managed_by": "National Archives and Records Administration (NARA)",
"update_frequency": "Biannual",
"tags": "nara,national archives catalog,archives,government records,aws-pds",
"license": "US Government work",
"deprecated": false
},
{
"slug": "nasa-airibrad",
"name": "AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC",
"description": "WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.\n\nThe thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitu",
"managed_by": "NASA",
"update_frequency": "From 2002-08-30 to Ongoing",
"tags": "aws-pds,atmosphere,datacenter,earth observation,global,hdf,ice,land,metadata,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-airicrad",
"name": "AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC",
"description": "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data ",
"managed_by": "NASA",
"update_frequency": "From 2002-08-30 to Ongoing",
"tags": "aws-pds,atmosphere,climate,datacenter,earth observation,global,metadata,opendap,orbit,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-astl1t",
"name": "ASTER Level 1T Precision Terrain Corrected Registered At-Sensor Radiance V004",
"description": "The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.004)) that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A ([AST_L1A](https://doi.org/10.5067/ASTER/A",
"managed_by": "NASA",
"update_frequency": "From 2000-03-04 to Ongoing (Varies)",
"tags": "aws-pds,cog,earth observation,global,land,orbit,cog",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-atl03",
"name": "ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006",
"description": "This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument pa",
"managed_by": "NASA",
"update_frequency": "From 2018-10-13 to Ongoing",
"tags": "aws-pds,atmosphere,datacenter,earth observation,global,hdf,ice,land,water",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-atl08",
"name": "ATLAS/ICESat-2 L3A Land and Vegetation Height V006",
"description": "This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\nRead our doc on how to get AWS Credentials to retrieve this",
"managed_by": "NASA",
"update_frequency": "From 2018-10-14 to Ongoing",
"tags": "aws-pds,atmosphere,datacenter,earth observation,global,ice,land,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-ega",
"name": "Mars Spectrometry: Detect Evidence for Past Habitability",
"description": "NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.
\nBecause communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limi",
"managed_by": "NASA",
"update_frequency": "Static dataset for the commercial data\nWhen more data is collected on the SAM instrument on Mars, the data will be available on the Planetary Data System website: Data from all SAM experiments are archived in the Planetary Data System (pds.nasa.gov). \n",
"tags": "aws-pds,analytics,archives,deep learning,planetary,machine learning,NASA SMD AI",
"license": "There are no restrictions on the use, access, and/or download of data from the NASA project.",
"deprecated": false
},
{
"slug": "nasa-gcms",
"name": "Mars Spectrometry 2: Gas Chromatography for the Sample Analysis at Mars Data (SAM) Instrument",
"description": "NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.
\nBecause communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limi",
"managed_by": "NASA",
"update_frequency": "\"Static dataset for the commercial data. When more data is collected on the SAM instrument on Mars, the data will be available on the Planetary Data System [website](https://pds-geosciences.wustl.edu/missions/msl/sam.htm)\" \n",
"tags": "aws-pds,analytics,archives,deep learning,planetary,machine learning,NASA SMD AI",
"license": "There are no restrictions on the use, access, and/or download of data from the NASA project.",
"deprecated": false
},
{
"slug": "nasa-gedi02a",
"name": "GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002",
"description": "The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest",
"managed_by": "NASA",
"update_frequency": "From 2019-04-04 to 2023-03-16 (Varies)",
"tags": "aws-pds,biodiversity,carbon,datacenter,earth observation,energy,global,hdf,ice,land",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gedil4aagbdensityv212056",
"name": "GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1",
"description": "This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of wav",
"managed_by": "NASA",
"update_frequency": "From 2019-04-17 to 2024-11-27",
"tags": "aws-pds,earth observation,ecosystems,global,land,land cover,lidar,opendap,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm2adpr",
"name": "GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) at GES DISC",
"description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n.\n\n2ADPR provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products: those derived from the Ku-band frequency over a wide swath (245 km), those derived from the Ka-band frequency over a n",
"managed_by": "NASA",
"update_frequency": "From 2014-03-08 to Ongoing",
"tags": "aws-pds,atmosphere,contamination,datacenter,earth observation,global,metadata,opendap,radar,water",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imergde",
"name": "GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC",
"description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observation",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,coastal,datacenter,global,hydrology,land,metadata,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imergdf",
"name": "GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC",
"description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observation",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,climate,coastal,datacenter,global,hydrology,ice,land,metadata,netcdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imergdl",
"name": "GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC",
"description": "Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observation",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,coastal,datacenter,global,hydrology,land,metadata,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imerghh",
"name": "GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH) at GES DISC",
"description": "Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the God",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,datacenter,forecast,global,hdf,hydrology,land,metadata",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imerghhe",
"name": "GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE) at GES DISC",
"description": "Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the God",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,datacenter,forecast,global,hdf,hydrology,land,metadata",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imerghhl",
"name": "GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL) at GES DISC",
"description": "Version 07B is the current version of the IMERG data sets. Older versions\nwill no longer be available and have been superseded by Version 07.\\n\\nThe Integrated\nMulti-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides\nthe multi-satellite precipitation product for the U.S. GPM team.\\n\\nThe precipitation\nestimates from the various precipitation-relevant satellite passive microwave (PMW)\nsensors comprising the GPM constellation are computed using the 2021 version of\nthe",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,datacenter,forecast,global,hydrology,land,metadata,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpm3imergm",
"name": "GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 (GPM_3IMERGM) at GES DISC",
"description": "Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the God",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,datacenter,forecast,global,hydrology,land,metadata,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpmimerglandseamask",
"name": "Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree V2 (GPM_IMERG_LandSeaMask) at GES DISC",
"description": "Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.\n\nThis land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant net",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,coastal,datacenter,global,land,metadata,opendap,netcdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-gpmmergir",
"name": "NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) at GES DISC",
"description": "These data originate from NOAA/NCEP.\n\nThe NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. The original record started from February, 2000, but in June, 2025 it was extended back to January, 1998.\n\nThe leading edge o",
"managed_by": "NASA",
"update_frequency": "From 1998-01-01 to Ongoing",
"tags": "aws-pds,atmosphere,climate,datacenter,forecast,global,metadata,opendap,netcdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-heasarc",
"name": "NASA High Energy Astrophysics Mission Data",
"description": "NASA data for high energy astrophysics (generally x-ray and gamma-ray domains) is made available here by the High Energy Astrophysics Science Archive Research Center. The HEASARC hosts the full data archives of over 30 different missions spanning 50 years. The data archive for each mission will contain a range of data types from spacecraft housekeeping and raw photon event list data up to high level science-ready products such as images, light curves (time series), and energy spectra.\n
",
"managed_by": "The [HEASARC](https://heasarc.gsfc.nasa.gov/)",
"update_frequency": "Various.",
"tags": "aws-pds,astronomy,archives,datacenter,imaging,satellite imagery,x-ray",
"license": "See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasarc/data_policy.html)",
"deprecated": false
},
{
"slug": "nasa-hlsl30",
"name": "HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0",
"description": "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every ",
"managed_by": "NASA",
"update_frequency": "From 2013-04-11 to Ongoing",
"tags": "aws-pds,atmosphere,cog,datacenter,earth observation,geospatial,global,ice,land,metadata",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-hlss30",
"name": "HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0",
"description": "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MS",
"managed_by": "NASA",
"update_frequency": "From 2015-11-28 to Ongoing",
"tags": "aws-pds,cog,datacenter,earth observation,geospatial,global,hdf,ice,land,metadata",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-imergprecipcanadaalaska2097",
"name": "ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020",
"description": "This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global c",
"managed_by": "NASA",
"update_frequency": "From 2000-06-01 to 2020-12-31",
"tags": "aws-pds,atmosphere,cog,earth observation,global,land,radar,cog",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-lambda",
"name": "NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA)",
"description": "NASA data for cosmic microwave background (CMB) analysis is made available here by the Legacy Archive for Microwave Background Data Analysis (LAMBDA), which is a part of NASA's High Energy Astrophysics Science Archive Research Center (HEASARC). LAMBDA hosts the data archives of over 30 different CMB missions spanning 30+ years. The data archive for each mission may contain a range of data types from low-level time-ordered data to high level science-ready products such as sky maps and angular pow",
"managed_by": "[LAMBDA](https://lambda.gsfc.nasa.gov/)",
"update_frequency": "Various.",
"tags": "aws-pds,astronomy,archives,datacenter,imaging,satellite imagery",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "nasa-m2i3npasm",
"name": "MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields",
"description": "M2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data field is available every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 U",
"managed_by": "NASA",
"update_frequency": "From 1980-01-01 to Ongoing",
"tags": "aws-pds,agriculture,air temperature,atmosphere,biodiversity,climate,coastal,datacenter,ecosystems,global",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-m2i3nvaer",
"name": "MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio 0.625 x 0.5 degree",
"description": "M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specificat",
"managed_by": "NASA",
"update_frequency": "From 1980-01-01 to Ongoing",
"tags": "aws-pds,agriculture,air quality,atmosphere,biodiversity,carbon,climate,coastal,datacenter,ecosystems",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-m2i3nvasm",
"name": "MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree",
"description": "M2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-",
"managed_by": "NASA",
"update_frequency": "From 1980-01-01 to Ongoing",
"tags": "aws-pds,agriculture,air temperature,atmosphere,biodiversity,climate,coastal,datacenter,ecosystems,global",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-m2t1nxslv",
"name": "MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree",
"description": "M2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or",
"managed_by": "NASA",
"update_frequency": "From 1980-01-01 to Ongoing",
"tags": "aws-pds,agriculture,air temperature,atmosphere,biodiversity,climate,coastal,datacenter,ecosystems,global",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mcd43a1",
"name": "MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061",
"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive ",
"managed_by": "NASA",
"update_frequency": "From 2000-02-16 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,earth observation,geospatial,global,land,opendap,tiles,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mcd43a3",
"name": "MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061",
"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.\n\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.e",
"managed_by": "NASA",
"update_frequency": "From 2000-02-16 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,earth observation,geospatial,global,land,opendap,satellite imagery,tiles,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mcd43a4",
"name": "MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m V061",
"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.\n\nUsers are u",
"managed_by": "NASA",
"update_frequency": "From 2000-02-16 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,earth observation,geospatial,global,land,opendap,satellite imagery,tiles,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mi1b2e",
"name": "MISR Level 1B2 Ellipsoid Data V004",
"description": "MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing.\r\n\r\nMISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is succe",
"managed_by": "NASA",
"update_frequency": "From 1999-12-18 to Ongoing",
"tags": "aws-pds,atmosphere,climate,cyclone typhoon hurricane,datacenter,earth observation,global,land,opendap,orbit",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mod02hkm",
"name": "MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m",
"description": "The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared ",
"managed_by": "NASA",
"update_frequency": "From 2000-02-24 to Ongoing",
"tags": "aws-pds,atmosphere,datacenter,earth observation,environmental,global,metadata,opendap,orbit,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mod09a1",
"name": "MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061",
"description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel c",
"managed_by": "NASA",
"update_frequency": "From 2000-02-18 to Ongoing (Weekly - < Monthly)",
"tags": "aws-pds,earth observation,geospatial,global,land,opendap,satellite imagery,hdf",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mod09ga",
"name": "MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061",
"description": "The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many",
"managed_by": "NASA",
"update_frequency": "From 2000-02-24 to Ongoing",
"tags": "aws-pds,datacenter,earth observation,geospatial,global,hdf,ice,land,opendap,satellite imagery",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mod09gq",
"name": "MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061",
"description": "The MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m prod",
"managed_by": "NASA",
"update_frequency": "From 2000-02-24 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,datacenter,earth observation,geospatial,global,hdf,ice,land,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mod13q1",
"name": "MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061",
"description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The ",
"managed_by": "NASA",
"update_frequency": "From 2000-02-18 to Ongoing (Weekly - < Monthly)",
"tags": "aws-pds,datacenter,earth observation,geospatial,global,hdf,ice,land,opendap,satellite imagery",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mod16a2",
"name": "MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061",
"description": "The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. \n\nProvided in the MOD16A2 p",
"managed_by": "NASA",
"update_frequency": "From 2021-01-01 to Ongoing (Weekly - < Monthly)",
"tags": "aws-pds,atmosphere,earth observation,evapotranspiration,geospatial,global,land,land cover,opendap,water",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-modis-t-jpl-l2p-v2019-0",
"name": "GHRSST Level 2P Global Sea Surface Skin Temperature from the MODIS on the NASA Terra satellite (GDS2)",
"description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in t",
"managed_by": "NASA",
"update_frequency": "From 2000-02-24 to Ongoing (Hourly - < Daily)",
"tags": "aws-pds,atmosphere,datacenter,earth observation,global,land,marine,metadata,oceans,orbit",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-mur-jpl-l4-glob-v41",
"name": "GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)",
"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments i",
"managed_by": "NASA",
"update_frequency": "From 2002-05-31 to Ongoing (Hourly - < Daily)",
"tags": "aws-pds,datacenter,earth observation,global,ice,metadata,oceans,parquet,us,water",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-myd09ga",
"name": "MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061",
"description": "The MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of th",
"managed_by": "NASA",
"update_frequency": "From 2002-07-04 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,datacenter,earth observation,geospatial,global,hdf,ice,land,opendap,satellite imagery",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-myd09gq",
"name": "MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061",
"description": "The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). \n\nKnow",
"managed_by": "NASA",
"update_frequency": "From 2002-07-04 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,datacenter,earth observation,geospatial,global,hdf,ice,land,opendap",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal2cslc-s1-staticv1",
"name": "OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1)",
"description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These s",
"managed_by": "NASA",
"update_frequency": "From 2014-04-03 to Ongoing",
"tags": "aws-pds,coastal,earth observation,hdf,ice,land,metadata,oceans,orbit,radar",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal2cslc-s1v1",
"name": "OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)",
"description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets correcte",
"managed_by": "NASA",
"update_frequency": "From 2014-06-15 to Ongoing",
"tags": "aws-pds,coastal,earth observation,hdf,ice,land,metadata,oceans,orbit,radar",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal2rtc-s1-staticv1",
"name": "OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)",
"description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and",
"managed_by": "NASA",
"update_frequency": "From 2014-04-03 to Ongoing",
"tags": "aws-pds,coastal,cog,earth observation,geoscience,global,ice,land,metadata,oceans",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal2rtc-s1v1",
"name": "OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)",
"description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Comp",
"managed_by": "NASA",
"update_frequency": "From 2016-04-14 to Ongoing",
"tags": "aws-pds,coastal,earth observation,geoscience,global,hdf,ice,land,metadata,oceans",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal3disp-s1v1",
"name": "OPERA Surface Displacement from Sentinel-1 validated product (Version 1)",
"description": "The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslide",
"managed_by": "NASA",
"update_frequency": "From 2016-07-01 to Ongoing",
"tags": "aws-pds,earth observation,land,metadata,netcdf,orbit,radar,sentinel-1,synthetic aperture radar,xml",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal3dist-alert-hlsprovisionalv0",
"name": "OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0)",
"description": "The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product which was released on March 14, 2024, and has achieved stage 1 validation.\n\nThe Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation",
"managed_by": "NASA",
"update_frequency": "From 2022-01-01 to 2024-02-26",
"tags": "aws-pds,cog,earth observation,environmental,global,land,land cover,land use",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal3dist-alert-hlsv1",
"name": "OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1)",
"description": "The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when ther",
"managed_by": "NASA",
"update_frequency": "From 2022-01-01 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,cog,earth observation,environmental,global,land,land cover,land use,satellite imagery",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal3dist-ann-hlsv1",
"name": "OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1)",
"description": "The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the [DIST-ALERT](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also pro",
"managed_by": "NASA",
"update_frequency": "From 2022-01-01 to Ongoing (Annual)",
"tags": "aws-pds,cog,earth observation,environmental,global,land,land cover,land use",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal3dswx-hlsv1",
"name": "OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1)",
"description": "This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Ins",
"managed_by": "NASA",
"update_frequency": "From 2023-04-04 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,cog,datacenter,earth observation,ice,land,land cover,metadata,surface water,water",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-operal3dswx-s1v1",
"name": "OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1)",
"description": "This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud condition",
"managed_by": "NASA",
"update_frequency": "From 2024-08-01 to Ongoing (Daily - < Weekly)",
"tags": "aws-pds,cog,datacenter,earth observation,global,land,orbit,radar,sentinel-1,surface water",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-osdr",
"name": "NASA Space Biology Open Science Data Repository (OSDR)",
"description": "NASA’s Space Biology Open Science Data Repository (OSDR) introduces a one-stop site where users can explore and contribute a variety of NASA open science biological data. This site consolidates data from the Ames Life Sciences Data Archive (ALSDA) and GeneLab and includes information about the broader NASA Open Science and Open Data initiatives, all at one centralized location. Our mission is to maximize the utilization of the valuable biological research resources and enable new discoveries.
\n- Meteorological: ~2-3 days
\n- Solar ~5-7 days
\nFor more information see the [NASA POWER Availability Dashboards](https://power.larc.nasa.gov/dashboard/availability/)\n",
"tags": "agriculture,air quality,analytics,archives,atmosphere,climate,climate model,data assimilation,deep learning,earth observation",
"license": "There are no restrictions on the use, access, and/or download of data from the NASA POWER Project. We request that you cite the NASA POWER Project when using the data provided from NASA POWER Project.
[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-psi",
"name": "NASA Physical Sciences Informatics (PSI)",
"description": "NASA's Physical Sciences Research Program, along with its predecessors, has conducted significant fundamental and applied research in the physical sciences. The International Space Station (ISS) is an orbiting laboratory that provides an ideal facility to conduct long-duration experiments in the near absence of gravity and allows continuous and interactive research similar to Earth-based laboratories. This enables scientists to pursue innovations and discoveries not currently achievable by other",
"managed_by": "NASA",
"update_frequency": "New research data is added as soon as it is available.",
"tags": "aws-pds,chemistry,space biology,fluid dynamics,physics,materials science",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "nasa-sentinel-1adpgrdhigh",
"name": "SENTINEL-1A_DUAL_POL_GRD_HIGH_RES",
"description": "Sentinel-1A Dual-pol ground projected high and full resolution images\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME",
"managed_by": "NASA",
"update_frequency": "From 2014-04-03 to Ongoing",
"tags": "aws-pds,agriculture,coastal,earth observation,earthquakes,ecosystems,ice,land,land cover,land use",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-sentinel-1aslc",
"name": "SENTINEL-1A_SLC",
"description": "Sentinel-1A slant-range product\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME",
"managed_by": "NASA",
"update_frequency": "From 2014-04-03 to Ongoing",
"tags": "aws-pds,coastal,earthquakes,ecosystems,ice,land,land cover,land use,metadata,oceans",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-sentinel-1bdpgrdhigh",
"name": "SENTINEL-1B_DUAL_POL_GRD_HIGH_RES",
"description": "Sentinel-1B Dual-pol ground projected high and full resolution images\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME",
"managed_by": "NASA",
"update_frequency": "From 2016-04-25 to 2021-12-24",
"tags": "aws-pds,agriculture,coastal,earthquakes,ecosystems,ice,land,land cover,land use,metadata",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-sentinel-1bslc",
"name": "SENTINEL-1B_SLC",
"description": "Sentinel-1B slant-range product\nRead our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME",
"managed_by": "NASA",
"update_frequency": "From 2016-04-25 to 2021-12-24",
"tags": "aws-pds,agriculture,coastal,earthquakes,ecosystems,ice,land,land cover,land use,metadata",
"license": "[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-soho-comet-challenge-on-aws",
"name": "NASA SOHO/LASCO2 comet challenge on AWS",
"description": "The SOHO/LASCO data set (prepared for the challenge hosted in Topcoder) provided here comes from the instrument’s C2 telescope and comprises approximately 36,000 images spread across 2,950 comet observations. The human eye is a very sensitive tool and it is the only tool currently used to reliably detect new comets in SOHO data - particularly comets that are very faint and embedded in the instrument background noise. Bright comets can be easily detected in the LASCO data by relatively simple aut",
"managed_by": "[NASA](http://www.nasa.gov/)",
"update_frequency": "No updates",
"tags": "aws-pds,astronomy,machine learning,NASA SMD AI",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "nasa-soteria-data",
"name": "NASA SOTERIA Simulation Testbed Data",
"description": "Commercial pilot simulation data during safety-of-flight scenarios.",
"managed_by": "[NASA](http://www.nasa.gov/)",
"update_frequency": "As required",
"tags": "workload analysis,neuroimaging,transportation,life sciences",
"license": "[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-controlled-mro-ctx-dtms",
"name": "NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs",
"description": "As of March, 2023 the Mars Reconnaissance Orbiter (MRO) High Resolution Science Experiment (HiRISE) sensor has collected more than 5000 targeted stereopairs. During HiRISE acquisition, the Context Camera (CTX) also collects lower resolution, higher spatial extent context images. These CTX acquisitions are also targeted stereopairs. This data set contains targeted CTX DTMs and orthoimages, created using the NASA Ames Stereopipeline. These data have been created using relatively controlled CTX ima",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "Updated as new stereoapirs are processed",
"tags": "aws-pds,planetary,satellite imagery,elevation,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-europa-dtms",
"name": "NASA / USGS Controlled Europa DTMs",
"description": "Knowledge of a planetary surface’s topography is necessary to understand its geology and enable landed mission operations. The Solid State Imager (SSI) on board NASA’s Galileo spacecraft acquired more than 700 images of Jupiter’s moon Europa. Although moderate- and high-resolution coverage is extremely limited, repeat coverage of a small number of sites enables the creation of digital terrain models (DTMs) via stereophotogrammetry. Here we provide stereo-derived DTMs of five sites on Europa. The",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "HiRISE data will be updated as new releases are made to the Planetary Data System.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-europa-mosaics",
"name": "NASA / USGS Europa Controlled Observation Mosaics",
"description": "The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 92 image mosaics generated from minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface.
\nThese images provide users with nearly the entire Galileo",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "No future updates planned.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-europa-observations",
"name": "NASA / USGS Europa Controlled Observations",
"description": "The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 481 minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface. These individual images were subsequently used as input into a set of 92 observation mosaics. <",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "No future updates planned.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-lunar-orbiter-laser-altimeter",
"name": "NASA / USGS Lunar Orbiter Laser Altimeter Cloud Optimized Point Cloud",
"description": "The lunar orbiter laser altimeter (LOLA) has collected and released almost 7 billion individual laser altimeter returns from the lunar surface. This dataset includes individual altimetry returns scraped from the Planetary Data System (PDS) LOLA Reduced Data Record (RDR) Query Tool, V2.0. Data are organized in 15˚ x 15˚ (longitude/latitude) sections, compressed and encoded into the Cloud Optimized Point Cloud (COPC) file format, and collected into a Spatio-Temporal Asset Catalog (STAC) collection",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "Intermittent as new LOLA RDR data are released and processing capabilities become available.",
"tags": "aws-pds,planetary,elevation,lidar,stac",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-mars-hirise-dtms",
"name": "NASA / USGS Released HiRISE Digital Terrain Models",
"description": "These data are digital terrain models (DTMs) created by multiple different institutions and released to the Planetary Data System (PDS) by the University of Arizona. The data are processed from the Planetary Data System (PDS) stored JP2 files, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are controlled to the Mars Orbiter Laser Altimeter (MOLA). Therefore, they are a proxy for the geodetic coordinate reference frame. These data are ",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "HiRISE DTMs will be updated as new releases are made by the University of Arizona to the Planetary Data System.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-mars-hirise",
"name": "NASA / USGS Uncontrolled HiRISE RDRs",
"description": "These data are red and color Reduced Data Record (RDR) observations collected and originally processed by the High Resolution Imaging Science Experiment (HiRISE) team. The mdata are processed from the Planetary Data System (PDS) stored RDRs, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are not photogrammetrically controlled and use a priori NAIF SPICE pointing. Therefore, these data will not co-register with controlled data products",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "HiRISE data will be updated as new releases are made to the Planetary Data System.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasa-usgs-themis-mosaics",
"name": "NASA / USGS Controlled THEMIS Mosaics",
"description": "These data are infrared image mosaics, tiled to the Mars quadrangle, generated using Thermal Emission Imaging System (THEMIS) images from the 2001 Mars Odyssey orbiter mission. The mosaic is generated at the full resolution of the THEMIS infrared dataset, which is approximately 100 meters/pixel. The mosaic was absolutely photogrammetrically controlled to an improved Viking MDIM network that was develop by the USGS Astrogeology processing group using the Integrated Software for Imagers and Spectr",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "None planned.",
"tags": "aws-pds,planetary,satellite imagery,stac,cog",
"license": "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "nasanex",
"name": "NASA Earth Exchange (NEX) Data Collection",
"description": "A collection of downscaled climate change projections, derived from the\nGeneral Circulation Model (GCM) runs conducted under the Coupled Model\nIntercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across\nthe four greenhouse gas emissions scenarios known as Representative\nConcentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth\nExchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and\nLOCA (CMIP5).\n",
"managed_by": "NASA",
"update_frequency": "Infrequently",
"tags": "aws-pds,CMIP5,natural resource,climate,sustainability",
"license": "US Government work",
"deprecated": false
},
{
"slug": "naturalearth",
"name": "Natural Earth",
"description": "Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.",
"managed_by": "North American Cartographic Information Society (nacis.org)",
"update_frequency": "As needed",
"tags": "aws-pds,mapping,geospatial,global,tiles,earth observation,population",
"license": "Public Domain. https://www.naturalearthdata.com/about/terms-of-use/",
"deprecated": false
},
{
"slug": "ncar-cesm-lens",
"name": "Community Earth System Model Large Ensemble (CESM LENS)",
"description": "The Community Earth System Model (CESM) Large Ensemble Numerical Simulation (LENS) dataset includes a 40-member ensemble of climate simulations for the period 1920-2100 using historical data (1920-2005) or assuming the RCP8.5 greenhouse gas concentration scenario (2006-2100), as well as longer control runs based on pre-industrial conditions. The data comprise both surface (2D) and volumetric (3D) variables in the atmosphere, ocean, land, and ice domains. The total data volume of the original dat",
"managed_by": "[National Center for Atmospheric Research](https://ncar.ucar.edu/)",
"update_frequency": "Rare. The LENS experiment is complete, but we may occasionally copy additional fields from NCAR to AWS, or fix problems discovered in the AWS copy.",
"tags": "climate,model,climate model,atmosphere,oceans,land,ice,geospatial,aws-pds,sustainability",
"license": "https://www.ucar.edu/terms-of-use/data",
"deprecated": false
},
{
"slug": "ncar-cesm2-arise",
"name": "Community Earth System Model v2 ARISE (CESM2 ARISE)",
"description": "Data from ARISE-SAI Experiments with CESM2",
"managed_by": "[National Center for Atmospheric Research](https://ncar.ucar.edu/)",
"update_frequency": "Rare once complete (August 2022)",
"tags": "climate,model,climate model,atmosphere,oceans,land,ice,geospatial,aws-pds,sustainability",
"license": "https://www.ucar.edu/terms-of-use/data",
"deprecated": false
},
{
"slug": "ncar-cesm2-lens",
"name": "Community Earth System Model v2 Large Ensemble (CESM2 LENS)",
"description": "The US National Center for Atmospheric Research partnered with the IBS Center for Climate Physics in South Korea to generate the CESM2 Large Ensemble which consists of 100 ensemble members at 1 degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Data sets from this ensemble were made downloadable via the Climate Data Gateway on June 14th, 2021.\nNCAR has copied a subset (currently ~500 TB) of CESM2 LENS data to Amazon S3 as",
"managed_by": "[National Center for Atmospheric Research](https://ncar.ucar.edu/)",
"update_frequency": "Rare. The LENS experiment is complete, but we may occasionally copy additional fields from NCAR to AWS, or fix problems discovered in the AWS copy.",
"tags": "climate,model,climate model,atmosphere,oceans,land,ice,geospatial,aws-pds,sustainability",
"license": "https://www.ucar.edu/terms-of-use/data",
"deprecated": false
},
{
"slug": "ncar-dart-cam6",
"name": "CAM6 Data Assimilation Research Testbed (DART) Reanalysis: Cloud-Optimized Dataset",
"description": "This is a cloud-hosted subset of the CAM6+DART (Community Atmosphere Model version 6 Data Assimilation Research Testbed) Reanalysis dataset. These data products are designed to facilitate a broad variety of research using the NCAR CESM 2.1 (National Center for Atmospheric Research's Community Earth System Model version 2.1), including model evaluation, ensemble hindcasting, data assimilation experiments, and sensitivity studies. They come from an 80 member ensemble reanalysis of the global tropo",
"managed_by": "[National Center for Atmospheric Research](https://ncar.ucar.edu/)",
"update_frequency": "Rare. Additional variables or years outside of 2011-2019 may be added in the future.",
"tags": "atmosphere,land,climate,climate model,data assimilation,forecast,meteorological,weather,geoscience,geospatial",
"license": "https://www.ucar.edu/terms-of-use/data",
"deprecated": false
},
{
"slug": "ncar-na-cordex",
"name": "NA-CORDEX - North American component of the Coordinated Regional Downscaling Experiment",
"description": "The NA-CORDEX dataset contains regional climate change scenario data and guidance for North America, for use in impacts, decision-making, and climate science. The NA-CORDEX data archive contains output from regional climate models (RCMs) run over a domain covering most of North America using boundary conditions from global climate model (GCM) simulations in the CMIP5 archive. These simulations run from 1950–2100 with a spatial resolution of 0.22°/25km or 0.44°/50km. This AWS S3 version of the d",
"managed_by": "[National Center for Atmospheric Research](https://ncar.ucar.edu/)",
"update_frequency": "Rare. NA-CORDEX is complete, but we may occasionally copy additional fields from NCAR to AWS, or fix problems discovered in the data.",
"tags": "climate,model,climate model,atmosphere,land,geospatial,aws-pds,sustainability,zarr",
"license": "https://na-cordex.org/terms-use.html",
"deprecated": false
},
{
"slug": "ncbi-blast-databases",
"name": "Basic Local Alignment Sequences Tool (BLAST) Databases",
"description": "A centralized repository of pre-formatted BLAST databases created by the National Center for Biotechnology Information (NCBI).",
"managed_by": "[National Library of Medicine (NLM)](http://nlm.nih.gov/)",
"update_frequency": "Periodically",
"tags": "aws-pds,bioinformatics,biology,health,life sciences,genetic,genomic,transcriptomics,protein,reference index",
"license": "\"[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)\"",
"deprecated": false
},
{
"slug": "ncbi-covid-19",
"name": "COVID-19 Genome Sequence Dataset",
"description": "This repository within the [ACTIV TRACE initiative](https://www.nih.gov/research-training/medical-research-initiatives/activ/tracking-resistance-coronavirus-evolution-trace) houses a comprehensive collection of datasets related to SARS-CoV-2. The processing of SARS-CoV-2 Sequence Read Archive (SRA) files has been optimized to identify genetic variations in viral samples. This information is then presented in the Variant Call Format (VCF). Each VCF file corresponds to the SRA parent-run's accessi",
"managed_by": "[National Library of Medicine (NLM)](http://nlm.nih.gov/)",
"update_frequency": "Daily",
"tags": "aws-pds,bioinformatics,biology,coronavirus,COVID-19,fastq,bam,cram,genomic,genetic",
"license": "[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)",
"deprecated": false
},
{
"slug": "ncbi-fcs-gx",
"name": "GX database for NCBI Foreign Contamination Screen (FCS) Tool Suite",
"description": "Sequence database used by FCS-GX (Foreign Contamination Screen - Genome Cross-species aligner) to detect contamination from foreign organisms in genome sequences.",
"managed_by": "[National Library of Medicine (NLM)](http://nlm.nih.gov/)",
"update_frequency": "Yearly",
"tags": "aws-pds,bioinformatics,biology,contamination,fasta,genome,assembly,genetic,health,life sciences",
"license": "[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)",
"deprecated": false
},
{
"slug": "ncbi-pmc",
"name": "NIH NLM NCBI PubMed Central (PMC) Article Datasets - Full-Text Biomedical and Life Sciences Journal Articles on AWS",
"description": "PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The PubMed Central (PMC) Article Datasets include full-text articles archived in PMC and made available under license terms that allow for text mining and other types of secondary analysis and reuse. The articles are organized on AWS based on PMCID and version number:
\nThe PMC Open Access (OA) Subset, which ",
"managed_by": "[National Library of Medicine (NLM)](http://nlm.nih.gov/)",
"update_frequency": "Continuous - article versions are updated continuously. Updates include - the addition of new article versions; the update of one or more objects belonging to an existing article version; in rare cases, the removal of an article version",
"tags": "aws-pds,life sciences,STRIDES,txt,xml,csv",
"license": "[PMC Copyright](https://www.ncbi.nlm.nih.gov/pmc/about/copyright/)",
"deprecated": false
},
{
"slug": "ncbi-sra",
"name": "NIH NCBI Sequence Read Archive (SRA) on AWS",
"description": "The Sequence Read Archive (SRA), produced by the [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/) at the [National Library of Medicine (NLM)](http://nlm.nih.gov/) at the [National Institutes of Health (NIH)](http://www.nih.gov/), stores raw DNA sequencing data and alignment information from high-throughput sequencing platforms. The SRA provides open access to these biological sequence data to support the research community's efforts to enhance reproducibility",
"managed_by": "[National Library of Medicine (NLM)](http://nlm.nih.gov/)",
"update_frequency": "Daily",
"tags": "aws-pds,life sciences,genetic,genomic,whole genome sequencing,whole exome sequencing,transcriptomics,fastq,bam,cram",
"license": "[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)",
"deprecated": false
},
{
"slug": "ncei-wcsd-archive",
"name": "NOAA Water-Column Sonar Data Archive",
"description": "Water-column sonar data archived at the NOAA National Centers for Environmental Information.",
"managed_by": "[NOAA](https://www.ngdc.noaa.gov/mgg/wcd/)",
"update_frequency": "New water-column sonar data are added regularly as they are provided to the archive.",
"tags": "aws-pds,earth observation,biodiversity,ecosystems,environmental,geospatial,mapping,oceans",
"license": "The data may be used and redistributed for free but is not intended for legal use, since it may contain inaccuracies. Neither the data Creator, NOAA, the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantabili",
"deprecated": false
},
{
"slug": "nci-imaging-data-commons",
"name": "National Cancer Institute Imaging Data Commons (IDC) Collections",
"description": "[Imaging Data Commons (IDC)](https://imaging.datacommons.cancer.gov) is a repository within the \n[Cancer Research Data Commons (CRDC)](https://datacommons.cancer.gov) that manages imaging data \nand enables its integration with the other components of CRDC. \n\nIDC hosts a growing number of imaging collections that are contributed \nby either funded US National Cancer Institute (NCI) data collection \nactivities, or by the individual researchers.\n\nImage data hosted by IDC is stored in DICOM format",
"managed_by": "Imaging Data Commons (IDC)(https://imaging.datacommons.cancer.gov) team",
"update_frequency": "Every 1-3 months",
"tags": "aws-pds,cancer,imaging,digital pathology,radiology,microscopy,fluorescence imaging,image processing,machine learning,life sciences",
"license": "https://fairsharing.org/FAIRsharing.0b5a1d",
"deprecated": false
},
{
"slug": "nciccr-dlbcl",
"name": "National Cancer Institute Center for Cancer Research - Diffuse Large B Cell Lymphoma (DLBCL) Genomics and Expression",
"description": "The study describes integrative analysis of genetic lesions in 574 diffuse large B cell lymphomas\n(DLBCL) involving exome and transcriptome sequencing, array-based DNA copy number analysis and\ntargeted amplicon resequencing. The dataset contains open RNA-Seq Gene Expression Quantification\ndata.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "ndui",
"name": "Normalized Difference Urban Index (NDUI)",
"description": "NDUI is combined with cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution,and it can greatly enhance urban areas, which can then be easily distinguished from bare lands including fallows and deserts. With the capability to delineate urban boundaries and, at the same time, to present sufficient spatial details within urban areas, the NDUI has the potential for urbanization st",
"managed_by": "Remote Sensing Big Data Intelligent Application Laboratory, School of Aeronautics and Astronautics, Sun Yat-sen University",
"update_frequency": "Next year's NDUI is added as soon as it is available.",
"tags": "aws-pds,earth observation,geospatial,urban,satellite imagery",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "nex-gddp-cmip6",
"name": "NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)",
"description": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate\nscenarios derived from the General Circulation Model (GCM) runs\nconducted under the Coupled Model Intercomparison Project Phase 6\n(CMIP6) and across two of the four \"Tier 1\" greenhouse gas emissions\nscenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6\nGCM runs were developed in support of the Sixth Assessment Report\nof the Intergovernmental Panel on Climate Change (IPCC AR6). This\ndataset includes downscaled proje",
"managed_by": "[NASA](https://www.nasa.gov)",
"update_frequency": "No future updates planned.",
"tags": "aws-pds,CMIP6,climate,climate model,model,global,environmental,earth observation,climate projections,netcdf",
"license": "As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive was\ninitially made available through a [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\nas required by the standard data license of the original CMIP6 data.\nIn June 2022, the CMIP6 community updated their underlying li",
"deprecated": false
},
{
"slug": "nexrad-arco",
"name": "NEXRAD ARCO - Analysis-Ready Cloud-Optimized Weather Radar",
"description": "NEXRAD Level II weather radar data converted to FAIR-compliant, analysis-ready cloud-optimized (ARCO) format using Zarr v3 and Icechunk V2. Hierarchically organized by Volume Coverage Pattern (VCP) and sweep, enabling instant time-series access to polarimetric variables (DBZH, ZDR, RHOHV, PHIDP, VELOCITY) without downloading individual files. Currently includes KLOT (Chicago, IL) with continuous updates.",
"managed_by": "[Atmoscale](https://atmoscale.ai/)",
"update_frequency": "New volume scans are added continuously as data becomes available.",
"tags": "aws-pds,earth observation,meteorological,radar,weather,zarr",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered",
"deprecated": false
},
{
"slug": "nifs-lhd",
"name": "NIFS Large Helical Device (LHD) Experiment",
"description": "The Large Helical Device (LHD), owned and operated by the National Institute for Fusion Science (NIFS), is one of the world's largest plasma confinement device which employs a heliotron magnetic configuration generated by the superconducting coils. The objectives are to conduct academic research on the confinement of steady-state, high-temperature, high-density plasmas, core plasma physics, and fusion reactor engineering, which are necessary to develop future fusion reactors. All the archived da",
"managed_by": "[NIFS](https://www.nifs.ac.jp/)",
"update_frequency": "Archived data files are updated nightly when new or revised data are generated in LHD experiment.",
"tags": "analytics,anomaly detection,archives,aws-pds,computed tomography,datacenter,digital assets,electricity,energy,fluid dynamics",
"license": "This data is available for anyone to use under the \"[Rights and Terms](https://www-lhd.nifs.ac.jp/pub/RightsTerms.html)\"",
"deprecated": false
},
{
"slug": "nj-imagery",
"name": "New Jersey Statewide Digital Aerial Imagery Catalog",
"description": "The New Jersey Office of GIS, NJ Office of Information Technology manages a series of 11 digital orthophotography and scanned aerial photo maps collected at various years ranging from 1930 to 2017. Each year’s worth of imagery are available as Cloud Optimized GeoTIFF (COG) files and some years are available as compressed MrSID and/or JP2 files. Additionally, each year of imagery is organized into a tile grid scheme covering the entire geography of New Jersey. Many years share the same tiling g",
"managed_by": "The New Jersey Office of GIS, NJ Office of Information Technology",
"update_frequency": "None",
"tags": "aerial imagery,aws-pds,earth observation,geospatial,imaging,mapping,cog",
"license": "The State of New Jersey provides the service \"as is\". The State makes no guarantee or warranty concerning the accuracy of information contained in the data. Also, the State makes no warranty, either expressed or implied, regarding the condition of the product or its fitness for any particular purpos",
"deprecated": false
},
{
"slug": "nj-lidar",
"name": "New Jersey Statewide LiDAR",
"description": "Elevation datasets in New Jersey have been collected over several years as several\ndiscrete projects. Each project covers a geographic area, which is a subsection of\nthe entire state, and has differing specifications based on the available technology\nat the time and project budget. The geographic extent of one project may overlap that\nof a neighboring project. Each of the 18 projects contains deliverable products such\nas LAS (Lidar point cloud) files, unclassified/classified, tiled to cover pr",
"managed_by": "The New Jersey Office of GIS, NJ Office of Information Technology",
"update_frequency": "None",
"tags": "aws-pds,lidar,elevation,geospatial,mapping",
"license": "The State of New Jersey provides the service \"as is\". The State makes no guarantee or warranty concerning the accuracy of information contained in the data. Also, the State makes no warranty, either expressed or implied, regarding the condition of the product or its fitness for any particular purpos",
"deprecated": false
},
{
"slug": "noaa-arl-hysplit",
"name": "NOAA HYSPLIT-compatible meteorological data archives",
"description": "The HYSPLIT model is a complete system for computing simple air parcel trajectories, as well as complex transport, dispersion, chemical transformation, and deposition simulations. HYSPLIT continues to be one of the most extensively used atmospheric transport and dispersion models in the atmospheric sciences community. A common application is a back trajectory analysis to determine the origin of air masses and establish source-receptor relationships. HYSPLIT has also been used in a variety of sim",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "At present, the following five datasets are updated every day or every week:\n- HRRR 3 km archive (Jun 2019 - present) - daily\n- NAMS Hybrid sigma-pressure archive (2010 - present) - daily\n- NAM 12 km archive (May 2007 - present) - daily\n- GDAS one-degree archive (Dec 2004 - present) - weekly\n- GFS quarter-degree archive (Jun 2019 - present) - daily\n\nThe following two datasets are updated monthly:\n- NCEP/NCAR reanalysis archive (1948 - present) - monthly, typically around the 5th of the following month.\n- Weather research and forecasting model 27 km archive (1980 - present) - monthly, typically with a delay of two to three months.\n\nThe following datasets are no longer updated:\n- HRRR 3 km version 1 archive (Jun 2015 - Jul 2019) - frozen\n- GDAS half-degree archive (Sep 2007 - Jun 2019) - frozen\n- FNL archive (1997 - 2006) - frozen\n- NGM archive (Jan 1991 - Apr 1997) - frozen\n- NAM data assimilation system archive (2004 - 2018) - frozen\n- NAM(Eta) data assimilation system archive (1997 - 2004) - frozen\n- NARR archive (1979 - 2019) - frozen\n",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-bathymetry",
"name": "NOAA National Bathymetric Source Data",
"description": "The National Bathymetric Source (NBS) project creates and maintains \nhigh-resolution bathymetry composed of the best available data. \nThis project enables the creation of next-generation nautical charts \nwhile also providing support for modeling, industry, science, \nregulation, and public curiosity. Primary sources of bathymetry include \nNOAA and U.S. Army Corps of Engineers hydrographic surveys and \ntopographic bathymetric (topo-bathy) lidar (light detection and ranging) \ndata. Data submitted ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Monthly where new data is available.",
"tags": "aws-pds,earth observation,model,oceans,bathymetry,marine navigation,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-cdr-atmospheric",
"name": "NOAA Atmospheric Climate Data Records",
"description": "NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
\nClimate",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.",
"tags": "aws-pds,agriculture,climate,meteorological,sustainability,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-cdr-fundamental",
"name": "NOAA Fundamental Climate Data Records (FCDR)",
"description": "NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
\nClimate",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.",
"tags": "aws-pds,agriculture,climate,meteorological,sustainability,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-cdr-oceanic",
"name": "NOAA Oceanic Climate Data Records",
"description": "NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
\nClimate",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.",
"tags": "aws-pds,agriculture,climate,meteorological,oceans,sustainability,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-cdr-terrestrial",
"name": "NOAA Terrestrial Climate Data Records",
"description": "NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
\nClimate",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.",
"tags": "aws-pds,agriculture,climate,meteorological,sustainability,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-cfs",
"name": "NOAA Climate Forecast System (CFS)",
"description": "The Climate Forecast System (CFS) is a model representing the global interaction between Earth's oceans, land, and atmosphere. Produced by several dozen scientists under guidance from the National Centers for Environmental Prediction (NCEP), this model offers hourly data with a horizontal resolution down to one-half of a degree (approximately 56 km) around Earth for many variables. CFS uses the latest scientific approaches for taking in, or assimilating, observations from data sources including ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Hourly, 6-Hourly, and Monthly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-climate-normals",
"name": "NOAA U.S. Climate Normals",
"description": "The U.S. Climate Normals are a large suite of data products that provide information about typical climate conditions for thousands of locations across the United States. Normals act both as a ruler to compare today’s weather and tomorrow’s forecast, and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climat",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Data is updated on 10 year cycles or when corrections are implemented.",
"tags": "aws-pds,agriculture,climate,meteorological,sustainability,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-coastal-lidar",
"name": "NOAA Coastal Lidar Data",
"description": "Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of ",
"managed_by": "[NOAA](https://www.noaa.gov/)",
"update_frequency": "Periodically, as new data becomes available",
"tags": "aws-pds,climate,elevation,disaster response,geospatial,lidar,stac",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-cris-hist",
"name": "NOAA nClimGrid and Livneh Gridded Historical Climate Observation Thresholds",
"description": "Livneh and nClimGrid are gridded observed historical climatology data that were used in the LOCA2 and STAR-ESDM downscaling process of global climate models as part of the 5th National Climate Assessment. The original Livneh and nClimGrid daily temperature and precipitation observations have been converted to a series of decision-relevant thresholds as part of the [(U.S. Climate Resilience Information System (CRIS))](https://cris.climate.gov/pages/about). These thresholds, such as days with extr",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "None\n",
"tags": "aws-pds,agriculture,climate,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-dcdb-bathymetry-pds",
"name": "Crowdsourced Bathymetry",
"description": "Community provided bathymetry data collected in collaboration with the International Hydrographic Organization.",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "New data is added once a week.",
"tags": "aws-pds,earth observation,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-eri",
"name": "NOAA Emergency Response Imagery",
"description": "In order to support NOAA's homeland security and emergency response requirements, the National Geodetic Survey Remote Sensing Division (NGS/RSD) has the capability to acquire and rapidly disseminate a variety of spatially-referenced datasets to federal, state, and local government agencies, as well as the general public. Remote sensing technologies used for these projects have included lidar, high-resolution digital cameras, a film-based RC-30 aerial camera system, and hyperspectral imagers. Exa",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Manually when needed",
"tags": "aws-pds,aerial imagery,climate,cog,disaster response,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-gefs-reforecast",
"name": "NOAA Global Ensemble Forecast System (GEFS) Re-forecast",
"description": "NOAA has generated a multi-decadal reanalysis and reforecast data set to accompany the next-generation version of its ensemble prediction system, the Global Ensemble Forecast System, version 12 (GEFSv12). Accompanying the real-time forecasts are “reforecasts” of the weather, that is, retrospective forecasts spanning the period 2000-2019. These reforecasts are not as numerous as the real-time data; they were generated only once per day, from 00 UTC initial conditions, and only 5 members were prov",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Re-forecasts do not adhere to an update frequency.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-gefs",
"name": "NOAA Global Ensemble Forecast System (GEFS)",
"description": "The Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutel",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "4 times a day, every 6 hours starting at midnight.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-gestofs",
"name": "NOAA Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global)",
"description": "\nNOTICE - The Coast Survey Development Laboratory (CSDL) in NOAA/National Ocean Service (NOS)/Office of Coast Survey has upgraded the Surge and Tide Operational Forecast System (STOFS, formerly ESTOFS) to Version 2.1. A Service Change Notice (SCN) has been issued and can be found \"[HERE](https://www.weather.gov/media/notification/pdf_2023_24/scn23-124_stofs_upgrade_v2.1.4_aaf.pdf)\"
\n\nNOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global) provides users with now",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Four times per day, every 6 hours starting at midnight UTC",
"tags": "aws-pds,global,coastal,disaster response,weather,water,environmental,meteorological,oceans,climate",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-gfs-bdp-pds",
"name": "NOAA Global Forecast System (GFS)",
"description": "NOTE - Upgrade NCEP Global Forecast System to v16.3.0 - Effective November 29, 2022 See notification [HERE](https://www.weather.gov/media/notification/pdf2/scn22-104_gfs.v16.3.0.pdf)\n
\n
\nThe Global Forecast System (GFS) is a weather forecast model produced\nby the National Centers for Environmental Prediction (NCEP). Dozens of\natmospheric and land-soil variables are available through this dataset,\nfrom temperatures, winds, and precipitation to soil moisture and\natmospheric ozone concentra",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "4 times a day, every 6 hours starting at midnight UTC",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-gfs-pds",
"name": "Unidata NOAA Global Forecast System (GFS) Model",
"description": "The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The entire globe is covered by the GFS at a base horizontal resolution of 18 miles (28 kilometers) between grid points, which is used by the operational forecasters who predict weather out ",
"managed_by": "[Unidata](https://www.unidata.ucar.edu/)",
"update_frequency": "Every 6 hours",
"tags": "aws-pds,agriculture,climate,weather,environmental,disaster response",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": true
},
{
"slug": "noaa-ghcn",
"name": "NOAA Global Historical Climatology Network Daily (GHCN-D)",
"description": "
UPDATE TO GHCN PREFIXES - The NODD team is working on improving performance and access to the GHCNd data and will be implementing an updated prefix structure. For more information on the prefix changes, please see the \"[READ ME on the NODD Github](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/GHCN-D)\". If you have questions, comments, or feedback, please reach out to nodd@noaa.gov with GHCN in the subject line.
\nGlobal Historical Climatology Network - Daily ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of",
"deprecated": false
},
{
"slug": "noaa-ghe",
"name": "NOAA Global Hydro Estimator (GHE) / Enterprise Rain Rate",
"description": "NOTE - The legacy on-premises version of the Global Hydroestimator (GHE) is being retired. It is being replaced by the global Enterprise Rain Rate algorithm. You can find Enterprise Rain Rate products in the new bucket listed under the Resources section. \n
\n
\nGlobal Hydro-Estimator provides a global mosaic imagery of rainfall estimates from multi-geostationary satellites, which currently includes GOES-16, GOES-15, Meteosat-8, Meteosat-11 and Himawari-8. The GHE products include: Instanta",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "15 minute-instantaneous",
"tags": "aws-pds,agriculture,meteorological,water,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-gk2a-pds",
"name": "Korea Meteorological Administration (KMA) GK-2A Satellite Data",
"description": "The Geo-KOMPSAT-2A (GK2A) is the new generation geostationary meteorological satellite (located in 128.2°E) of the Korea Meteorological Administration (KMA). The main mission of the GK2A is to observe the atmospheric phenomena over the Asia-Pacific region. The Advance Meteorological Imager (AMI) on GK2A scan the Earth full disk every 10 minutes and the Korean Peninsula area every 2 minutes with a high spatial resolution of 4 visible channels and 12 infrared channels. In addition, the AMI has an ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared channels.",
"tags": "aws-pds,agriculture,geospatial,weather,earth observation,meteorological,disaster response,satellite imagery",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-gmgsi",
"name": "NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI)",
"description": "NOAA/NESDIS Global Mosaic of Geostationary Satellite Imagery (GMGSI) visible (VIS), shortwave infrared (SIR), longwave infrared (LIR) imagery, and water vapor imagery (WV) are composited from data from several geostationary satellites orbiting the globe, including the GOES-East and GOES-West Satellites operated by U.S. NOAA/NESDIS, the Meteosat-10 and Meteosat-9 satellites from theMeteosat Second Generation (MSG) series of satellites operated by European Organization for the Exploitation of Mete",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Hourly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-goes",
"name": "NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17, 18 & 19",
"description": "
\n
\nNEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-op",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "New data is added as soon as it's available",
"tags": "aws-pds,agriculture,geospatial,weather,earth observation,meteorological,disaster response,satellite imagery",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-gsod",
"name": "NOAA Global Surface Summary of Day",
"description": "Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typicall",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Data is updated as new products are available.",
"tags": "aws-pds,agriculture,environmental,climate,weather,natural resource,regulatory",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-himawari",
"name": "JMA Himawari-8/9",
"description": "Himawari-9, stationed at 140.7E, owned and operated by the Japan Meteorological Agency (JMA), is a geostationary meteorological satellite, with Himawari-8 as on-orbit back-up, that provides constant and uniform coverage of east Asia, and the west and central Pacific regions from around 35,800 km above the equator with an orbit corresponding to the period of the earth’s rotation. This allows JMA weather offices to perform uninterrupted observation of environmental phenomena such as typhoons, volc",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes for Regions 4 and 5.",
"tags": "aws-pds,agriculture,geospatial,weather,earth observation,meteorological,disaster response,satellite imagery",
"license": "Himawari data is produced and managed by JMA. NOAA has rights to distribute this data freely and openly to the public. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA and JMA request attribution fo",
"deprecated": false
},
{
"slug": "noaa-historicalcharts",
"name": "NOAA Historical Maps and Charts",
"description": "Historical Charts are not for Navigation. The collection primarily consists of historic charts and maps produced by NOAA's Coast Survey and its predecessors, especially the U.S. Coast and Geodetic Survey and the U.S. Lake Survey (previously under the Department of War). The collection also includes bathymetric maps, land sketches, Civil War battle maps, aeronautical charting from the 1930s to the 1950s, and other drawings and photographs.",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Periodic manual updates when historic charts are added to the collection.",
"tags": "aws-pds,history,mapping,coastal,geospatial,survey",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-hrrr-pds",
"name": "NOAA High-Resolution Rapid Refresh (HRRR) Model",
"description": "The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.
The HRRR ZARR formatted data was originally generated by the University of Utah under a grant provided by NOAA. They are are conti",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Hourly",
"tags": "aws-pds,agriculture,climate,weather,environmental,disaster response",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-isd",
"name": "NOAA Integrated Surface Database (ISD)",
"description": "The Integrated Surface Database (ISD) consists\nof global hourly and synoptic observations\ncompiled from numerous sources into a gzipped\nfixed width format. ISD was developed as a joint\nactivity within Asheville's Federal Climate\nComplex. The database includes over 35,000 stations\nworldwide, with some having data as far back\nas 1901, though the data show a substantial\nincrease in volume in the 1940s and again in\nthe early 1970s. Currently, there are over\n14,000 \"active\" stations updated daily in ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-jpss",
"name": "NOAA Joint Polar Satellite System (JPSS)",
"description": "*Near Real Time JPSS data is now flowing! See bucket information on the right side of this page to access products!*\n\n
Satellites in the JPSS constellation gather global measurements of atmospheric, terrestrial and oceanic conditions, including sea and land surface temperatures, vegetation, clouds, rainfall, snow and ice cover, fire locations and smoke plumes, atmospheric temperature, water vapor and ozone. JPSS delivers key observations for the Nation's essential products and services, i",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "New data is added as soon as it's available",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-jscope",
"name": "NOAA JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE)",
"description": "J-SCOPE (JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem) is funded by NOAA and presented by NANOOS. This project aims to provide experimental seasonal forecasts (six to nine months) of upper ocean properties, based on operational simulations by NOAA's Climate Forecast System (CFS) model, and dynamical downscaling with a high-resolution version of the Regional Ocean Model System (ROMS) that includes a state-of-the-art biogeochemical module. Forecasts of specific oceanic properties cru",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Quarterly. Updated in February, May and October with January, April, and September initialized forecast products respectively. We also do a year in review when the historical fields will be available and this is typically in January or February of the year following its occurrence.",
"tags": "aws-pds,climate,coastal,chemistry,marine,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-mrms-pds",
"name": "NOAA Multi-Radar/Multi-Sensor System (MRMS)",
"description": "The MRMS system was developed to produce severe weather, transportation, and precipitation products for improved decision-making capability to improve hazardous weather forecasts and warnings, along with hydrology, aviation, and numerical weather prediction.\n
\n
\nMRMS is a system with fully-automated algorithms that quickly and intelligently integrate data streams from multiple radars, surface and upper air observations, lightning detection systems, satellite observations, and forecast mo",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Data is delivered in real-time with a 2-minute update cycle.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nam",
"name": "NOAA North American Mesoscale Forecast System (NAM)",
"description": "The North American Mesoscale Forecast System (NAM) is one of the National Centers For Environmental Prediction’s (NCEP) major models for producing weather forecasts. NAM generates multiple grids (or domains) of weather forecasts over the North American continent at various horizontal resolutions. Each grid contains data for dozens of weather parameters, including temperature, precipitation, lightning, and turbulent kinetic energy. NAM uses additional numerical weather models to generate high-res",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Four times daily (0000, 0600, 1200, and 1800 UTC)",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-nbm-parallel",
"name": "NOAA National Blend of Models (NBM) Parallel",
"description": "The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. This dataset contains data from the current parallel version of the NBM which is a test version, featuring many changes, that is a candidate to be impleme",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Once per hour.\n",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nbm",
"name": "NOAA National Blend of Models (NBM)",
"description": "The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. \n",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Once per hour",
"tags": "aws-pds,agriculture,climate,cog,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-nclimgrid",
"name": "NOAA U.S. Climate Gridded Dataset (NClimGrid)",
"description": "The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On an annual basis, approximately one year of \"final\" nClimGrid will be submitted to replace the initially supplied \"preliminary\" data for the same time perio",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Monthly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ncn",
"name": "NOAA Continuously Operating Reference Stations (CORS) Network (NCN)",
"description": "The [NOAA Continuously Operating Reference Stations (CORS) Network (NCN)](https://geodesy.noaa.gov/CORS/), managed by NOAA/National Geodetic Survey ([NGS](https://geodesy.noaa.gov/)), provide Global Navigation Satellite System (GNSS) data, supporting three dimensional positioning, meteorology, space weather, and geophysical applications throughout the United States. The NCN is a multi-purpose, multi-agency cooperative endeavor, combining the efforts of hundreds of government, academic, and priva",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Most data are available within 1 hour from when they were recorded at the remote site, and a few sites have a delay of 24 hours.",
"tags": "aws-pds,broadcast ephemeris,Continuously Operating Reference Station (CORS),earth observation,geospatial,GPS,GNSS,mapping,NOAA CORS Network (NCN),post-processing",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ndfd",
"name": "NOAA National Digital Forecast Database (NDFD)",
"description": "Please note NWS is Soliciting Comments until April 30, 2024 on Availability of Probabilistic Snow Grids for Select Weather Forecast Offices (WFOs) as an Experimental Element in the National Digital Forecast Database (NDFD) for the Contiguous United States (CONUS). A PDF version of the Public Notice can be found \"[HERE](https://www.weather.gov/media/notification/pdf2/pns19-02_prob_snow_aad.pdf)\"\n
\n
\nThe National Digital Forecast Database (NDFD) is a suite of gridded forecasts of sensibl",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "As often as once every half hour (varies by forecast element, forecast projection, and domain)",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-nesdis-swfo-ccor-1-pds",
"name": "NOAA Space Weather Follow-On Mission Geostationary Operational Environmental Satellite (GOES) 19",
"description": "
\nThe National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite 19 (GOES-19) is the fourth and final satellite in the\nGeostationary Operational Environmental Satellites (GOES) – R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental monitoring \nsystem.\n
\nThe GOES-R Series provides advanced imagery and atmospheric measurements, real-time mapping of lightning activity, and space weather observations. As a",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "New data is added as soon as it's available",
"tags": "aws-pds,space weather,satellite imagery",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nesdis-tcprimed-pds",
"name": "Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)",
"description": "The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Dataset (TC PRIMED) is a dataset centered around passive microwave observations of global tropical cyclones from low-Earth-orbiting satellites. TC PRIMED is a compilation of tropical cyclone data from various sources, including 1) tropical cyclone information from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service National Hurricane Center (NHC) and Central Pacific Hurricane Center (CPHC) a",
"managed_by": "[CIRA](https://www.cira.colostate.edu/)",
"update_frequency": "Annually for the final version, several months after the conclusion of the Northern Hemisphere tropical cyclone season and daily for the preliminary version, several days after the dissipation of a tropical cyclone.",
"tags": "atmosphere,aws-pds,earth observation,environmental,geophysics,geoscience,global,meteorological,model,netcdf",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nexrad",
"name": "NEXRAD on AWS",
"description": "Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network.\n
\n
Update
\nThe NEXRAD Level II archive data is moving to a new bucket: unidata-nexrad-level2 \nand SNS topic: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive. The old\nbucket and SNS topic are now deprecated and will no longer be available starting September 1, 2025.\n
\n",
"managed_by": "[Unidata](https://www.unidata.ucar.edu/)",
"update_frequency": "New Level II data is added as soon as it is available.",
"tags": "aws-pds,agriculture,earth observation,natural resource,weather,meteorological",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nmme",
"name": "NOAA North American Multi-Model Ensemble (NMME)",
"description": "The North American Multi-Model Ensemble (NMME) is an experimental multi-model seasonal forecasting system consisting of coupled models from US modeling centers including NOAA/NCEP, NOAA/GFDL, NCAR, NASA, and Canada's ECCC.\n
\nThe need for the development of NMME operational predictive capability was recommended in US National Academies report \"Assessment of Intraseasonal to Interannual Climate Prediction and Predictability\". Indeed, the national effort is required to meet the specific tai",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Monthly",
"tags": "aws-pds,climate,weather,meteorological",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-nodd-kerchunk",
"name": "NOAA Cloud Optimized Zarr Reference Files (Kerchunk)",
"description": "This repository contains references to datasets published to the NOAA Open Data Dissemination Program. These reference datasets serve as index files to the original data by mapping to the [Zarr V2 specification](https://zarr-specs.readthedocs.io/en/latest/v2/v2.0.html). When multidimensional model output is read through zarr, data can be lazily loaded (i.e. retrieving only the data chunks needed for processing) and data reads can be scaled horizontally to optimize object storage read performance",
"managed_by": "[NOAA's National Ocean Service, the Integrated Ocean Observing System (IOOS)](https://ioos.noaa.gov/)",
"update_frequency": "Optimizations run every time new data is uploaded to the source buckets and are available here within minutes.",
"tags": "aws-pds,climate,coastal,disaster response,environmental,meteorological,oceans,water,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-nos-cora",
"name": "NOAA's Coastal Ocean Reanalysis (CORA) Dataset: 1979-2022",
"description": "NOAA's [Coastal Ocean Reanalysis (CORA)](https://tidesandcurrents.noaa.gov/cora.html) for the Gulf, East Coast/Atlantic, and Caribbean (GEC) is produced using verified hourly water levels from the National Ocean Service’s [Center of Operational Oceanographic Products & Services](https://tidesandcurrents.noaa.gov/) (CO-OPS). [ADvanced CIRCulation Model (ADCIRC)](https://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/476698/advanced-circulation-model/) and [Simulating W",
"managed_by": "[NOAA’s National Ocean Service, The Center for Operational Oceanographic Products and Services (CO-OPS)](https://tidesandcurrents.noaa.gov/about_us.html)\n",
"update_frequency": "Product dependent. At minimum, annually.",
"tags": "aws-pds,agriculture,weather,climate,environmental,disaster response,agriculture,transportation,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.\n\nNOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltere",
"deprecated": false
},
{
"slug": "noaa-nos-scuba-icesat2-pds",
"name": "NOAA / NGA Satellite Computed Bathymetry Assessment-SCuBA",
"description": "One of the National Geospatial-Intelligence Agency’s (NGA) and the National Oceanic and Atmospheric Administration’s (NOAA) missions is to ensure the safety of navigation on the seas by \nmaintaining the most current information and the highest quality services for U.S. and global transport networks. To achieve this mission, we need accurate coastal bathymetry over diverse \nenvironmental conditions. The SCuBA program focused on providing critical information to improve existing bathymetry resourc",
"managed_by": "[NOAA’s National Geodetic Survey](https://geodesy.noaa.gov/)",
"update_frequency": "Monthly, quarterly, and annually, depending on the dataset.",
"tags": "aws-pds,bathymetry,agriculture,weather,climate,environmental,disaster response,agriculture,transportation,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nos-stofs3d",
"name": "NOAA 3-D Surge and Tide Operational Forecast System for the Atlantic Basin (STOFS-3D-Atlantic)",
"description": "\nNOTICE - The Coast Survey Development Laboratory (CSDL) in NOAA/National Ocean Service (NOS)/Office of Coast Survey is upgrading the Surge and Tide Operational Forecast System (STOFS, formerly ESTOFS) to Version 2.1. A Service Change Notice (SCN) has been issued and can be found \"[HERE](https://www.weather.gov/media/notification/pdf_2023_24/scn23-124_stofs_upgrade_v2.1.4_aaf.pdf)\"
\n\n NOAA's Surge and Tide Operational Forecast System: Three-Dimensional Component for the Atlantic Basin",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "One time per day at 12 UTC",
"tags": "aws-pds,global,coastal,marine navigation,disaster response,weather,water,environmental,meteorological,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nwm-pds",
"name": "NOAA National Water Model Short-Range Forecast",
"description": "The National Water Model (NWM) is a water resources model that simulates and forecasts water\nbudget variables, including snowpack, evapotranspiration, soil moisture and streamflow, over\nthe entire continental United States (CONUS). The model, launched in August 2016, is designed\nto improve the ability of NOAA to meet the needs of its stakeholders (forecasters, emergency\nmanagers, reservoir operators, first responders, recreationists, farmers, barge operators, and\necosystem and floodplain manager",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,weather,climate,environmental,disaster response,agriculture,transportation",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nws-aorc",
"name": "NOAA Analysis of Record for Calibration (AORC) Dataset",
"description": "The Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas. It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and s",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "To be determined",
"tags": "aws-pds,agriculture,weather,climate,environmental,disaster response,agriculture,transportation",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-nws-fourcastnetgfs",
"name": "(EXPERIMENTAL) NOAA FourCastNet Global Forecast System (FourCastNetGFS) (EXPERIMENTAL)",
"description": "The FourCastNet Global Forecast System (FourCastNetGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The model runs on a 0.25 degree latitude-longitude grid (about 28 km) and 13 pressure levels. The model produces forecasts 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, relative humidity and 2 meter temperature",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "4 times a day at 00Z, 06Z, 12Z and 18Z",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA's FourCastNetGFS products are released under CC0 license. The underlying FourCastNet code package follows Apache-2.0 license as specified in the ECMWF’s ai-models-fourcastnetv2 github page. The products in this bucket only contain NOAA produced products and not FourCastNet code or training data",
"deprecated": false
},
{
"slug": "noaa-nws-graphcastgfs-pds",
"name": "NOAA EAGLE (Experimental AI Global and Limited-Area Ensemble) Global Deterministic and Ensemble Forecasts",
"description": "Update
\nEffective on December 17, 2025, the NOAA/NWS National Centers for Environmental Prediction (NCEP) implemented three new models: the Artificial Intelligence Global Forecast System (AIGFS), the Artificial Intelligence Global Ensemble Forecast System (AIGEFS), and the Hybrid Global Ensemble Forecast System (HGEFS). The AIGFS/AIGEFS are the operational replacement for EAGLE SOLO/Ense",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "4 times a day at 00Z, 06Z, 12Z and 18Z",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA's GraphCast GFS products are released under CC0 license. The products in this bucket only contain NOAA produced products and not GraphCast code or training data. For information on the GraphCast code package, please visit the [Google DeepMind GraphCast Github License page](https://github.com/go",
"deprecated": false
},
{
"slug": "noaa-nws-hafs",
"name": "NOAA Hurricane Analysis and Forecast System (HAFS)",
"description": "The last several hurricane seasons have been active with records being set for the number of tropical storms and hurricanes in the Atlantic basin. These record-breaking seasons underscore the importance of accurate hurricane forecasting. Imperative to increased forecasting skill for hurricanes is the development of the Hurricane Forecast Analysis System or HAFS. To accelerate improvements in hurricane forecasting, this project has the following goals:\n
\n
\n1) To improve the HAFS. The",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Event Driven.
As of August 2025, a few forecast cycles for Hurricane Fiona (07L) and Hurricane Nicole (17L) from the 2022 Atlantic hurricane season have been made available. These files can be found under the subdirectories of hfsa_retro and hfsb_retro. Additional forecast files from the 2022 hurricane season can be made available upon user request.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nws-naqfc-pds",
"name": "NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance",
"description": "The National Air Quality Forecasting Capability (NAQFC) dataset contains model-generated air quality (AQ) forecast guidance from three different prediction systems. The first system is a coupled weather and atmospheric chemistry numerical forecast model, known as the Air Quality Model (AQM). It is used to produce forecast guidance for ozone (O3) and particulate matter that is less than or equal to 2.5 micrometers in diameter (PM2.5). Prior to May 14, 2024, AQM predictions were derived using the ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Two times per day, 0600 and 1200 UTC for O3, PM2.5, and dust; once per day, 0300 UTC for smoke",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-nws-wam-ipe",
"name": "NOAA Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS)",
"description": "The coupled Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS) is developed and maintained by the NOAA Space Weather Prediction Center (SWPC). The WAM-IPE model provides a specification of ionosphere and thermosphere conditions with real-time nowcasts and forecasts up to two days in advance in response to solar, geomagnetic, and lower atmospheric forcing. The WAM is an extension of the Global Forecast System (GFS) with a spectral hydrostatic dynamical ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "The update frequencies of the WAM-IPE dataset range from 10 minutes to 6 hours depending on the CONOPS.",
"tags": "aws-pds,climate,meteorological,solar,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-oar-arl-nacc-pds",
"name": "NOAA Global Forecast System (GFS) netCDF Formatted Data",
"description": "The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The GFS data files stored here can be immediately used for [OAR/ARL’s NOAA-EPA Atmosphere-Chemistry Coupler Cloud (NACC-Cloud) tool](https://www.arl.noaa.gov/research/surface-atmosphere-exc",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/yyyymmdd) for the 12Z cycle and includes hourly output out to 24 hours for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc). The data is uploaded daily at ~ 1800 UTC.
The climatological GLDSs (including vegetative canopy data variables) are updated on an as-needed basis when new sources, methods, and advancements become available.",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-oar-hourly-gdp",
"name": "NOAA - hourly position, current, and sea surface temperature from drifters",
"description": "This dataset includes *hourly* sea surface temperature and current data collected by satellite-tracked surface drifting buoys (\"drifters\") of the [NOAA Global Drifter Program](https://www.aoml.noaa.gov/phod/gdp). The [Drifter Data Assembly Center (DAC) at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML)](https://www.aoml.noaa.gov/phod/gdp/data.php) has applied quality control procedures and processing to edit these observational data and obtain estimates at regular hourly inter",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "New data is added as soon as it's available.",
"tags": "aws-pds,climate,meteorological,sustainability,weather,oceans,environmental",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-oar-myrorss-pds",
"name": "NOAA Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS)",
"description": "The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) consists of radar reflectivity data run through the Multi-Radar, Multi-Sensor (MRMS) framework to create a three-dimensional radar volume on a quasi-Cartesian latitude-longitude grid across the entire contiguous United States. The radar reflectivity grid is also combined with hourly forecast model analyses to produce derived products such as echo top heights and hail size estimates. Radar Doppler velocity data was also processed into ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "No updates",
"tags": "aws-pds,agriculture,earth observation,meteorological,natural resource,sustainability,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ocean-climate-stations",
"name": "NOAA/PMEL Ocean Climate Stations Moorings",
"description": "The mission of the Ocean Climate Stations (OCS) Project is to make meteorological and \noceanic measurements from autonomous platforms. Calibrated, quality-controlled, and well-documented \nclimatological measurements are available on the OCS webpage and the OceanSITES Global Data\nAssembly Centers (GDACs), with near-realtime data available prior to release of the complete, \ndownloaded datasets.
\n\nOCS measurements served through the Big Data Program come from OCS high-latitude moored buoy",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "KEO and Papa data on BDP are synchronized with the OceanSITES Global Data Assembly Center periodically.",
"tags": "aws-pds,climate,environmental,oceans,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ocs-hydrodata",
"name": "NOAA Office of Coast Survey - Hydrographic Survey Data",
"description": "Founded in 1807, NOAA’s Office of Coast Survey is the nation’s first scientific agency and today is responsible for supporting nearly $5.4 trillion in economic activity through providing advanced marine navigation services. The Office of Coast Survey collects and qualifies hydrographic, bathymetric, and topographic data, from NOAA platforms and many other data providers. These data and associated deliverables are posted here for various users to access, including but not limited to the \"[Nationa",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "This bucket is updated as new data source become available",
"tags": "aws-pds,earth observation,model,oceans,bathymetry,marine navigation,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ofs",
"name": "NOAA Operational Forecast System (OFS)",
"description": "ANNOUNCEMENTS: [NOS OFS Version Updates and Implementation of Upgraded Oceanographic Forecast Modeling Systems for Lakes Superior and Ontario; Effective October 25, 2022}(https://www.weather.gov/media/notification/pdf2/scn22-91_nos_loofs_lsofs_v3.pdf)\n
\n
\nFor decades, mariners in the United States have depended on NOAA's Tide Tables for the best estimate of expected water levels. These tables provide accurate predictions of the astronomical tide (i.e., the change in water level due to th",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day with 24-hour nowcast) and 48- to 120-hour forecast guidance.",
"tags": "aws-pds,climate,coastal,disaster response,environmental,meteorological,oceans,water,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-rap",
"name": "NOAA Rapid Refresh (RAP)",
"description": "The Rapid Refresh (RAP) is a NOAA/NCEP operational weather prediction system comprised primarily of a numerical forecast model and analysis/assimilation system to initialize that model. It covers North America and is run with a horizontal resolution of 13 km and 50 vertical layers. The RAP was developed to serve users needing frequently updated short-range weather forecasts, including those in the US aviation community and US severe weather forecasting community. The model is run for every hour ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Hourly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-reanalyses-pds",
"name": "NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis",
"description": "The NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis is a curated joint observation archive containing Earth system data from 1979 to present prepared by teams at NOAA's Physical Sciences Laboratory and NASA's Global Modeling and Assimilation Office. The goal is to foster collaboration across organizations and develop the ability for direct comparison of Earth System reanalysis results. Providing a singular dataset for observation input use will allow reanalyses to be c",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "1 time a day",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-rrfs",
"name": "NOAA Rapid Refresh Forecast System (RRFS) [Prototype]",
"description": "The Rapid Refresh Forecast System (RRFS) is the National Oceanic and Atmospheric Administration’s (NOAA) next generation convection-allowing, rapidly-updated ensemble prediction system, currently scheduled for operational implementation in 2026. The operational configuration will feature a 3 km grid covering North America and include deterministic forecasts every hour out to 18 hours, with deterministic and ensemble forecasts to 60 hours four times per day at 00, 06, 12, and 18 UTC.The RRFS will",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\\), which is well-known and internationally recognized. There are no restrictions on the use of",
"deprecated": false
},
{
"slug": "noaa-rtma",
"name": "NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA)",
"description": "The Real-Time Mesoscale Analysis (RTMA) is a NOAA National Centers For Environmental Prediction (NCEP) high-spatial and temporal resolution analysis/assimilation system for near-surf ace weather conditions. Its main component is the NCEP/EMC Gridpoint Statistical Interpolation (GSI) system applied in two-dimensional variational mode to assimilate conventional and satellite-derived observations.
\nThe RTMA was developed to support NDFD operations and provide field forecasters with high qu",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Hourly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-rtofs",
"name": "NOAA Global Real-Time Ocean Forecast System (Global RTOFS)",
"description": "NOAA is soliciting public comment on petential changes to the Real Time Ocean Forecast System (RTOFS) through March 27, 2024. Please see Public Notice at (https://www.weather.gov/media/notification/pdf_2023_24/pns24-12_rtofs_v2.4.0.pdf)
\n\nNOAA's Global Real-Time Ocean Forecast System (Global RTOFS) provides users with nowcasts (analyses of near present conditions) and forecast guidance up to eight days of ocean temperature and salinity, water velocity, sea surface elevation, sea ice c",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Once a day. The products are available at the following times each day
00z - Analysis and analysis post-processing
06z - forecast days 1-4, post-processing for most of these days
12z - forecast days 5-8, post-processing for these days and some of forecast days 1-4.
",
"tags": "aws-pds,global,coastal,disaster response,weather,water,environmental,meteorological,oceans,climate",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-s102",
"name": "NOAA S-102 Bathymetric Surface Data",
"description": "S-102 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s100-project), an international standard for hydrographic data exchange. This collection of data contains bathymetric surfaces from [NOAA/NOS/OCS National Bathymetric Source](https://nauticalcharts.noaa.gov/updates/building-the-national-bathymetry/), for various U.S. coastal and offshore waters and the great lakes. These datasets are encoded as HDF5 files conformin",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Irregular, updated when new data is available.",
"tags": "aws-pds,bathymetry,marine navigation,hydrography,oceans,seafloor,water",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-s104",
"name": "NOAA S-104 Water Level Data",
"description": "S-104 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s-100-universal-hydrographic-data-model), an international standard for hydrographic data. This collection of data contains water level forecast guidance from [NOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global)](https://polar.ncep.noaa.gov/estofs/), an operational hydrodynamic nowcast and forecast modeling system for global water level c",
"managed_by": "\"[NOAA](http://www.noaa.gov/)\"\n",
"update_frequency": "Static\n",
"tags": "aws-pds,marine navigation,hydrography,oceans,coastal,water",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.\nNOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered",
"deprecated": false
},
{
"slug": "noaa-s111",
"name": "NOAA S-111 Surface Water Currents Data",
"description": "S-111 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s100-project), an international standard for hydrographic data. This collection of data contains surface water currents forecast guidance from [NOAA/NOS Operational Forecast Systems](https://tidesandcurrents.noaa.gov/models.html), a set of operational hydrodynamic nowcast and forecast modeling systems, for various U.S. coastal waters and the great lakes. The collec",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Four times per day for each OFS, except Global RTOFS which updates once per day",
"tags": "aws-pds,oceans,water",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-space-weather",
"name": "NOAA Space Weather Forecast and Observation Data",
"description": "Space weather forecast and observation data is collected and disseminated by NOAA’s Space Weather Prediction Center (SWPC) in Boulder, CO. SWPC produces forecasts for multiple space weather phenomenon types and the resulting impacts to Earth and human activities. A variety of products are available that provide these forecast expectations, and their respective measurements, in formats that range from detailed technical forecast discussions to NOAA Scale values to simple bulletins that give info",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "The update frequencies of the space weather dataset range from one minute observations to daily and monthly updates of more slowly-varying indices",
"tags": "aws-pds,climate,meteorological,solar,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-swdi",
"name": "NOAA Severe Weather Data Inventory (SWDI)",
"description": "The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce. Rare, unusual, ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Monthly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-ufs-coastal-pds",
"name": "NOAA Unified Forecast System (UFS) Coastal Model",
"description": "The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The [UFS Coastal application](https://github.com/oceanmodeling/ufs-coastal-app) is a project under development by NOAA and NCAR, which supports coastal forecasting requirements based on UFS standards. The coupling infrastructure for UFS Coastal App is currently being developed based on a fork of the ufs-weather-model (UFS-WM), with additional coastal model-components including SCHISM, ADCIRC, RO",
"managed_by": "[NOAA](https://www.noaa.gov/)",
"update_frequency": "Periodically, as new data becomes available",
"tags": "aws-pds,climate,elevation,disaster response,geospatial,lidar,stac",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-gdas-pds",
"name": "NOAA Global Data Assimilation (DA) Test Data",
"description": "The [Unified Forecast System (UFS)](https://ufs.epic.noaa.gov/) is a community-based, coupled, comprehensive Earth Modeling System. It supports [multiple applications](https://ufs.epic.noaa.gov/applications/) with different forecast durations and spatial domains. The [Global Data Assimilation System (GDAS)](https://github.com/NOAA-EMC/GDASApp) Application (App) is being used as the basis for uniting the [Global Workflow](http://NOAA-EMC/global-workflow) and Global Forecast System (GFS) model wit",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "These are stable datasets for use with global DA projects. They will be updated as new sample cases are added.",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-gefsv13replay-pds",
"name": "NOAA Unified Forecast System (UFS) Global Ensemble Forecast System (GEFS) Version 13 Replay",
"description": "The NOAA Unified Forecast System (UFS) / Global Ensemble Forecast System version 13 (GEFSv13) Replay dataset supports the retrospective forecast archive in preparation for GEFSv13 / GFSv17. It includes a range of atmospheric and oceanic variables—such as temperature, humidity, winds, salinity, and currents—covering global conditions at a nominal horizontal resolution of ¼ degree, enabling detailed weather analysis.\n
\n
\nThe dataset was generated by replaying the coupled UFS model against ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Static",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-htf-pds",
"name": "NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF)",
"description": "The \"[Unified Forecast System](https://ufs.epic.noaa.gov/)\" (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The Hierarchical Testing Framework (HTF) serves as a comprehensive toolkit designed to enhance the testing capabilities within UFS \"[repositories](https://github.com/ufs-community)\". It aims to standardize and simplify the testing process across various \"[UFS Weather Model](https://github.com/ufs-community/ufs-weather-model)\" (WM) components and associated modules",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "The UFS HTF is in its prototype phase and will be updated as new case studies are added.",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-land-da",
"name": "NOAA Unified Forecast System (UFS) Land Data Assimilation (DA) System",
"description": "The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. It supports \"[multiple applications](https://ufscommunity.org/science/aboutapps/)\" covering different forecast durations and spatial domains. The Land Data Assimilation (DA) System is an offline version of the Noah Multi-Physics (Noah-MP) land surface model (LSM) used in the UFS Weather Model (WM). Its data assimilation framework uses \"[Joint Effort for Data assimilation Integration - JEDI] (htt",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "These are stable datasets for use with the Land DA System. They will not be updated frequently. Additional data may be added with each subsequent Land DA release.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-marinereanalysis",
"name": "NOAA Unified Forecast System (UFS) Marine Reanalysis: 1979-2019",
"description": "The NOAA UFS Marine Reanalysis is a global sea ice ocean coupled reanalysis product produced by the marine data assimilation team of the UFS Research-to-Operation (R2O) project. Underlying forecast and data assimilation systems are based on the UFS model prototype version-6 and the Next Generation Global Ocean Data Assimilation System (NG-GODAS) release of the Joint Effort for Data assimilation Integration (JEDI) Sea Ice Ocean Coupled Assimilation (SOCA). Covering the 40 year reanalysis time per",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "The UFS-DATM-MOM6-CICE6 model free run output contains continuous files from 1979-01-01 to 2019-08-30. Daily MOM6 and CICE6 diagnostic output files were produced for both model states and observations assimilated in the reanalysis experiment. In the reanalysis runs, the MOM6 and CICE6 models were initialized in a 24-hour analysis window centered at 12Z.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-regtests",
"name": "NOAA Unified Forecast System Weather Model (UFS-WM) Regression Tests",
"description": "The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The ufs-weather-model (UFS-WM) is the model source of the UFS for NOAA’s operational numerical weather prediction applications. The UFS-WM Regression Test (RT) is the testing software to ensure that previously developed and tested capabilities in UFS-WM still work after code changes are integrated into the system. It is required that UFS-WM RTs are performed successfully on the required Tier-",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "The input and baseline datasets are updated for the latest two-months of development code commits/revisions made to the UFS-WM’s development branch.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-ufs-s2s",
"name": "NOAA Unified Forecast System Subseasonal to Seasonal Prototypes",
"description": "The Unified Forecast System Subseasonal to Seasonal prototypes consist of reforecast data from the UFS atmosphere-ocean coupled model experimental prototype version 5, 6, 7, and 8 produced by the Medium Range and Subseasonal to Seasonal Application team of the UFS-R2O project. The UFS prototypes are the first dataset released to the broader weather community for analysis and feedback as part of the development of the next generation operational numerical weather prediction system from NWS. The d",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "A Prototype is a retrospective run for the period from 2011 to 2018. The runs are initialized twice per month (1st and 15th) and the length of the forecast is 35 days with an output frequency of 6 hours.",
"tags": "aws-pds,agriculture,climate,disaster response,environmental,meteorological,weather,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-ufs-shortrangeweather",
"name": "NOAA Unified Forecast System Short-Range Weather (UFS SRW) Application",
"description": "The \"[Unified Forecast System (UFS)](https://ufscommunity.org/)\" is a community-based, coupled, comprehensive Earth Modeling System. It supports \" [multiple applications](https://ufscommunity.org/science/aboutapps/)\" with different forecast durations and spatial domains. The UFS Short-Range Weather (SRW) Application figures among these applications. It targets predictions of atmospheric behavior on a limited spatial domain and on time scales from minutes to several days. The SRW Application incl",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "These are stable datasets for use with the SRW Application. They will not be updated frequently.",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-uwpd-cmip5",
"name": "Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset",
"description": "The University of Wisconsin Probabilistic Downscaling (UWPD) is a statistically downscaled dataset based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. UWPD consists of three variables, daily precipitation and maximum and minimum temperature. The spatial resolution is 0.1°x0.1° degree resolution for the United States and southern Canada east of the Rocky Mountains.\n
\n
\nThe downscaling methodology is not deterministic. Inst",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Periodically, as new data becomes available or when corrections are implemented.",
"tags": "aws-pds,climate,coastal,disaster response,environmental,meteorological,sustainability,oceans,water,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-wave-ensemble-reforecast",
"name": "NOAA Wave Ensemble Reforecast",
"description": "This is a 20-year global wave reforecast generated by WAVEWATCH III model (https://github.com/NOAA-EMC/WW3) forced by GEFSv12 winds (https://noaa-gefs-retrospective.s3.amazonaws.com/index.html). The wave ensemble was run with one cycle per day (at 03Z), spatial resolution of 0.25°X0.25° and temporal resolution of 3 hours. There are five ensemble members (control plus four perturbed members) and, once a week (Wednesdays), the ensemble is expanded to eleven members. The forecast range is 16 days a",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Quarterly",
"tags": "aws-pds,agriculture,climate,meteorological,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "noaa-wod",
"name": "NOAA World Ocean Database (WOD)",
"description": "The World Ocean Database (WOD) is the largest uniformly formatted, quality-controlled, publicly available historical subsurface ocean profile database. From Captain Cook's second voyage in 1772 to today's automated Argo floats, global aggregation of ocean variable information including temperature, salinity, oxygen, nutrients, and others vs. depth allow for study and understanding of the changing physical, chemical, and to some extent biological state of the World's Oceans. Browse the bucket via",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Data is update on a quarterly basis",
"tags": "aws-pds,climate,oceans",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination ",
"deprecated": false
},
{
"slug": "noaa-wsa-enlil",
"name": "NOAA Wang Sheeley Arge (WSA) Enlil",
"description": "The WSA-Enlil heliospheric model provides critical information regarding the propagation of solar Coronal Mass Ejections (CMEs) and transient structures within the heliosphere. Two distinct models comprise the WSA-Enlil modeling system; 1) the Wang-Sheeley-Arge (WSA) semi-empirical solar coronal model, and 2) the Enlil magnetohydrodynamic (MHD) heliospheric model. MHD modeling of the full domain (solar photosphere to Earth) is extremely computationally demanding due to the large parameter space ",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "Only model output from model runs containing CMEs are provided here. As these runs are executed on demand, there is no regular update frequency.",
"tags": "aws-pds,climate,meteorological,solar,weather",
"license": "NOAA data disseminated through NODD are open to the public and can be used as desired.
NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of",
"deprecated": false
},
{
"slug": "northern-california-earthquakes",
"name": "Northern California Earthquake Data",
"description": "This dataset contains various types of digital data relating\nto earthquakes in central and northern California. \nTime series data come from broadband, short period, and strong motion\nseismic sensors, GPS, and other geophysical sensors.\n",
"managed_by": "[Northern California Earthquake Data Center](https://ncedc.org)",
"update_frequency": "Daily",
"tags": "aws-pds,earth observation,earthquakes,seismology",
"license": "NCEDC hereby grants the non-exclusive, royalty free, non-transferable, \nworldwide right and license to use, reproduce and publicly display in all media \npublic data from the Northern California Seismic Network. Please cite \nthe \"NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley\nS",
"deprecated": false
},
{
"slug": "nrel-pds-building-stock",
"name": "End-Use Load Profiles for the U.S. Building Stock",
"description": "The U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly\navailable dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses,\nacross all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models\nusing many different measured datasets, as descri",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "Twice per year",
"tags": "aws-pds,climate,cities,energy,energy modeling,geospatial,metadata,model,open source software,sustainability",
"license": "[ComStock License](https://github.com/NREL/ComStock/blob/main/LICENSE.txt) and [ResStock License](https://github.com/NREL/resstock/blob/main/LICENSE.md)",
"deprecated": false
},
{
"slug": "nrel-pds-dsgrid",
"name": "Demand-Side Grid (dsgrid) Toolkit",
"description": "Projects that use the dsgrid toolkit assemble bottom-up descriptions of electricity \ndemand and related data that are highly resolved geographically, temporally, and sectorally. \nTypically modelers describe multiple scenarios of future energy use at hourly resolution, \nsuitable for inclusion in long-term power system planning models, i.e., capacity expansion and \nproduction cost models. \n",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "Annually",
"tags": "data assimilation,electricity,energy,energy modeling,meteorological,transportation,industrial,solar",
"license": "Creative Commons Attribution 4.0 United States License",
"deprecated": false
},
{
"slug": "nrel-pds-ncdb",
"name": "National Climate Database (NCDB)",
"description": "The National Climate Database (NCDB) seeks to be the definitive source of climate \ndata for energy applications. The goal of the NCDB is to provide unbiased high \ntemporal and spatial resolution climate data needed for renewable energy modeling. \nThe NCDB seeks to maintain the inherent relationship between the various parameters \nthat are needed to model solar, wind, hydrology and load and provide data for multiple \nimportant climate scenarios. \n",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,earth observation,energy,geospatial,meteorological,solar,climate projections,CMIP5,CMIP6",
"license": "Creative Commons Attribution 3.0 United States License",
"deprecated": false
},
{
"slug": "nrel-pds-nsrdb",
"name": "NREL National Solar Radiation Database",
"description": "Released to the public as part of the Department of Energy's Open Energy Data Initiative,\nthe [National Solar Radiation Database (NSRDB)](https://nsrdb.nrel.gov/) is\na serially complete collection of hourly and half-hourly values of the three\nmost common measurements of solar radiation – global horizontal, direct\nnormal, and diffuse horizontal irradiance — and meteorological data. These\ndata have been collected at a sufficient number of locations and temporal and\nspatial scales to accurately rep",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "Annually",
"tags": "aws-pds,earth observation,energy,geospatial,meteorological,solar",
"license": "Creative Commons Attribution 3.0 United States License",
"deprecated": false
},
{
"slug": "nrel-pds-porotomo",
"name": "PoroTomo",
"description": "Released to the public as part of the Department of Energy's Open Energy Data\nInitiative, these data represent vertical and horizontal distributed acoustic\nsensing (DAS) data collected as part of the Poroelastic Tomography (PoroTomo)\nproject funded in part by the Office of Energy Efficiency and Renewable\nEnergy (EERE), U.S. Department of Energy.\n",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,geothermal,seismology,image processing,geospatial",
"license": "Creative Commons Attribution 3.0 United States License",
"deprecated": false
},
{
"slug": "nrel-pds-sup3rcc",
"name": "Sup3rCC",
"description": "Released to the public as part of the Department of Energy's Open Energy Data Initiative, \nthese data represent a serially complete collection of hourly 4km wind, solar, temperature, \nhumidity, and pressure fields for the Continental United States under climate change scenarios.\n\nSup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial file set tagged\n\"sup3rcc_conus_mriesm20_ssp585_r1i1p1f1\" is downscaled from MRI ESM 2.0 for climate change \nscenario SSP5 8.5 and variant l",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "Annual",
"tags": "aws-pds,energy,solar,air temperature,climate model",
"license": "Creative Commons Attribution 4.0 United States License",
"deprecated": false
},
{
"slug": "nrel-pds-windai",
"name": "Wind AI Bench",
"description": "This data lake contains multiple datasets related to fundamental problems \nin wind energy research. This includes data for wind plant power production \nfor various layouts/wind flow scenarios, data for two- and three-dimensional \nflow around different wind turbine airfoils/blades, wind turbine noise \nproduction, among others. The purpose of these datasets is to establish a \nstandard benchmark against which new AI/ML methods can be tested, compared, \nand deployed. Details regarding the generation",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "Annually",
"tags": "aws-pds,energy,benchmark,machine learning",
"license": "Creative Commons Attribution 4.0 United States License",
"deprecated": false
},
{
"slug": "nrel-pds-wtk",
"name": "NREL Wind Integration National Dataset",
"description": "Released to the public as part of the Department of Energy's Open Energy Data Initiative,\nthe [Wind Integration National Dataset (WIND)](https://www.nrel.gov/grid/wind-toolkit.html)\nis an update and expansion of the Eastern Wind Integration Data Set and\nWestern Wind Integration Data Set. It supports the next generation of wind\nintegration studies.\n",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "As Needed",
"tags": "aws-pds,environmental,geospatial,meteorological",
"license": "Creative Commons Attribution 3.0 United States License",
"deprecated": false
},
{
"slug": "nsd",
"name": "Natural Scenes Dataset",
"description": "Here, we collected and pre-processed a massive, high-quality 7T fMRI dataset that can be used to advance our understanding of how the brain works. A unique feature of this dataset is the massive amount of data available per individual subject. The data were acquired using ultra-high-field fMRI (7T, whole-brain, 1.8-mm resolution, 1.6-s TR). We measured fMRI responses while each of 8 participants viewed 9,000–10,000 distinct, color natural scenes (22,500–30,000 trials) in 30–40 weekly scan sessio",
"managed_by": "[Computational Visual Neuroscience Laboratory](http://cvnlab.net) at the [Center for Magnetic Resonance Research](http://www.cmrr.umn.edu)",
"update_frequency": "This dataset is now released. A small portion of the data is held out and will be released in the future. Besides that, we do not expect major updates, aside from any corrections or additions that arise (these will be logged on the NSD Data Manual).",
"tags": "aws-pds,life sciences,imaging,neuroscience,nifti,neuroimaging,magnetic resonance imaging,machine learning,image processing,computer vision",
"license": "[Terms & Conditions for the NSD dataset](https://cvnlab.slite.com/api/s/note/9dgh5HCqgZYhMoAESZBS86/Terms-and-Conditions)",
"deprecated": false
},
{
"slug": "nsf-ncar-era5",
"name": "NSF NCAR Curated ECMWF Reanalysis 5 (ERA5)",
"description": "NSF NCAR is providing a NetCDF-4 structured version of the 0.25 degree atmospheric ECMWF Reanalysis 5 (ERA5) to the AWS ODSP. ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa",
"managed_by": "[NSF National Center for Atmospheric Research](https://ncar.ucar.edu/)",
"update_frequency": "Monthly, with a 3-4 month lag from realtime",
"tags": "climate,model,atmosphere,land,data assimilation,forecast,meteorological,weather,geoscience,geospatial",
"license": "https://www.ucar.edu/terms-of-use/data",
"deprecated": false
},
{
"slug": "nsw-herbarium",
"name": "National Herbarium of NSW",
"description": "The National Herbarium of New South Wales is one of the most significant scientific, cultural and historical botanical resources in the Southern hemisphere. The 1.43 million preserved plant specimens have been captured as high-resolution images and the biodiversity metadata associated with each of the images captured in digital form. Botanical specimens date from year 1770 to today, and form voucher collections that document the distribution and diversity of the world's flora through time, parti",
"managed_by": "Royal Botanic Gardens and Domain Trust",
"update_frequency": "Quarterly",
"tags": "aws-pds,agriculture,biodiversity,biology,climate,digital preservation,ecosystems,environmental",
"license": "https://creativecommons.org/licenses/by/4.0/legalcode",
"deprecated": false
},
{
"slug": "nuview-state",
"name": "NUVIEW - Multi-State Geospatial Data",
"description": "NUVIEW hosts and manages a unified collection of geospatial datasets from multiple U.S. states and agencies\n(LiDAR, orthophoto imagery, DEM/DSM, and derivative products). Data are organized in a\nsingle S3 bucket with a logical sub-folder hierarchy: `/state_or_agency_product_type/acqusition_project_name/...`. All assets\nare cloud-optimized (COG GeoTIFFs, COPC (Cloud Optimized Point Cloud) LAZ point clouds, etc.) and available under open licenses.\n",
"managed_by": "[NUVIEW](https://nuview.space/)",
"update_frequency": "Project-based updates.",
"tags": "aws-pds,geospatial,satellite imagery,natural resource,sustainability,disaster response,dem,lidar",
"license": "CC0 \"Public Domain\" (or state/agency-specific open data licenses)",
"deprecated": false
},
{
"slug": "nwm-archive",
"name": "NOAA National Water Model CONUS Retrospective Dataset",
"description": "The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations\n",
"managed_by": "[NOAA](http://www.noaa.gov/)",
"update_frequency": "No updates",
"tags": "aws-pds,agriculture,weather,climate,environmental,disaster response,agriculture,transportation",
"license": "Open Data. There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "nyc-tlc-trip-records-pds",
"name": "New York City Taxi and Limousine Commission (TLC) Trip Record Data",
"description": "Data of trips taken by taxis and for-hire vehicles in New York City. Note: access to this dataset is free, however direct S3 access does require an AWS account. Anonymous downloads are accessible from the dataset's documentation webpage listed below.",
"managed_by": "City of New York Taxi and Limousine Commission",
"update_frequency": "As soon as new data is available to be shared publicly.",
"tags": "aws-pds,cities,urban,transportation",
"license": "http://www1.nyc.gov/home/terms-of-use.page",
"deprecated": false
},
{
"slug": "nyu-fastmri",
"name": "NYU Langone & FAIR FastMRI Dataset",
"description": "This dataset contains deidentified raw k-space data and DICOM image files of over 1,500 knees and 6,970 brains.",
"managed_by": "[FastMRI](https://fastmri.med.nyu.edu/)",
"update_frequency": "The dataset is estimated to grow annually to include MRI raw data and imaging for additional body structures.",
"tags": "aws-pds,life sciences,health,imaging,biology,image processing,neurobiology,neuroimaging,magnetic resonance imaging",
"license": "MIT License",
"deprecated": false
},
{
"slug": "nyumets-brain",
"name": "NYUMets Brain Dataset",
"description": "This dataset contains 8,000+ brain MRIs of 2,000+ patients with brain metastases.",
"managed_by": "[NYUMets](https://nyumets.org/)",
"update_frequency": "This dataset will be updated as new data is added.",
"tags": "aws-pds,life sciences,health,imaging,biology,image processing,neurobiology,neuroimaging,magnetic resonance imaging,cancer",
"license": "[Custom Non-Commercial License](https://signup.nyumets.org/#agreement)",
"deprecated": false
},
{
"slug": "nz-coastal",
"name": "New Zealand Coastal Elevation",
"description": "The New Zealand Coastal Elevation dataset consists of New Zealand's publicly owned coastal digital elevation models, which are freely available to use under an open licence. The data consists of bare earth (DEM) data that traverses the coastal zone, including the seabed down to approximately 25m in depth. Data is provided as nationally consistent 1m resolution tiles derived from LiDAR surveys.\n\nAll of the coastal elevation files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using LERC c",
"managed_by": "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)",
"update_frequency": "New coastal elevation data will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.",
"tags": "aws-pds,coastal,elevation,lidar,earth observation,stac,cog,geospatial",
"license": "Toitū Te Whenua Land Information New Zealand is the copyright owner for the coastal elevation data that is available in this bucket. Licensed for re-use under \"[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)\".",
"deprecated": false
},
{
"slug": "nz-elevation",
"name": "New Zealand Elevation",
"description": "The New Zealand Elevation dataset consists of New Zealand's publicly owned digital elevation models and digital surface models, which are freely available to use under an open licence. The dataset contains 1m resolution grids derived from LiDAR data. Point clouds are not included in the initial release.\n\nAll of the elevation files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using LERC compression for the main grid and LERC compression with lower max_z_error for the overviews. These el",
"managed_by": "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)",
"update_frequency": "New elevation data will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.",
"tags": "aws-pds,elevation,earth observation,stac,cog,geospatial",
"license": "Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the elevation data that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each elevation data survey. Licensed for re-use under \"[CC-BY-4.0](https://creativecommons.org/lice",
"deprecated": false
},
{
"slug": "nz-imagery",
"name": "New Zealand Imagery",
"description": "The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and geor",
"managed_by": "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)",
"update_frequency": "New imagery will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.",
"tags": "aws-pds,aerial imagery,satellite imagery,earth observation,stac,cog,geospatial",
"license": "Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the imagery that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each imagery survey. Licensed for re-use under \"[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)\"",
"deprecated": false
},
{
"slug": "obis",
"name": "Ocean Biodiversity Information System (OBIS) species occurrence data",
"description": "The Ocean Biodiversity Information System (OBIS) was founded in 2000 under the Census of Marine Life. It is now a programme component of the International Oceanographic Data and Information Exchange (IODE) programme of the Intergovernmental Oceanographic Commission (IOC) of UNESCO. OBIS aims to be the most comprehensive data and information gateway on the diversity, distribution and abundance of marine life to support its Member States in achieving a healthy and resilient ocean ecosystem. The OB",
"managed_by": "The Ocean Biodiversity Information System (OBIS)",
"update_frequency": "Weekly",
"tags": "biodiversity,coastal,oceans,conservation,ecosystems,environmental,geospatial,water,life sciences",
"license": "https://creativecommons.org/licenses/by-nc/4.0/",
"deprecated": false
},
{
"slug": "oceanomics",
"name": "OceanOmics",
"description": "Minderoo Foundation OceanOmics aims to establish environmental DNA (eDNA) as a tool to measure, understand, and protect oceans. OceanOmics mainly generates two types of data: eDNA sequencing data (metabarcoding, metagenomics), and genome assembly data (marine vertebrates).",
"managed_by": "Minderoo Foundation OceanOmics, Dr Shannon Corrigan, Dr Philipp Bayer",
"update_frequency": "Data will be continually updated as it is generated.",
"tags": "biodiversity,bioinformatics,biology,conservation,genetic,genomic,life sciences",
"license": "CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/deed.en",
"deprecated": false
},
{
"slug": "ocmr_data",
"name": "Ohio State Cardiac MRI Raw Data (OCMR)",
"description": "OCMR is an open-access repository that provides multi-coil k-space data for cardiac cine. The fully sampled MRI datasets are intended for quantitative comparison and evaluation of image reconstruction methods. The free-breathing, prospectively undersampled datasets are intended to evaluate their performance and generalizability qualitatively.",
"managed_by": "[OCMR](https://www.ocmr.info/)",
"update_frequency": "Annually",
"tags": "aws-pds,life sciences,Homo sapiens,magnetic resonance imaging,image processing,imaging,signal processing",
"license": "Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "oedi-data-lake",
"name": "Department of Energy's Open Energy Data Initiative (OEDI)",
"description": "Data released under the Department of Energy's (DOE) Open Energy Data Initiative\n(OEDI). The Open Energy Data Initiative aims to improve and automate\naccess of high-value energy data sets across the U.S. Department of Energy’s\nprograms, offices, and national laboratories. OEDI aims to make data\nactionable and discoverable by researchers and industry to accelerate\nanalysis and advance innovation.\n",
"managed_by": "[National Laboratory of the Rockies](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,energy,environmental,geospatial,model,solar,lidar",
"license": "Creative Commons Attribution 3.0 United States License",
"deprecated": false
},
{
"slug": "ogs-arco-ocean",
"name": "ARCO-OCEAN",
"description": "ARCO-OCEAN is an analysis-ready cloud-optimized dataset providing physical properties of the ocean, waves, and sea ice for a period of about 28 years between the 1st of January 1993 and the 30th of June 2021. The dataset includes also atmospheric and hydrological variables that would be needed as boundary conditions and used to drive a numerical simulation. The dataset is the result of collecting, processing, merging and optimizing for the cloud different data sources, all retrospective analyses",
"managed_by": "[OGS](https://www.ogs.it/en/dynamics-ecosystems-and-computational-oceanography)",
"update_frequency": "Variable (as needed).",
"tags": "aws-pds,analysis ready data,atmosphere,climate,hydrology,ice,machine learning,oceans,physics,zarr",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "ohsu-cnl",
"name": "Oregon Health & Science University Chronic Neutrophilic Leukemia Dataset",
"description": "The OHSU-CNL study offers the whole exome and RNA-sequencing on a cohort of 100 cases with rare\nhematologic malignancies such as Chronic neutrophilic leukemia (CNL), atypical chronic myeloid\nleukemia (aCML), and unclassified myelodysplastic syndrome/myeloproliferative neoplasms\n(MDS/MPN-U). This dataset contains open RNA-Seq Gene Expression Quantification data.\n",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "oida",
"name": "Opioid Industry Documents Archive (OIDA) Data on AWS",
"description": "The OIDA Data on AWS contain the metadata, documents, and extracted text for all of the documents in the UCSF-JHU Opioid Industry Documents Archive, a growing corpus of internal corporate records and other documents arising from the opioid industry.",
"managed_by": "Johns Hopkins University",
"update_frequency": "monthly",
"tags": "aws-pds,archives,text analysis,txt,pharmaceutical,life sciences",
"license": "https://www.industrydocuments.ucsf.edu/opioids/help/copyright/",
"deprecated": false
},
{
"slug": "ome-zarr-open-scivis",
"name": "OME-Zarr Open SciVis Datasets",
"description": "This project provides the Open SciVis Datasets in a chunked, highly-compressed, multi-scale format, encodes metadata in JSON according to the OME-Zarr specification, and hosts the datasets on AWS S3 through the AWS Open Data Program, aiming to serve as a web-based resource for the scientific visualization community to enhance reproducibility and facilitate testing and development of OME-Zarr tools.",
"managed_by": "NumFOCUS",
"update_frequency": "On a biannual basis we update the datasets and sync with OME-Zarr standards.",
"tags": "biology,image processing,imaging,neuroimaging,neuroscience,life sciences,magnetic resonance imaging,computed tomography,volumetric imaging,zarr",
"license": "CC-BY-4.0 unless otherwise specified",
"deprecated": false
},
{
"slug": "omi-no2-nasa",
"name": "Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density",
"description": "NO2 tropospheric column density, screened for CloudFraction < 30% global daily\n composite at 0.25 degree resolution for the temporal range of 2004 to May\n2020. Original archive data in HDF5 has been processed into a [Cloud-Optimized\nGeoTiff (COG)](https://www.cogeo.org/) format. Quality Assurance - This data\nhas been validated by the NASA Science Team at Goddard Space Flight Center.\n\nCautionary Note: https://airquality.gsfc.nasa.gov/caution-interpretation.\n",
"managed_by": "NASA",
"update_frequency": "None",
"tags": "aws-pds,earth observation,geospatial,satellite imagery,air quality,atmosphere,environmental",
"license": "There are no restrictions on the use of these data.",
"deprecated": false
},
{
"slug": "ons-opendata-portal",
"name": "ONS Open Data Portal",
"description": "The ONS Open Data Portal, produced by the National Operator of the Electric System (ONS), gathers historical data from the Brazilian electricity sector in an easy and democratic way with the main objective to facilitate and improve the access and consumption of this type of content by all users and audiences. The Portal gathers consolidated data on energy generation (hydraulic, thermal, solar and wind), areas of national and international energy exchange and energy load, information about equipm",
"managed_by": "[ONS - National Electric System Operator](https://www.ons.org.br/)",
"update_frequency": "diary",
"tags": "aws-pds,electricity,hydrography,energy",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "ont-open-data",
"name": "Oxford Nanopore Technologies Benchmark Datasets",
"description": "The ont-open-data registry provides reference sequencing data from Oxford Nanopore Technologies to support, 1) Exploration of the characteristics of nanopore sequence data. 2) Assessment and reproduction of performance benchmarks 3) Development of tools and methods. The data deposited showcases DNA sequences from a representative subset of sequencing chemistries. The datasets correspond to publicly-available reference samples (e.g. Genome In A Bottle reference cell lines). Raw data are provided ",
"managed_by": "Oxford Nanopore Technologies",
"update_frequency": "Additional datasets will be added periodically. Updates and amendents will be made to existing entries when algorithmic advancements are made (e.g. improvements to basecalling algorithms).\n",
"tags": "aws-pds,bioinformatics,biology,fastq,fast5,genomic,life sciences,Homo sapiens,whole genome sequencing",
"license": "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/\nThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385.\n",
"deprecated": false
},
{
"slug": "ont_basemod_data",
"name": "ONT Methylation Benchmarking Datasets",
"description": "ONT Methylation Benchmarking Datasets are generated to benchmark existing methylation-calling tools on the Oxford Nanopore sequencing platform using their recent R10.4.1 flowcell chemistry. It spans a diverse range of species, including bacteria (E. coli, H. pylori J99, H. pylori 26695, A. variabilis, T. denticola), plants (Rice, Arabidopsis), and mammals (mouse, human).In addition, the dataset includes EMSeq data for E. coli, plant, and mouse samples, which can serve as ground truth for methyla",
"managed_by": "[CSIR-Centre for Cellular and Molecular Biology](https://www.ccmb.res.in/)",
"update_frequency": "Datasets will be updated periodically as additional data is generated.",
"tags": "aws-pds,life sciences,genomic,long read sequencing,bioinformatics,epigenomics,benchmark,bam",
"license": "[MIT License](https://opensource.org/license/mit)",
"deprecated": false
},
{
"slug": "ooni",
"name": "Open Observatory of Network Interference (OONI)",
"description": "A free software, global observation network for detecting censorship, surveillance and traffic manipulation on the internet.",
"managed_by": "Open Observatory of Network Interference",
"update_frequency": "Hourly",
"tags": "aws-pds,internet",
"license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https://github.com/ooni/license/blob/master/data/LICENSE.md",
"deprecated": false
},
{
"slug": "open-bio-ref-data",
"name": "Open Bioinformatics Reference Data for Galaxy",
"description": "This dataset provides genomic reference data and software packages for use with [Galaxy](https://galaxyproject.org/) and [Bioconductor](https://bioconductor.org/) applications. The reference data is available for hundreds of reference genomes and has been formatted for use with a variety of tools. The available configuration files make this data easily incorporable with a local Galaxy server without additional data preparation. Additionally, Bioconductor's AnnotationHub and ExperimentHub data ar",
"managed_by": "Galaxy and Bioconductor Projects",
"update_frequency": "Periodically but not on a set schedule.",
"tags": "aws-pds,bioinformatics,biology,genetic,genomic,life sciences,reference index",
"license": "Public Domain",
"deprecated": false
},
{
"slug": "open-ceda",
"name": "Open CEDA by Watershed",
"description": "CEDA is a multi-regional Environmentally-Extended Input-Output (EEIO) model developed to support a wide range of environmental systems analyses—including corporate carbon accounting and sustainable spend analysis. CEDA provides unparalleled global coverage and granularity, representing 95% of the world's GDP across 148 countries and 400 sectors, enabling robust and geographically comprehensive Scope 3 greenhouse gas (GHG) measurement. \nOpen CEDA is the publicly avaialable version of CEDA, now ea",
"managed_by": "[Watershed Technology](https://watershed.com)",
"update_frequency": "Annual",
"tags": "aws-pds,climate,carbon,scope 3,supply chain,spend-based models,EEIO",
"license": "Creative Commons CC BY-SA",
"deprecated": false
},
{
"slug": "open-cravat",
"name": "OpenCRAVAT",
"description": "OpenCRAVAT is a module variant annotation tool developed by KarchinLab at Johns Hopkins.\nThis dataset is a mirror of the OpenCRAVAT store available at https://store.opencravat.org.\nYou can configure OpenCRAVAT to use this mirror by editing the \"cravat-system.yml\" file.\nThe path to this file is in the first output line of the command \"oc config system\". In that file,\nchange the value of \"store_url\" to \"https://opencravat-store-aws.s3.amazonaws.com\".\n",
"managed_by": "KarchinLab, Potomac IT Group",
"update_frequency": "Data is mirrored daily. Update frequencies of individual annotators depend on the update schedule\nof the source data.\n",
"tags": "aws-pds,genetic,genomic,life sciences,variant annotation,sqlite,tertiary analysis",
"license": "License varies per-annotator. Commercial users must check the \"commercial_warning\" field \nin the module_name.yml file of each annotator.\n",
"deprecated": false
},
{
"slug": "open-lidar-data",
"name": "Collection of open nation-scale LiDAR datasets",
"description": "The goal of this project is to collect all publicly available large scale LiDAR datasets and archive them in an\nuniform fashion for easy access and use. Initial efforts to collect the datasets are concentrated on Europe and will\nbe in future expanded to USA and other regions, striving for global coverage. Every dataset includes files in original\ndata format and translated to COPC format. For faster browsing, we include an overview file that includes a small\nsubset of data points from every datas",
"managed_by": "[Flai](https://flai.ai/)",
"update_frequency": "When new open dataset is published.",
"tags": "aws-pds,lidar,earth observation,geoscience,geospatial,land cover,mapping,survey",
"license": "The exact version of the licence depends on LiDAR dataset and is not the same for all datasets. Look for license.pdf file in the used dataset.",
"deprecated": false
},
{
"slug": "open-meteo",
"name": "Open-Meteo Weather API Database",
"description": "Open-Meteo integrates weather models from reputable national weather services, offering a swift and efficient weather API. Real-time weather forecasts are unified into a time-series database that provides historical and future weather data for any location worldwide.\n\nThrough Open-Meteo on AWS Open Data, you can download the Open-Meteo weather database and analysis weather data locally. Docker images are provided to download data and to expose an HTTP API endpoint. Using Open-Meteo SDKs, you can",
"managed_by": "[Open-Meteo](https://www.open-meteo.com/)",
"update_frequency": "Hourly",
"tags": "aws-pds,agriculture,climate,earth observation,meteorological,weather",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "open-neurodata",
"name": "Open NeuroData",
"description": "This bucket contains multiple neuroimaging datasets (as Neuroglancer Precomputed Volumes) across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include segmentations and meshes.",
"managed_by": "[NeuroData](https://neurodata.io/ocp)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,biology,image processing,neuroimaging,neuroscience,electron microscopy,life sciences,light-sheet microscopy,magnetic resonance imaging,array tomography",
"license": "ODC-By v1.0 unless otherwise specified",
"deprecated": false
},
{
"slug": "open-robo-care",
"name": "OpenRoboCare Multi-Modal Expert Demonstration Dataset for Robot-Assisted Caregiving",
"description": "A comprehensive multimodal dataset capturing real-world caregiving routines from 21 occupational therapists performing 15 daily caregiving tasks. The dataset includes synchronized RGB-D video, tactile sensing, eye-gaze tracking, pose annotations, and action labels across 315 sessions totaling 19.8 hours of expert demonstrations. Data modalities include anonymized RGB images, depth maps, 44-sensor tactile readings, 2D/3D pose tracking, temporal action annotations, and first/third-person videos, e",
"managed_by": "[EmPRISE Lab at Cornell University](https://emprise.cs.cornell.edu/)",
"update_frequency": "Static dataset - no regular updates planned",
"tags": "computer vision,robotics,machine learning,health,aws-pds,life sciences",
"license": "BSD-3-Clause license - Academic and non-commercial use permitted. See documentation for full terms.",
"deprecated": false
},
{
"slug": "openaerialmap",
"name": "OpenAerialMap on AWS",
"description": "OpenAerialMap is a collection of high-resolution openly licensed satellite and aerial imagery.",
"managed_by": "[Humanitarian OpenStreetMap Team](https://www.hotosm.org/)",
"update_frequency": "New imagery is added as soon as it is uploaded by community contributors.",
"tags": "satellite imagery,aerial imagery,earth observation,disaster response,cog",
"license": "All imagery is publicly licensed CC-BY 4.0, with attribution as contributors of Open Imagery Network. All imagery is available to be traced in OpenStreetMap..",
"deprecated": false
},
{
"slug": "openalex",
"name": "OpenAlex dataset",
"description": "An open, comprehensive index of scolarly papers, citations, authors, institutions, and journals.",
"managed_by": "[OurResearch](https://ourresearch.org/)",
"update_frequency": "Approximately monthly",
"tags": "graph,json,metadata,scholarly communication,aws-pds",
"license": "[CC0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "openaq",
"name": "OpenAQ",
"description": "Global, aggregated physical air quality data from public data sources provided by government, research-grade and other sources. These awesome groups do the hard work of measuring these data and publicly sharing them, and our community makes them more universally-accessible to both humans and machines.",
"managed_by": "[OpenAQ](https://openaq.org)",
"update_frequency": "Hourly",
"tags": "aws-pds,air quality,cities,environmental,geospatial",
"license": "Varies, depends on data provider",
"deprecated": false
},
{
"slug": "opencitymodel",
"name": "Open City Model (OCM)",
"description": "Open City Model is an initiative to provide cityGML data for all the buildings in the United States.\nBy using other open datasets in conjunction with our own code and algorithms it is our goal to provide 3D geometries for every US building.\n",
"managed_by": "BuildZero",
"update_frequency": "Quarterly",
"tags": "aws-pds,events,cities,geospatial",
"license": "https://github.com/opencitymodel/opencitymodel#license",
"deprecated": false
},
{
"slug": "openfold",
"name": "OpenProteinSet",
"description": "Multiple sequence alignments (MSAs) for 140,000 unique [Protein Data Bank](https://www.rcsb.org/) (PDB) chains and 16,000,000 [UniClust30](https://uniclust.mmseqs.com/) clusters. Template hits are also provided for the PDB chains and 270,000 UniClust30 clusters chosen for maximal diversity and MSA depth. MSAs were generated with HHBlits (-n3) and JackHMMER against MGnify, BFD, UniRef90, and UniClust30 while templates were identified from PDB70 with HHSearch, all according to procedures outlined ",
"managed_by": "OpenFold",
"update_frequency": "Never",
"tags": "openfold,msa,protein,protein template,protein folding,alphafold,open source software,life sciences,aws-pds",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "openfold3",
"name": "OpenFold3 Training Data",
"description": "This dataset contains MSAs and predicted structures used to train OpenFold3 preview, an open-source, all-atom ligand, RNA and protein structure prediction software. This includes - \n - PDB - 245k structures and alignments from the RCSB Protein Data Bank - https://www.rcsb.org/\n - Long monomer distillation set - ~13 million long (sequence length >= 200 amino acids) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.\n - Short monomer distillation set - 400k short (sequence",
"managed_by": "OpenFold Consortium",
"update_frequency": "Never",
"tags": "openfold,msa,protein,protein template,protein folding,open source software,life sciences,aws-pds",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "openfoodfacts-images",
"name": "Open Food Facts Images",
"description": "A dataset of all images of Open Food Facts, the biggest open dataset of food products in the world.",
"managed_by": "[Open Food Facts](https://world.openfoodfacts.org)",
"update_frequency": "Monthly",
"tags": "aws-pds,machine learning,image processing",
"license": "All data contained in this dataset is licenced under the [Creative Commons Attribution ShareAlike licence](https://creativecommons.org/licenses/by-sa/3.0/deed.en)",
"deprecated": false
},
{
"slug": "openhgl",
"name": "Open Human Genome Library",
"description": "The Open Human Genome Library (OpenHGL) is a collection of high-quality *de novo* human assemblies that are publicly available in genomic databases (e.g. NCBI and CNCB) or from individual research papers. It provides consistent naming and uniform formats across datasets, supporting efficient subsequence retrieval and approximate string search.\n",
"managed_by": "Heng Li lab at Dana-Farber Cancer Institute and Harvard Medical School",
"update_frequency": "As new data or new analyses become available",
"tags": "aws-pds,bioinformatics,genomic,biology,life sciences",
"license": "Creative Commons Zero (CC0)",
"deprecated": false
},
{
"slug": "openneuro",
"name": "OpenNeuro",
"description": "OpenNeuro is a database of openly-available brain imaging data. The data are shared according to a Creative Commons CC0 license, providing a broad range of brain imaging data to researchers and citizen scientists alike. The database primarily focuses on functional magnetic resonance imaging (fMRI) data, but also includes other imaging modalities including structural and diffusion MRI, electroencephalography (EEG), and magnetoencephalograpy (MEG). OpenfMRI is a project of the [Center for Reproduc",
"managed_by": "[Stanford University Center for Reproducible Neuroscience](https://reproducibility.stanford.edu/)",
"update_frequency": "New datasets deposited every 4-6 days",
"tags": "aws-pds,biology,imaging,life sciences,neurobiology,neuroimaging",
"license": "CC0",
"deprecated": false
},
{
"slug": "opensurfaces",
"name": "OpenSurfaces",
"description": "A large database of annotated surfaces created from real-world consumer photographs.",
"managed_by": "Cornell University",
"update_frequency": "Static dataset (not updated)",
"tags": "computer vision,aws-pds",
"license": "The annotations are licensed under a Creative Commons Attribution 4.0 International License. The photos have their own licenses.",
"deprecated": false
},
{
"slug": "opentargets",
"name": "Open Targets",
"description": "The Open Targets Platform is a comprehensive data integration tool that supports systematic identification and prioritisation of potential therapeutic drug targets. By integrating publicly available datasets including data generated by the Open Targets experimental and informatics research programmes, the Platform provides data and services to assist in the task of therapeutic hypothesis building.",
"managed_by": "Open Targets",
"update_frequency": "The data is released every three months.",
"tags": "genetic,life sciences,genomic,aws-pds,bioinformatics,biology,protein,drug discovery",
"license": "https://creativecommons.org/publicdomain/zero/1.0/",
"deprecated": false
},
{
"slug": "openuniverse2024",
"name": "OpenUniverse 2024 Simulated Roman & Rubin Images",
"description": "This release consists of simulated data products designed to mimic observations of the same region of the sky as seen by two astronomical facilities: the Nancy Grace Roman Telescope and the Vera C. Rubin Observatory.",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "OpenUniverse 2024 has been finalized and will not be updated.",
"tags": "aws-pds,astronomy,imaging,object detection,parquet,satellite imagery,simulations,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "openwings",
"name": "OpenWings OpenData",
"description": "DNA sequence data of UCE loci collected from the world's bird species (n=10,560).",
"managed_by": "[OpenWings Project](https://github.com/openwings-project)",
"update_frequency": "Monthly",
"tags": "biodiversity,fastq,genome,genetic,life sciences,museum,wildlife",
"license": "[OpenWings Data Use Policy](https://github.com/openwings-project/opendata?tab=readme-ov-file#data-use-policy)",
"deprecated": false
},
{
"slug": "orcasound",
"name": "Orcasound - bioacoustic data for marine conservation",
"description": "Live-streamed and archived audio data (~2018-present) from underwater microphones (hydrophones) containing marine biological signals as well as ambient ocean noise. Hydrophone placement and passive acoustic monitoring effort prioritizes detection of orca sounds (calls, clicks, whistles) and potentially harmful noise. Geographic focus is on the US/Canada critical habitat of Southern Resident killer whales (northern CA to central BC) with initial focus on inland waters of WA. In addition to the ra",
"managed_by": "Orcasound",
"update_frequency": "Typical latency is 10-120 seconds",
"tags": "aws-pds,biodiversity,biology,coastal,conservation,deep learning,ecosystems,environmental,geospatial,labeled",
"license": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
"deprecated": false
},
{
"slug": "organoid-pancreatic",
"name": "Pancreatic Cancer Organoid Profiling",
"description": "This study generated a collection of patient-derived pancreatic normal and cancer organoids and it was sequenced using Whole Genome Sequencing (WGS), Whole Exome Sequencing (WXS) and RNA-Seq as well as matched tumor and normal tissue if available. The study provides a valuable resource for pancreatic cancer researchers.\nThe dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and ",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genetic,genomic,transcriptomics,whole genome sequencing,STRIDES,life sciences",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "os-climate-physrisk",
"name": "OS-Climate Physrisk",
"description": "Collection of adapted and derived hazard indicator datasets optimized for running physical climate risk analyses.",
"managed_by": "[OS-Climate](https://os-climate.org/)",
"update_frequency": "Data is updated as new important public domain datasets become available or if corrections are published.",
"tags": "climate risk,physical,hazard,peril,hazard indicator,extreme weather",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "osm",
"name": "OpenStreetMap on AWS",
"description": "OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3 in both standard formats (OSM PBF, XML) and cloud-native formats optimized for analytics workloads.",
"managed_by": "OpenStreetMap Foundation (OSMF) and Pacific Atlas",
"update_frequency": "Data is updated minutely/hourly/daily (changes) and weekly (snapshots)",
"tags": "aws-pds,geospatial,mapping,osm,disaster response",
"license": "https://www.openstreetmap.org/copyright",
"deprecated": false
},
{
"slug": "osmlr",
"name": "OpenStreetMap Linear Referencing",
"description": "OSMLR a linear referencing system built on top of OpenStreetMap. OSM has great information about roads around the world and their interconnections, but it lacks the means to give a stable identifier to a stretch of roadway. OSMLR provides a stable set of numerical IDs for every 1 kilometer stretch of roadway around the world. In urban areas, OSMLR IDs are attached to each block of roadways between significant intersections.",
"managed_by": "Unmanaged",
"update_frequency": "New data is added based on community feedback",
"tags": "aws-pds,geospatial,traffic,osm,disaster response",
"license": "https://github.com/opentraffic/osmlr/blob/master/LICENSE.md",
"deprecated": true
},
{
"slug": "overture",
"name": "Overture Maps Foundation Open Map Data",
"description": "Overture is a collaboratively built, global, open map data project for developers who build map services or use geospatial data. Overture Open Map Data contains data that are standardized under the themes of Admins, Base, Buildings, Places, and Transportation. Overture also includes a Global Entity Reference System (GERS) which encodes map data to a shared universal reference. Beginning with the Overture 2023-11-14-alpha.0 release, the data is available as cloud-native GeoParquet files. \n",
"managed_by": "[Overture Maps Foundation](https://overturemaps.org)",
"update_frequency": "Monthly",
"tags": "aws-pds,geospatial,global,mapping,osm,parquet,transportation",
"license": "Overture data is licensed under the Community Database License Agreement Permissive v2 (CDLA) unless derived from a source that requires publishing under a different license, such as data derived from OpenStreetMap, that constitutes a 'Derivative Database' (as defined under ODbL v1.0), which will be",
"deprecated": false
},
{
"slug": "pacbio-human-wgs-reference",
"name": "Reference data for HiFi human WGS",
"description": "Reference data bundle for analyzing HiFi human whole genome\nsequencing data\n",
"managed_by": "[Pacific Biosciences of California, Inc](https://www.pacb.com/)",
"update_frequency": "Files are updated to reflect the support for the lastest version of\n[PacBio WGS Variant Pipeline]\n(https://github.com/PacificBiosciences/HiFi-human-WGS-WDL)\n",
"tags": "aws-pds,health,life sciences,Homo sapiens,long read sequencing,genetic,mapping,whole genome sequencing,vcf,variant annotation",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "pacific-sound",
"name": "Pacific Ocean Sound Recordings",
"description": "This project offers passive acoustic data (sound recordings) from a deep-ocean environment off central California. Recording began in July 2015, has been nearly continuous, and is ongoing. These resources are intended for applications\nin ocean soundscape research, education, and the arts.\n",
"managed_by": "[Monterey Bay Aquarium Research Institute](https://www.mbari.org/)",
"update_frequency": "daily",
"tags": "aws-pds,acoustics,biodiversity,ecosystems,biology,marine mammals,oceans,climate,coastal,deep learning",
"license": "CC-BY 4.0",
"deprecated": false
},
{
"slug": "palsar-2-scansar-flooding-in-bangladesh",
"name": "PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1)",
"description": "Tropical Cyclone Mocha began to form in the Bay of Bengal on 11 May 2023 and continues to intensify as it moves towards Myanmar and Bangladesh.Cyclone Mocha is the first storm to form in the Bay of Bengal this year and is expected to hit several coastal areas in Bangladesh on 14 May with wind speeds of up to 175 km/h.After made its landfall in the coast between Cox’s Bazar (Bangladesh) and Kyaukphyu (Myanmar) near Sittwe (Myanmar). At most, Catastrophic Damage-causing winds was possible especial",
"managed_by": "[JAXA](https://www.jaxa.jp/)",
"update_frequency": "As available.",
"tags": "aws-pds,agriculture,cog,disaster response,earth observation,geospatial,natural resource,satellite imagery,stac,sustainability",
"license": "Data is available for free under the terms of use.",
"deprecated": false
},
{
"slug": "palsar-2-scansar-flooding-in-rwanda",
"name": "PALSAR-2 ScanSAR Flooding in Rwanda (L2.1)",
"description": "Torrential rainfall triggered flooding and landslides in many parts of Rwanda. The hardest-hit districts were Ngororero, Rubavu, Nyabihu, Rutsiro and Karongi. According to reports, 14 people have died in Karongi, 26 in Rutsiro, 18 in Rubavu, 19 in Nyabihu and 18 in Ngororero.Rwanda National Police reported that the Mukamira-Ngororero and Rubavu-Rutsiro roads are impassable due to flooding and landslide debris. UNITAR on behalf of United Nations Office for the Coordination of Humanitarian Affairs",
"managed_by": "[JAXA](https://www.jaxa.jp/)",
"update_frequency": "As available.",
"tags": "aws-pds,agriculture,cog,deafrica,disaster response,earth observation,geospatial,natural resource,satellite imagery,stac",
"license": "Data is available for free under the terms of use.",
"deprecated": false
},
{
"slug": "palsar2-scansar-turkey-syria",
"name": "PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1)",
"description": "JAXA has responded to the Earthquake events in Turkey and Syria by conducting emergency disaster observations and providing data as requested by the Disaster and Emergency Management Authority (AFAD), Ministry of Interior in Turkey, through Sentinel Asia and the International Disaster Charter. Additional information on the event and dataset can be found [here](https://earth.jaxa.jp/en/earthview/2023/02/14/7381/index.html). The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broa",
"managed_by": "[JAXA](https://www.jaxa.jp/)",
"update_frequency": "As available.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,sustainability,disaster response,synthetic aperture radar,deafrica",
"license": "Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html).",
"deprecated": false
},
{
"slug": "paracrawl",
"name": "Provision of Web-Scale Parallel Corpora for Official European Languages (ParaCrawl)",
"description": "ParaCrawl is a set of large parallel corpora to/from English for all official EU languages by a broad web crawling effort. State-of-the-art methods are applied for the entire processing chain from identifying web sites with translated text all the way to collecting, cleaning and delivering parallel corpora that are ready as training data for CEF.AT and translation memories for DG Translation.\n",
"managed_by": "[ParaCrawl](https://paracrawl.eu)",
"update_frequency": "New data is added according to ParaCrawl release schedule.",
"tags": "aws-pds,machine translation,natural language processing",
"license": "Creative Commons CC0 license (\"no rights reserved\").",
"deprecated": false
},
{
"slug": "pass-summaries-fewsum",
"name": "PASS: Perturb-and-Select Summarizer for Product Reviews",
"description": "A collection of product reviews summaries automatically generated by PASS for 32 Amazon products from the FewSum dataset",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not updated",
"tags": "amazon.science,natural language processing,text analysis",
"license": "This data is available for anyone to use under the terms of the CDLA-Sharing license, which is available [here](https://cdla.dev/sharing-1-0/)",
"deprecated": false
},
{
"slug": "pasteur-logan",
"name": "Logan Unitigs and Contigs of the Sequence Read Archive (SRA) on AWS",
"description": "This repository is a re-analysis of the NCBI Sequence Read Archive (SRA), December 2023 freeze, to make it more accessible. The SRA is an open access database of biological sequences, containing raw data from high-throughput DNA and RNA sequencing platforms. It is the largest database of public DNA sequences worldwide, containing a wealth of genomic diversity across all living organisms. This repository contains Logan, a set of compressed FASTA files for all individual SRA accessions, in the for",
"managed_by": "Institut Pasteur (https://www.pasteur.fr)",
"update_frequency": "Occasional",
"tags": "aws-pds,life sciences,genetic,genomic,whole genome sequencing,whole exome sequencing,transcriptomics,metagenomics,fasta,STRIDES",
"license": "[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)",
"deprecated": false
},
{
"slug": "pcd",
"name": "Phrase Clustering Dataset (PCD)",
"description": "This dataset is part of the paper \"McPhraSy: Multi-Context Phrase Similarity and Clustering\" by DN Cohen et al (2022). The purpose of PCD is to evaluate the quality of semantic-based clustering of noun phrases. The phrases were collected from the [Amazon Review Dataset] (https://nijianmo.github.io/amazon/).\n",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not updated",
"tags": "amazon.science,natural language processing,json",
"license": "This data is available for anyone to use under the terms of the CDLA-permissive license, which is available [here](https://cdla.dev/permissive-1-0/)",
"deprecated": false
},
{
"slug": "pd12m",
"name": "PD12M",
"description": "PD12M is a collection of 12.4 million CC0/PD image-caption pairs for the purpose of training generative image models.",
"managed_by": "Spawning",
"update_frequency": "Data will be adjusted as infringing works are discovered, improved provenance is acquired, or infringing captions are discovered.",
"tags": "image processing,machine learning,media,art,deep learning,labeled",
"license": "https://cdla.dev/permissive-2-0/",
"deprecated": false
},
{
"slug": "pdb-3d-structural-biology-data",
"name": "Protein Data Bank 3D Structural Biology Data",
"description": "The \"Protein Data Bank (PDB) archive\" was established in 1971 as the first open-access digital data archive in biology. It is a collection of three-dimensional (3D) atomic-level structures of biological macromolecules (i.e., proteins, DNA, and RNA) and their complexes with one another and various small-molecule ligands (e.g., US FDA approved drugs, enzyme co-factors). For each PDB entry (unique identifier: 1abc or PDB_0000001abc) multiple data files contain information about the 3D atomic coordi",
"managed_by": "[Worldwide Protein Data Bank Partnership](wwpdb.org)",
"update_frequency": "New and updated data files are published weekly and released on Wednesdays 0:00 UTC.\n",
"tags": "aws-pds,amino acid,archives,bioinformatics,biomolecular modeling,cell biology,chemical biology,COVID-19,electron microscopy,electron tomography",
"license": "https://creativecommons.org/publicdomain/zero/1.0/",
"deprecated": false
},
{
"slug": "person-path-22",
"name": "PersonPath22",
"description": "PersonPath22 is a large-scale multi-person tracking dataset containing 236 videos captured mostly from static-mounted cameras, collected from sources where we were given the rights to redistribute the content and participants have given explicit consent. Each video has ground-truth annotations including both bounding boxes and tracklet-ids for all the persons in each frame.",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Periodically",
"tags": "amazon.science,computer vision",
"license": "Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0)",
"deprecated": false
},
{
"slug": "pgc-arcticdem",
"name": "ArcticDEM",
"description": "ArcticDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2007 to the present. The ArcticDEM project seeks to fill the need for high-resolution time-series elevation data in the Arctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comp",
"managed_by": "[Polar Geospatial Center](https://www.pgc.umn.edu/)",
"update_frequency": "New DEM strips are added twice yearly. Mosaic products are added as soon as they are available.",
"tags": "aws-pds,elevation,earth observation,geospatial,mapping,open source software,satellite imagery,cog,stac",
"license": "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "pgc-earthdem",
"name": "EarthDEM",
"description": "EarthDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2002 to the present. The EarthDEM project seeks to fill the need for high-resolution time-series elevation data in non-polar regions. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and",
"managed_by": "[Polar Geospatial Center](https://www.pgc.umn.edu/)",
"update_frequency": "New DEM strips are added when allowed by licensing restrictions. Mosaic products are added as soon as they are available.",
"tags": "aws-pds,elevation,earth observation,geospatial,mapping,open source software,satellite imagery,cog,stac",
"license": "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "pgc-rema",
"name": "Reference Elevation Model of Antarctica (REMA)",
"description": "The Reference Elevation Model of Antarctica - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2009 to the present. The REMA project seeks to fill the need for high-resolution time-series elevation data in the Antarctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of prov",
"managed_by": "[Polar Geospatial Center](https://www.pgc.umn.edu/)",
"update_frequency": "New DEM strips are added twice yearly. Mosaic products are added as soon as they are available.",
"tags": "aws-pds,elevation,earth observation,geospatial,mapping,open source software,satellite imagery,cog,stac",
"license": "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "physionet",
"name": "Physionet",
"description": "PhysioNet offers free web access to large collections of recorded physiologic signals (PhysioBank) and related open-source software (PhysioToolkit).",
"managed_by": "[MIT Laboratory for Computational Physiology](https://lcp.mit.edu/)",
"update_frequency": "Not updated",
"tags": "aws-pds,biology,life sciences",
"license": "PhysioBank databases are made available under the ODC Public Domain Dedication and License v1.0. https://opendatacommons.org/licenses/pddl/1.0/",
"deprecated": false
},
{
"slug": "planette_c3s_seasonal_forecast_data",
"name": "Planette C3S Seasonal Forecast Data",
"description": "The C3S seasonal forecast dataset provides global, daily, probabilistic forecasts of the Earth system, \nenabling users to assess the likelihood of future climate states. These forecasts are particularly \nvaluable for studying slowly evolving climate patterns such as El Niño, La Niña, and the North Atlantic \nOscillation (NAO), which can be predicted with greater skill than the chaotic atmosphere. This dataset \nis derived from the Copernicus Climate Change Service (C3S) archive and includes SEAS5 ",
"managed_by": "Planette.ai",
"update_frequency": "Monthly",
"tags": "aws-pds,climate,weather,earth observation",
"license": "Copernicus Licence (similar to CC-BY-4.0): You are free to share and adapt the material \nfor any purpose, even commercially, provided that you give appropriate credit.\nhttps://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf\n",
"deprecated": false
},
{
"slug": "planette_era5_reanalysis",
"name": "Planette ERA5 Archive",
"description": "The ERA5 archive provides a comprehensive record of global weather and climate from 1940 to present, \nwith multiple temporal aggregations for flexible analysis. This dataset is derived from \nthe ECMWF/Copernicus ERA5 reanalysis and includes daily means, 7-day rolling means, \nand monthly/seasonal aggregations at 0.25°×0.25° global resolution. The Planette ERA5 archive stores \nthis data in cloud-native format (Zarr with icechunk) for efficient access and analysis.\n\nThe dataset includes essential a",
"managed_by": "Planette.ai",
"update_frequency": "Monthly",
"tags": "aws-pds,climate,weather,earth observation",
"license": "Copernicus Licence (similar to CC-BY-4.0): You are free to share and adapt the material \nfor any purpose, even commercially, provided that you give appropriate credit.\nhttps://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf\n",
"deprecated": false
},
{
"slug": "platinum-pedigree",
"name": "Platinum Pedigree",
"description": "The Platinum Pedigree Consortium (PCC) is a collaborative project to create a comprehensive reference for human genetic variation using a four-generation, 28-member family (CEPH-1463). We employed five different short and long-read sequencing technologies to generate phased assemblies and characterize both inherited and de novo variation, including at some of the most difficult to genotype genomic regions such as tandem repeats, centromeres, and the Y chromosome. This extensive \"truth set\" is pu",
"managed_by": "Platinum Pedigree Consortium",
"update_frequency": "As needed",
"tags": "genomic,genotyping,long read sequencing,bioinformatics,Homo sapiens,life sciences,whole genome sequencing",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "pmsp-lidar",
"name": "Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud",
"description": "The objective of the Mapa 3D Digital da Cidade (M3DC) of the São Paulo City Hall is to publish LiDAR point cloud data. The initial data was acquired in 2017 by aerial surveying and future data will be added. This publicly accessible dataset is provided in the [Entwine Point Tiles](https://entwine.io/entwine-point-tile.html) format as a lossless octree, full density, based on [LASzip](https://laszip.org) (LAZ) encoding.",
"managed_by": "[GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp.gov.br)",
"update_frequency": "Local survey executed by demand generates new data as local point clouds.",
"tags": "cities,land,lidar,urban,geospatial,elevation,mapping,aws-pds",
"license": "[GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html)",
"deprecated": false
},
{
"slug": "pohang-canal-dataset",
"name": "Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters",
"description": "This dataset presents a multi-modal maritime dataset acquired in restricted waters in Pohang, South Korea. The sensor suite is composed of three LiDARs (one 64-channel LiDAR and two 32-channel LiDARs), a marine radar, two visual cameras used as a stereo camera, an infrared camera, an omnidirectional camera with 6 directions, an AHRS, and a GPS with RTK. The dataset includes the sensor calibration parameters and SLAM-based baseline trajectory. It was acquired while navigating a 7.5 km route that ",
"managed_by": "[MORIN](http://morin.kaist.ac.kr)",
"update_frequency": "Not updated",
"tags": "aws-pds,autonomous vehicles,marine navigation,robotics,computer vision,lidar",
"license": "[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "pre-post-purchase-questions",
"name": "Pre- and post-purchase product questions",
"description": "This dataset provides product related questions, including their textual content and gap, in hours, between purchase and posting time. \nEach question is also associated with related product details, including its id and title.\n",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,machine learning",
"license": "[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)",
"deprecated": false
},
{
"slug": "prod-comp-shopping",
"name": "Product Comparison Dataset for Online Shopping",
"description": "The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "None",
"tags": "product comparison,online shopping,amazon.science,natural language processing,machine learning",
"license": "[CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)",
"deprecated": false
},
{
"slug": "proj-datum-grids",
"name": "PROJ datum grids",
"description": "Horizontal and vertical adjustment datasets for coordinate transformation to be used by PROJ 7 or later. PROJ is a generic coordinate transformation software that transforms geospatial coordinates from one coordinate reference system (CRS) to another. This includes cartographic projections as well as geodetic transformations.",
"managed_by": "[PROJ](https://proj.org)",
"update_frequency": "New grids are added when made available",
"tags": "aws-pds,geospatial,mapping",
"license": "Per file. Under an Open Source Definition compliant license. Consult the READMEs or look at the Copyright TIFF tag of each file.",
"deprecated": false
},
{
"slug": "proteingym",
"name": "ProteinGym",
"description": "ProteinGym is a benchmark suite for assessing the performance of protein fitness prediction and design models. It comprises a large curated collection of 200+ high-throughput experimental assays (~3M mutated sequences), as well as clinical annotations from experts about the pathogenicity of mutants in over 3k human genes.\n",
"managed_by": "Harvard Medical School, University of Oxford",
"update_frequency": "Quarterly",
"tags": "aws-pds,protein,bioinformatics,biology,life sciences,deep learning,machine learning",
"license": "MIT License",
"deprecated": false
},
{
"slug": "pubseq",
"name": "PubSeq - Public Sequence Resource",
"description": "COVID-19 PubSeq is a free and open online bioinformatics public sequence resource with on-the-fly analysis of sequenced SARS-CoV-2 samples that allows for a quick turnaround in identification of new virus strains. PubSeq allows anyone to upload sequence material in the form of FASTA or FASTQ files with accompanying metadata through the web interface or REST API.",
"managed_by": "[UTHSC GeneNetwork](https://covid19.genenetwork.org/)",
"update_frequency": "Rolling dataset.",
"tags": "aws-pds,bam,bioinformatics,biology,coronavirus,COVID-19,fasta,fastq,fast5,genetic",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise specified.",
"deprecated": false
},
{
"slug": "pyenvs-and-callargs",
"name": "PyEnvs and CallArgs",
"description": "PyEnvs is a collection of 2814 permissively licensed Python packages along with their isolated development environments. Paired with a program analyzer (e.g. Jedi Language Server), it supports querying for project-related information. \n\nCallArgs is a dataset built on top of PyEnvs for function call argument completion. It provides function definition, implementation, and usage information for each function call instance.\n",
"managed_by": "Amazon",
"update_frequency": "None Planned",
"tags": "machine learning,code completion",
"license": "Please note that while we are providing this aggregation of code snippets unlicensed, each package that makes up the broader dataset may have and often does have its own license. Individual licenses can be found in the source code for the respective packages.\n",
"deprecated": false
},
{
"slug": "pyfr-mtu-t161-dns-data",
"name": "High-Order Accurate Direct Numerical Simulation of Flow over a MTU-T161 Low Pressure Turbine Blade",
"description": "The archive comprises snapshot, point-probe, and time-average data produced via a high-fidelity computational simulation of turbulent air flow over a low pressure turbine blade, which is an important component in a jet engine. The simulation was undertaken using the open source PyFR flow solver on over 5000 Nvidia K20X GPUs of the Titan supercomputer at Oak Ridge National Laboratory under an INCITE award from the US DOE. The data can be used to develop an enhanced understanding of the complex th",
"managed_by": "http://www.pyfr.org",
"update_frequency": "Periodically",
"tags": "aws-pds,computational fluid dynamics,low-pressure turbine,green aviation,turbulence",
"license": "CC BY 2.0",
"deprecated": false
},
{
"slug": "qiime2",
"name": "QIIME 2 Tutorial Data",
"description": "QIIME 2 (pronounced “chime two”) is a microbiome multi-omics bioinformatics and data science platform that is trusted, free, open source, extensible, and community developed and supported.",
"managed_by": "Caporaso Lab",
"update_frequency": "Twice per year",
"tags": "aws-pds,bioinformatics,biology,ecosystems,environmental,genetic,genomic,health,microbiome,metagenomics",
"license": "BSD 3-Clause License",
"deprecated": false
},
{
"slug": "racecar-dataset",
"name": "RACECAR Dataset",
"description": "The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge during 2021-22 have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, o",
"managed_by": "Amar Kulkarni (ark8su@virginia.edu)",
"update_frequency": "This dataset was constructed during a single racing season (2021-22). Future seasons may potentially be added.",
"tags": "aws-pds,autonomous vehicles,autonomous racing,robotics,computer vision,perception,lidar,radar,GNSS,image processing",
"license": "Creative Commons Attribution-NonCommercial 4.0 International Public License [(CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "radarsat-1",
"name": "RADARSAT-1",
"description": "Developed and operated by the Canadian Space Agency, it is Canada's first commercial Earth observation satellite\n
\nDéveloppé et exploité par l'Agence spatiale canadienne, il s'agit du premier satellite commercial d'observation de la Terre au Canada.",
"managed_by": "[Natural Resources Canada](https://nrcan.gc.ca/)",
"update_frequency": "NRCan opened a new [RADARSAT-1 Processing service](https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb) to the public on December 18, 2025. The processor allows users to produce image products from the original raw data archive free of charge, with the primary goal of making previously unprocessed data accessible for the first time.\n
\nRNCan a ouvert un nouveau [service de traitement RADARSAT-1](https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb) au public le 18 décembre 2025. Le processeur permet aux utilisateurs de produire des produits d'imagerie à partir des archives de données brutes originales gratuitement, avec l'objectif principal de rendre accessible pour la première fois les données jamais traitées auparavant.",
"tags": "earth observation,global,aws-pds,ice,agriculture,disaster response,satellite imagery,geospatial,cog,synthetic aperture radar",
"license": "[Open Government License (OGL)](https://open.canada.ca/en/open-government-licence-canada)",
"deprecated": false
},
{
"slug": "radiant-mlhub",
"name": "Radiant MLHub",
"description": "Radiant MLHub is an open library for geospatial training data that hosts datasets generated by [Radiant Earth Foundation](https://www.radiant.earth/)'s team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a [SpatioTemporal Asset Catalog (STAC)](https://stacspec.org/) compliant catalog an",
"managed_by": "[Radiant Earth Foundation](https://www.radiant.earth/)",
"update_frequency": "New training data catalogs are added on a rolling basis",
"tags": "aws-pds,labeled,machine learning,geospatial,earth observation,satellite imagery,environmental,cog,stac",
"license": "Access to Radiant MLHub data is free for everyone. Each dataset has its own license (usually CC-BY or CC-BY-SA). View [Terms of Service](https://www.radiant.earth/terms/).",
"deprecated": false
},
{
"slug": "radiant",
"name": "RADIANT Public Data",
"description": "The Real-time Analysis and Discovery in Integrated And Networked Technologies (RADIANT) initiative seeks to develop an extensible, federated framework for rapid exchange of multimodal clinical and research data on behalf of accelerated discovery and patient impact. Coordination and implementation of initial RADIANT deployments will leverage a network of more than 35 partnered health care systems and participating patient families within the Children’s Brain Tumor Network (CBTN) and the Pediat",
"managed_by": "[The Center for Data-Driven Discovery in Biomedicine (D3b) at the Children's Hospital of Philadelphia](https://d3b.center/)",
"update_frequency": "Data is updated on a regular basis by the RADIANT teams to make data available as\nrapidly as possible. \n",
"tags": "aws-pds,life sciences,cancer,genetic,genomic,transcriptomics,medical imaging,radiology,Homo sapiens,pediatric",
"license": "NIH Genomic Data Sharing Policy: https://grants.nih.gov/grants/guide/notice-files/not-od-14-124.html",
"deprecated": false
},
{
"slug": "rapid-nrt-flood-maps",
"name": "RAPID NRT Flood Maps",
"description": "Near Real-time and archival data of High-resolution (10 m) flood inundation dataset over the Contiguous United States, developed based on the Sentinel-1 SAR imagery (2016-current) archive, using an automated Radar Produced Inundation Diary (RAPID) algorithm.",
"managed_by": "University of Connecticut; Guangxi University",
"update_frequency": "NRT data will be update as soon as SAR images available and done processed.",
"tags": "aws-pds,agriculture,earth observation,water,environmental,disaster response",
"license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International",
"deprecated": false
},
{
"slug": "rapid7-fdns-any",
"name": "Rapid7 FDNS ANY Dataset",
"description": "This dataset has been deprecated. Please see this [Rapid7 blog post](https://www.rapid7.com/blog/post/2022/02/10/evolving-how-we-share-rapid7-research-data-2/) for details.\n",
"managed_by": "Not currently managed",
"update_frequency": "Monthly\n",
"tags": "aws-pds,computer security,cyber security,analytics,internet",
"license": "Non-commercial (NC) - Free for non-commercial purposes. For commercial use, contact research@rapid7.com. Full terms: https://opendata.rapid7.com/about/#terms\n",
"deprecated": true
},
{
"slug": "rareplanes",
"name": "RarePlanes",
"description": "RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very high resoluti",
"managed_by": "In-Q-Tel - CosmiQ Works",
"update_frequency": "None Planned",
"tags": "computer vision,deep learning,earth observation,geospatial,machine learning,satellite imagery,aws-pds,labeled",
"license": "[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)",
"deprecated": false
},
{
"slug": "rcm-ceos-ard",
"name": "RCM CEOS Analysis Ready Data | Données prêtes à l'analyse du CEOS pour le MCR",
"description": "The [RADARSAT Constellation Mission (RCM)](https://www.asc-csa.gc.ca/eng/satellites/radarsat/) is Canada's third generation of Earth observation satellites. Launched on June 12, 2019, the three identical satellites work together to bring solutions to key challenges for Canadians. As part of ongoing [Open Government](https://open.canada.ca/en/about-open-government) efforts, NRCan produces a CEOS analysis ready data (ARD) of Canada landmass using a 30M Compact-Polarization standard coverage, every",
"managed_by": "[Natural Resources Canada](https://www.nrcan.gc.ca/)",
"update_frequency": "The initial dataset will be Canada-wide, 30M Compact-Polarization standard coverage, every 12 days, per mission revisit frequency.\n
\nL'ensemble de données initial couvrira l'ensemble du Canada, une couverture standard de 30 metres de polarisation compacte, tous les 12 jours, par fréquence de revisite de mission.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,sustainability,disaster response,synthetic aperture radar,stac,ceos",
"license": "RCM image products are available free of charge, to the broadest extent possible, in order to promote the development of innovative products and services derived from SAR data. The Government of Canada retains ownership of all RCM data and image products. Intellectual property rights to value-added ",
"deprecated": false
},
{
"slug": "real-changesets",
"name": "real-changesets",
"description": "The real-changesets is an augmented representation of OpenStreetMap changesets in JSON format. It contains the current and the previous version of each feature in a changeset.\nIt's primary used by OSMCha, the main OpenStreetMap validation tool, to have a visualization of the changeset and provide to the user the understanding of what was changed on the map.\nThe real-changesets are created by combining the changeset metadata and the augmented diff generated by overpass.\n",
"managed_by": "OpenStreetMap US",
"update_frequency": "Minutely",
"tags": "geospatial,osm,mapping,disaster response,aws-pds",
"license": "[Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/)\n",
"deprecated": false
},
{
"slug": "recount",
"name": "recount3",
"description": "recount3 is an online resource consisting of RNA-seq gene, exon, and exon-exon junction counts as well as coverage bigWig files for 8,679 and 10,088 different studies for human and mouse respectively. It is the third generation of the ReCount project and part of recount.bio. recount2 is also included for historical purposes. The pipeline used to generate the data in recount3 (but not recount2) is available [here](https://github.com/langmead-lab/monorail-external).\n",
"managed_by": "Johns Hopkins University",
"update_frequency": "Not currently updated",
"tags": "aws-pds,bioinformatics,biology,cancer,csv,gene expression,genetic,genomic,Homo sapiens,life sciences",
"license": "[recount3 is Public Domain, recount2 is CC0](https://creativecommons.org/share-your-work/public-domain/cc0/)",
"deprecated": false
},
{
"slug": "redasa-covid-data",
"name": "REDASA COVID-19 Open Data",
"description": "The REaltime DAta Synthesis and Analysis (REDASA) COVID-19 snapshot contains the output of the curation protocol produced by our curator community. A detailed description can be found in [our paper](https://www.jmir.org/2021/5/e25714). The first S3 bucket listed in Resources contains a large collection of medical documents in text format extracted from the [CORD-19 dataset](https://registry.opendata.aws/cord-19/), plus other sources deemed relevant by the REDASA consortium. The second S3 bucket",
"managed_by": "REDASA Consortium, Imperial College London, UK",
"update_frequency": "Yearly updates",
"tags": "aws-pds,COVID-19,coronavirus,life sciences,information retrieval,natural language processing,text analysis",
"license": "CC-BY-4.0",
"deprecated": false
},
{
"slug": "refgenie",
"name": "Refgenie reference genome assets",
"description": "Pre-built refgenie reference genome data assets used for aligning and analyzing DNA sequence data.",
"managed_by": "Sheffield lab at the University of Virginia",
"update_frequency": "As new data becomes available (roughly quarterly)",
"tags": "aws-pds,biology,bioinformatics,genetic,genomic,infrastructure,life sciences,single-cell transcriptomics,transcriptomics,whole genome sequencing",
"license": "Public domain",
"deprecated": false
},
{
"slug": "registry-open-data",
"name": "Registry of Open Data on AWS",
"description": "The Registry of Open Data on AWS contains publicly available datasets that are available for access from AWS resources. Note that datasets in this registry are available via AWS resources, but they are not provided by AWS; these datasets are owned and maintained by a variety of government organizations, researchers, businesses, and individuals. This dataset contains derived forms of the data in [https://github.com/awslabs/open-data-registry](https://github.com/awslabs/open-data-registry) that ha",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Automatically when new datasets are added",
"tags": "amazon.science,json,metadata",
"license": "[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)",
"deprecated": false
},
{
"slug": "roa",
"name": "Rain over Africa",
"description": "The Rain over Africa (RoA) dataset consists of spaceborn estimates of precipitation of Rain over Africa using only geostationary imagery and obtained through a convolutional and quantile regression neural network. The dataset also contains some uncertainty estimates.",
"managed_by": "[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)",
"update_frequency": "At most, yearly",
"tags": "aws-pds,agriculture,analysis ready data,atmosphere,aws-pds,climate,deep learning,earth observation,geophysics,geoscience",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "roadmapepigenomics",
"name": "NIH Roadmap Epigenomics",
"description": "The NIH Roadmap Epigenomics Mapping Consortium was launched with the goal of producing a public resource of human epigenomic data to catalyze basic biology and disease-oriented research. The project has generated high-quality, genome-wide maps of several key histone modifications, chromatin accessibility, DNA methylation and mRNA expression across 100s of human cell types and tissues. To see what data is available, please check the directory listing: https://roadmapepigenomics.s3.us-west-2.amazo",
"managed_by": "NIH Roadmap Epigenomics Mapping Consortium, Ting Wang Lab at WashU (https://wang.wustl.edu/)",
"update_frequency": "NIH Roadmap Epigenomics Mapping Consortium ended, no updates would be provided.",
"tags": "biology,bioinformatics,genetic,genomic,epigenomics,life sciences,aws-pds",
"license": "External data users may freely download, analyze, and publish results based on any NIH Roadmp Epigenomics data provided here without restrictions.",
"deprecated": false
},
{
"slug": "rsna-abdominal-trauma-detection",
"name": "RSNA Abdominal Trauma Detection (RSNA-ABT)",
"description": "Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically",
"managed_by": "Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,computed tomography,x-ray tomography",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-cervical-spine-fracture-detection",
"name": "RSNA Cervical Spine Fracture Detection (RSNA-CSF) Dataset",
"description": "Over 1.5 million spine fractures occur annually in the United States alone resulting in over 17,730 spinal cord injuries annually. The most common site of spine fracture is the cervical spine. There has been a rise in the incidence of spinal fractures in the elderly and in this population, fractures can be more difficult to detect on imaging due to degenerative disease and osteoporosis. Imaging diagnosis of adult spine fractures is now almost exclusively performed with computed tomography (CT). ",
"managed_by": "[Radiological Society of North America](https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,computed tomography,x-ray tomography",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-intracranial-aneurysm-detection-dataset",
"name": "RSNA Intracranial Aneurysm Detection Dataset (RSNA-ICA)",
"description": "The Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) dataset is a collection of over 4,000 CT brain scans annotated by a cohort of over 40 volunteer radiologists from RSNA and the American Society of Neuroradiology to show the presence and location of intracranial aneurysms. It also includes a set of about 200 imaging studies that are annotated with AI-generated segmentations highlighting abnormalities. The imaging data was provided by 18 institutions. Initially c",
"managed_by": "Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,life sciences",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-intracranial-hemorrhage-detection",
"name": "RSNA Intracranial Hemorrhage Detection",
"description": "RSNA assembled this dataset in 2019 for the RSNA Intracranial Hemorrhage Detection AI Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/). De-identified head CT studies were provided by four research institutions. A group of over 60 volunteer expert radiologists recruited by RSNA and the American Society of Neuroradiology labeled over 25,000 exams for the presence and subtype classification of acute intracranial hemorrhage.",
"managed_by": "Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,computed tomography,x-ray tomography",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-lumbar-spine-degenerative-classification-dataset",
"name": "RSNA Lumbar Spine Degenerative Classification Dataset (RSNA-LSDD)",
"description": "The Radiological Society of North America Lumbar Spine Degenerative Classification dataset (RSNA-LSDD) is a collection of over 2,600 magnetic resonance imaging (MR) scans of the lumbar spine annotated by a cohort of about 60 volunteer radiologists recruited by the RSNA, the American Society for Spine Radiology and the American Society of Neuroradiology to identify the location and severity of five degenerative conditions across the five intervertebral disc levels (L1/L2, L2/L3, L3/L4, L4/L5, and",
"managed_by": "Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,life sciences",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-pulmonary-embolism-detection",
"name": "RSNA Pulmonary Embolism Detection",
"description": "RSNA assembled this dataset in 2020 for the RSNA STR Pulmonary Embolism Detection AI Challenge (https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection/). With more than 12,000 CT pulmonary angiography (CTPA) studies contributed by five international research centers, it is the largest publicly available annotated PE dataset. RSNA collaborated with the Society of Thoracic Radiology to recruit more than 80 expert thoracic radiologists who labeled the dataset with detailed clinical annotati",
"managed_by": "[Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,computed tomography,x-ray tomography",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-ratic",
"name": "RSNA Abdominal Traumatic Injury CT (RATIC)",
"description": "Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically",
"managed_by": "Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,computed tomography,x-ray tomography",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "rsna-screening-mammography-breast-cancer-detection",
"name": "RSNA Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset",
"description": "According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process ",
"managed_by": "Radiological Society of North America (https://www.rsna.org/)",
"update_frequency": "The dataset may be updated with additional or corrected data on a need-to-update basis.",
"tags": "aws-pds,radiology,medical imaging,medical image computing,machine learning,computer vision,csv,labeled,mammography,breast cancer",
"license": "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be",
"deprecated": false
},
{
"slug": "run_dbcan",
"name": "run_dbcan CAZyme and CGC annotation database on AWS",
"description": "Database for use with run_dbcan (CAZyme and CGC annotation), including CAZyme, Transporter, Transcription factor, Signaling Transduction Protein, Sulfatase, Peptidase, and Polysaccharide utilization Loci.",
"managed_by": "Xinpeng Zhang; Haidong Yi; Yanbin Yin",
"update_frequency": "This database will update annually and usually in July/August.",
"tags": "aws-pds,metagenomics,microbiome,benchmark,bioinformatics,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "s1-orbits",
"name": "Sentinel-1 Precise Orbit Determination (POD) Products",
"description": "Sentinel-1 Precise Orbit Determination (POD) products contain auxiliary data on satellite position and velocity for the European Space Agency's (ESA) Sentinel-1 mission. Sentinel-1 is a C-band Synthetic Aperture Radar (SAR) satellite constellation first launched in 2014 as part of the European Union's Copernicus Earth Observation programme. POD products are a necessary auxiliary input for nearly all Sentinel-1 data processing workflows.
This dataset is a mirror of the [Sentinel-1 Orbi",
"managed_by": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)",
"update_frequency": "Updated as new data becomes available on the [Copernicus Data Space Ecosystem](https://documentation.dataspace.copernicus.eu/Data/ComplementaryData/Additional.html#sentinel-1-orbits). Typically AUX_POEORB files are published daily and AUX_RESORB files are published every other hour.\n",
"tags": "auxiliary data,disaster response,earth observation,earthquakes,floods,geophysics,sentinel-1,synthetic aperture radar",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://dataspace.copernicus.eu/terms-and-conditions).\n",
"deprecated": false
},
{
"slug": "safecast",
"name": "Safecast",
"description": "An ongoing collection of radiation and air quality measurements taken by devices involved in the Safecast project.",
"managed_by": "[Safecast](https://safecast.org/)",
"update_frequency": "Continuous",
"tags": "air quality,aws-pds,climate,environmental,geospatial,radiation",
"license": "Safecast data is published under a [CC0 designation](https://creativecommons.org/publicdomain/zero/1.0/). This is a public domain designation and means the data is free and open for anyone to use under any circumstance.\n",
"deprecated": false
},
{
"slug": "salk-aging-mouse-brain-epigeneti",
"name": "Aging Mouse Brain Epigenetic",
"description": "Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types.",
"managed_by": "[Salk Institute](http://www.salk.edu)",
"update_frequency": "Never",
"tags": "life sciences,genetic,genomic,whole genome sequencing,whole exome sequencing,transcriptomics,fastq,bam,cram,aws-pds",
"license": "[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)",
"deprecated": false
},
{
"slug": "satellogic-earthview",
"name": "Satellogic EarthView dataset",
"description": "Satellogic EarthView dataset includes high-resolution satellite images captured over all continents. The dataset is organized in Hive partition format and hosted by AWS. The dataset can be accessed via STAC browser or aws cli. Each item of the dataset corresponds to a specific region and date, with some of the regions revisited for additional data. The dataset provides Top-of-Atmosphere (TOA) reflectance values across four spectral bands (Red, Green, Blue, Near-Infrared) at a Ground Sample Dista",
"managed_by": "[Satellogic](https://www.satellogic.com)",
"update_frequency": "New data will be made available periodically, with annual updates expected in the future covering the same or other new regions.",
"tags": "aws-pds,satellite imagery,earth observation,image processing,geospatial,computer vision,stac,cog",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en)",
"deprecated": false
},
{
"slug": "sau-global-fisheries-catch-data",
"name": "Sea Around Us Global Fisheries Catch Data",
"description": "The project presents Sea Around Us Global Fisheries Catch Data aggregated at EEZ level. The data are computed from reconstructed catches from various official fisheries statistics, scientific, technical and policy reports about the fisheries, and includes estimation of discards, unreported and illegal catch data from all maritime countries and major territories of the world.\n\nThis project was the result of a work between Sea Around Us and the CIC programme, a collaborative programme between the ",
"managed_by": "[Sea Around Us](https://www.seaaroundus.org/)",
"update_frequency": "The full dataset is computed only once or twice a year or when there is a significant change in data. Updates will occur as soon as newly recomputed datasets are available.\n",
"tags": "aws-pds,fisheries,ecosystems,biodiversity,marine",
"license": "This data is available for anyone to use under the [Sea Around Us Terms of Use](https://www.seaaroundus.org/terms-and-conditions/)",
"deprecated": false
},
{
"slug": "sbn-css",
"name": "Catalina Sky Survey (CSS) subset data on AWS",
"description": "Raw data that discovers Near Earth Objects (NEOs) which potentially could impact Earth",
"managed_by": "[Planetary Data Systems Small Bodies Node (SBN)](https://sbn.psi.edu/pds/support/)",
"update_frequency": "CSS data are delivered nightly into the PDS archive starting with the night of Jan. 20, 2022. Earlier data will also be delivered into the archive on an irregular schedule.",
"tags": "aws-pds,planetary,astronomy,object detection,survey",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "schweizer-haltestellen-oev",
"name": "Swiss Public Transport Stops",
"description": "The basic geo-data set for public transport stops comprises public transport stops in Switzerland and additional selected geo-referenced public transport locations that are of operational or structural importance (operating points).\n",
"managed_by": "Swiss Geoportal",
"update_frequency": "annually",
"tags": "aws-pds,cities,geospatial,infrastructure,mapping,traffic,transportation",
"license": "You may use this dataset for non-commercial purposes. You may use this dataset for commercial purposes. You must cite the source (title of the dataset, Federal Office of Transport FOT).",
"deprecated": false
},
{
"slug": "scottish-lidar",
"name": "Scottish Public Sector LiDAR Dataset",
"description": "This dataset is Lidar data that has been collected by the Scottish public sector and made available under the Open Government Licence. The data are available as point cloud (LAS format or in LAZ compressed format), along with the derived Digital Terrain Model (DTM) and Digital Surface Model (DSM) products as Cloud optimized GeoTIFFs (COG) or standard GeoTIFF. The dataset contains multiple subsets of data which were each commissioned and flown in response to different organisational requirements.",
"managed_by": "[Joint Nature Conservation Committee](https://jncc.gov.uk/)",
"update_frequency": "New datasets have historically been added every 2-3 years but there is no guarantee that this will continue.",
"tags": "lidar,cities,coastal,environmental,urban,elevation,cog,aws-pds",
"license": "All data is made available under the [Open Government Licence v3](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) unless otherwise stated. Currently the exception is only the phase 2 - LAZ files which are under [non-commercial government license](https://www.nationalarchiv",
"deprecated": false
},
{
"slug": "sdgstoday-mst",
"name": "My School Today",
"description": "This database provides estimates of walking travel time of school-aged populations to schools recorded in OpenStreetMap. Population counts of male and female students are sorted into 3 groups of travel time - under 30 minutes, 30-60 minutes, and over 60 minutes. It covers the African continent and is aggregated by first-level administrative divisions.",
"managed_by": "SDSN SDGs Today",
"update_frequency": "Monthly (weekly updates in progress)",
"tags": "aws-pds,education,geospatial,infrastructure,schools",
"license": "This work is licensed under a Creative Commons by Attribution (CC BY 4.0) license.",
"deprecated": false
},
{
"slug": "sdoml-fdl",
"name": "Solar Dynamics Observatory (SDO) Machine Learning Dataset",
"description": "The v1 dataset includes AIA/HMI observations 2010-2018 and v2 includes AIA/HMI observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.",
"managed_by": "[NASA](http://www.nasa.gov/)",
"update_frequency": "N/A (The IDL/Python scripts for generating the datasets are published online, which can be used by users to generate the v1 dataset after 2018 and v2 dataset after 2020)",
"tags": "aws-pds,machine learning,NASA SMD AI",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "seefar",
"name": "SeeFar V0",
"description": "A collection of multi-resolution satellite images from both public and commercial satellites. The dataset is specifically curated for training geospatial foundation models.",
"managed_by": "Coastal Carbon",
"update_frequency": "Yearly",
"tags": "geospatial,earth observation,satellite imagery,climate,biodiversity,coastal,machine learning,environmental,sustainability,natural resource",
"license": "The SeeFar dataset includes multiple licensing terms, specific to each satellite data source:\n - **Satellogic (Earthview)**: \"Creative Commons Attribution 4.0 International (CC BY 4.0) — allows for free use, sharing, and adaptation with attribution and indication of changes.\"\n - **WorldStrat (Airb",
"deprecated": false
},
{
"slug": "sentinel-1-rtc-indigo",
"name": "Analysis Ready Sentinel-1 Backscatter Imagery",
"description": "The [Sentinel-1 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a constellation of\nC-band Synthetic Aperature Radar (SAR) satellites from the European Space Agency launched since 2014.\nThese satellites collect observations of radar backscatter intensity day or night, regardless of the\nweather conditions, making them enormously valuable for environmental monitoring.\nThese radar data have been processed from original Ground Range Detected (GRD) scenes into a Radiometrically\n",
"managed_by": "[Indigo Ag, Inc.](https://www.indigoag.com/)",
"update_frequency": "Data updates are paused while we repair the processing pipeline, but the target is to update on a daily cadence for the dataset's spatial domain (CONUS).",
"tags": "agriculture,aws-pds,disaster response,earth observation,environmental,geospatial,satellite imagery,cog,stac,synthetic aperture radar",
"license": "The use of these data fall under the terms and conditions of the [Indigo Atlas Sentinel License](https://www.indigoag.com/forms/atlas-sentinel-license). \n",
"deprecated": false
},
{
"slug": "sentinel-1",
"name": "Sentinel-1",
"description": "[Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a pair of European radar imaging (SAR) satellites launched in 2014 and 2016. Its 6 days revisit cycle and ability to observe through clouds makes it perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to [cloud-optimized GeoTIFF format](https://www.cogeo.org/).",
"managed_by": "[Element 84](http://www.element84.com/)",
"update_frequency": "New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,disaster response,cog,synthetic aperture radar",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "sentinel-2-l2a-cogs",
"name": "Sentinel-2 Cloud-Optimized GeoTIFFs",
"description": "The [Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is\na land monitoring constellation of two satellites that provide high resolution\noptical imagery and provide continuity for the current SPOT and Landsat missions.\nThe mission provides a global coverage of the Earth's land surface every 5 days,\nmaking the data of great use in ongoing studies.\nThis dataset is the same as the [Sentinel-2](https://registry.opendata.aws/sentinel-2/)\ndataset, except the JP2K files wer",
"managed_by": "[Element 84](https://www.element84.com/)",
"update_frequency": "New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,cog,stac",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "sentinel-2",
"name": "Sentinel-2",
"description": "The [Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is\na land monitoring constellation of two satellites that provide high resolution\noptical imagery and provide continuity for the current SPOT and Landsat missions.\nThe mission provides a global coverage of the Earth's land surface every 5 days,\nmaking the data of great use in on-going studies. L1C data are available from\nJune 2015 globally. L2A data are available from November 2016 over Europe\nregion and globally",
"managed_by": "[Element 84](https://www.element84.com/)",
"update_frequency": "New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,stac",
"license": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "sentinel-3",
"name": "Sentinel-3",
"description": "This data set consists of observations from the Sentinel-3 satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-3 is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the Ocean and Land Colour Instrument (OLCI) for medium resolution marine and terrestrial optical measurements, the Sea and Land Surface Temperature Radiometer (SLSTR), the SAR Radar Altimeter (SRAL), the MicroWave Radiometer (MWR) and the Precise Orbit Determinatio",
"managed_by": "[Meteorological Environmental Earth Observation](http://www.meeo.it/)",
"update_frequency": "Daily",
"tags": "aws-pds,oceans,earth observation,environmental,geospatial,land,satellite imagery,cog,stac",
"license": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice",
"deprecated": false
},
{
"slug": "sentinel-products-ca-mirror",
"name": "Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada",
"description": "The official Government of Canada (GC) 🍁 Near Real-time (NRT) Sentinel Mirror connected to the [EU Copernicus programme](https://www.copernicus.eu), focused on Canadian coverage. In 2015, [Canada joined the Sentinel collaborative ground segment](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Canada_joins_Sentinel_collaborative_ground_segment) which introduced an NRT Sentinel mirror site for users and programs inside the Government of Canada (GC). In 2022, the [Commission signed ",
"managed_by": "[Natural Resources Canada](https://www.nrcan.gc.ca/)",
"update_frequency": "Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite downlink. Non-NRT Sentinel-2 and Sentinel-3 are also updated as quickly as possible based on Canada coverage and availability at the source.\n
\nSentinel-1 est un ensemble de données NRT récupéré de l'ESA dans les 90 minutes suivant la liaison descendante du satellite. Sentinel-2 et Sentinel-3 non NRT sont également récupérés le plus rapidement possible en fonction de la couverture du Canada et de la disponibilité à la source.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,sustainability,disaster response,synthetic aperture radar,stac",
"license": "The access and use of Copernicus Sentinel data is available on a free, full and open basis through the Copernicus Data Space Ecosystem and shall be governed by the Legal Notice on the use of Copernicus Sentinel Data and Service published here: https://sentinels.copernicus.eu/documents/247904/690755/",
"deprecated": false
},
{
"slug": "sentinel-s2-l2a-mosaic-120",
"name": "Sentinel-2 L2A 120m Mosaic",
"description": "Sentinel-2 L2A 120m mosaic is a derived product, which contains best pixel values for 10-daily periods, modelled by removing the cloudy pixels and then performing interpolation among remaining values. As there are some parts of the world, which have lengthy cloudy periods, clouds might be remaining in some parts. The actual modelling script is available [here](https://sentinel-hub.github.io/custom-scripts/sentinel-2/interpolated_time_series/).",
"managed_by": "[Sinergise](https://www.sinergise.com/)",
"update_frequency": "New data will be added annually.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,machine learning,cog",
"license": "CC-BY 4.0, Credit: Contains modified Copernicus data [year] processed by Sentinel Hub\n",
"deprecated": false
},
{
"slug": "sentinel1-slc-seasia-pds",
"name": "Sentinel-1 SLC dataset for South and Southeast Asia, Taiwan, Korea and Japan",
"description": "The S1 Single Look Complex (SLC) dataset contains Synthetic Aperture Radar (SAR) data in the C-Band wavelength. The SAR sensors are installed on a two-satellite (Sentinel-1A and Sentinel-1B) constellation orbiting the Earth with a combined revisit time of six days, operated by the European Space Agency. The S1 SLC data are a Level-1 product that collects radar amplitude and phase information in all-weather, day or night conditions, which is ideal for studying natural hazards and emergency respon",
"managed_by": "[Earth Observatory of Singapore, Nanyang Technological University](https://earthobservatory.sg)",
"update_frequency": "S1 SLC data for the region of interest will be updated regularly, as it becomes available on the Alaska Satellite Facility endpoint.",
"tags": "aws-pds,disaster response,satellite imagery,geospatial,earth observation,environmental,synthetic aperture radar",
"license": "The data usage will inherit and fully comply with the free and open data policy from ESA’s [Sentinel Data Policy](https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/TC_Sentinel_Data_31072014.pdf).\n",
"deprecated": false
},
{
"slug": "sentinel1-slc",
"name": "Sentinel-1 SLC dataset for Germany",
"description": "The Sentinel1 Single Look Complex (SLC) unzipped dataset contains Synthetic Aperture Radar (SAR) data from the European Space Agency’s Sentinel-1 mission. Different from the zipped data provided by ESA, this dataset allows direct access to individual swaths required for a given study area, thus drastically minimizing the storage and downloading time requirements of a project. Since the data is stored on S3, users can utilize the boto3 library and s3 get_object method to read the entire content o",
"managed_by": "[LiveEO](https://live-eo.com/)",
"update_frequency": "New Sentinel1-SLC IW data are updated regularly in an interval of 6 days, after they are available on the Alaska Satellite Facility endpoint.",
"tags": "aws-pds,disaster response,satellite imagery,geospatial,sustainability,earth observation,environmental,synthetic aperture radar",
"license": "The data usage will inherit and fully comply with the free and open data policy from ESA. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).\n",
"deprecated": false
},
{
"slug": "sentinel5p",
"name": "Sentinel-5P Level 2",
"description": "This data set consists of observations from the Sentinel-5 Precursor (Sentinel-5P) satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen diox",
"managed_by": "[Meteorological Environmental Earth Observation](http://www.meeo.it/)",
"update_frequency": "Daily",
"tags": "aws-pds,air quality,atmosphere,earth observation,environmental,geospatial,satellite imagery,cog,stac",
"license": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice",
"deprecated": false
},
{
"slug": "serratus-lovelywater",
"name": "Serratus: Ultra-deep Search for Novel Viruses - Versioned Data Release",
"description": "Serratus is a collaborative open science project for ultra-rapid discovery of known and unknown coronaviruses in response to the COVID-19 pandemic through re-analysis of publicly available genomic data. Our resulting vertebrate viral alignment data is explorable via the [Serratus Explorer](https://serratus.io/explorer) and directly accessible on Amazon S3.",
"managed_by": "Serratus / UBC Cloud Innovation Centre",
"update_frequency": "Quarterly",
"tags": "aws-pds,life sciences,genetic,genomic,bam,virus,COVID-19,SARS,SARS-CoV-2,MERS",
"license": "[CC-0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)",
"deprecated": false
},
{
"slug": "sevir",
"name": "Storm EVent ImageRy (SEVIR)",
"description": "Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.",
"managed_by": "Mark S. Veillette",
"update_frequency": "New events will be added to SEVIR yearly",
"tags": "satellite imagery,weather,meteorological,aws-pds",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "sgnex",
"name": "The Singapore Nanopore Expression Data Set",
"description": "The Singapore Nanopore Expression (SG-NEx) project is an international collaboration to generate reference transcriptomes and a comprehensive benchmark data set for long read Nanopore RNA-Seq. Transcriptome profiling is done using PCR-cDNA sequencing (PCR-cDNA), amplification-free cDNA sequencing (direct cDNA), direct sequencing of native RNA (direct RNA), and short read RNA-Seq. The SG-NEx core data includes 5 of the most commonly used cell lines and it is extended with additional cell lines an",
"managed_by": "The Genome Institute of Singapore (https://www.a-star.edu.sg/gis)",
"update_frequency": "Datasets will be updated periodically as additional data are generated.",
"tags": "aws-pds,genomic,transcriptomics,life sciences,long read sequencing,short read sequencing,bioinformatics,fast5,fasta,fastq",
"license": "[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "shopping-humor-generation",
"name": "Shopping Humor Generation",
"description": "This dataset provides a set of non-shoppable items, which are items that can't be purchased via a virtual assistant (love, vampires, etc). \nIn addition, for each non-shoppable item, the dataset contains humorous responses generated by two different large language models, and a template-based generation solution that uses a commonsense knowledge graph called ConceptNet.\nFinally, each row contains a score provided by human annotators that judge how funny each response is.\nThe columns provided for ",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,commerce",
"license": "Dataset is published under [CDLA-Permissive](https://cdla.io/).\n",
"deprecated": false
},
{
"slug": "short_peptides",
"name": "Conformational Space of Short Peptides",
"description": "Co-managed by [Toyoko](toyoko.io) and the [Structural Biology Group at the Universidad Nacional de Quilmes](http://ufq.unq.edu.ar/sbg/), this dataset allows us to explore the conformational space of all possible peptides using the 20 common amino acids. It consists of a collection of exhaustive molecular dynamics simulations of tripeptides and pentapeptides.",
"managed_by": "[Toyoko](https://www.toyoko.io/)",
"update_frequency": "Not updated",
"tags": "aws-pds,amino acid,bioinformatics,biomolecular modeling,molecular dynamics,protein,structural biology,life sciences",
"license": "CC-BY 4.0 See https://github.com/ToyokoLabs/SBG/blob/master/LICENSE",
"deprecated": false
},
{
"slug": "silam",
"name": "SILAM Air Quality",
"description": "Air Quality is a global SILAM atmospheric composition and air quality forecast performed on a daily basis for > 100 species and covering the troposphere and the stratosphere. The output produces 3D concentration fields and aerosol optical thickness. The data are unique: 20km resolution for global AQ models is unseen worldwide.",
"managed_by": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)",
"update_frequency": "1 time a day",
"tags": "aws-pds,earth observation,climate,weather,air quality,meteorological",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "silo",
"name": "SILO climate data on AWS",
"description": "[SILO](https://www.longpaddock.qld.gov.au/silo) is a database of Australian [climate data](https://www.longpaddock.qld.gov.au/silo/about/climate-variables) from 1889 to the present. It provides continuous, daily time-step [data products](https://www.longpaddock.qld.gov.au/silo/about/data-products) in ready-to-use [formats](https://www.longpaddock.qld.gov.au/silo/about/file-formats-and-samples) for research and operational applications.\nSILO's gridded datasets (in NetCDF and GeoTiff formats) are ",
"managed_by": "Queensland Government",
"update_frequency": "Daily",
"tags": "aws-pds,agriculture,climate,earth observation,environmental,meteorological,model,sustainability,water,weather",
"license": "SILO datasets are constructed by the [Queensland Government](http://www.qld.gov.au/) using observational data provided by the [Australian Bureau of Meteorology](http://www.bom.gov.au/) and other providers as noted in the metadata. All SILO datasets are provided under the [Creative Commons Attributio",
"deprecated": false
},
{
"slug": "singlecellhumanbloodatlas",
"name": "Single-Cell Atlas of Human Blood During Healthy Aging",
"description": "Comprehensive, large-scale single-cell profiling of healthy human blood at different ages is one of the critical pending tasks required to establish a framework for systematic understanding of human aging. Here, using single-cell RNA/TCR/BCR-seq with protein feature barcoding (20 antibodies), we profiled 317 samples from 166 healthy individuals aged 25 to 85 years old drawn over 3-year period. Dataset spanning ~2 million cells describes 50 subpopulations of blood immune cells, with 14 subpopulat",
"managed_by": "Sage Bionetworks",
"update_frequency": "Never",
"tags": "aws-pds,protein,single-cell transcriptomics,life sciences",
"license": "[CC BY]",
"deprecated": false
},
{
"slug": "sipecam",
"name": "SiPeCaM (Sitios Permanentes de la Calibración y Monitoreo de la Biodiversidad)",
"description": "The SiPeCaM goal is to create a data source that allows to evaluate changes in the biodiversity state, considering key aspect of how does the ecosystem behaves.",
"managed_by": "[Conabio](https://monitoreo.conabio.gob.mx/)",
"update_frequency": "SiPeCaM offers data releases once the biologist recollect the data from the protected natural areas (every 3 months)",
"tags": "aws-pds,biodiversity,biology,ecosystems,image processing,multimedia,wildlife",
"license": "Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "sissa-forecast-database-dataset",
"name": "CRC-SAS/SISSA historical seasonal and subseasonal forecast database",
"description": "En el marco del Sistema de Información de Sequías del Sur de Sudamérica (SISSA) se ha desarrollado una base de predicciones en escala subestacional y estacional con datos corregidos y sin corregir, con el propósito que permita estudiar predictibilidad en distintas escalas y también que sirva para alimentar modelos de sectores como agricultura e hidrología. \n
\n
\nLa base contiene datos en escala diaria entre 2000-2019 (sin corregir) y 2010-2019 (corregidos) para diversas variables incluyen",
"managed_by": "[SISSA](https://sissa.crc-sas.org/)",
"update_frequency": "Static database from 2000-2019 without correction and 2010-2019 with correction.",
"tags": "aws-pds,earth observation,natural resource,weather,forecast,meteorological,agriculture,hydrology",
"license": "[Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org/licenses/by/2.5/ar)",
"deprecated": false
},
{
"slug": "slacken",
"name": "Metagenomic reference libraries for Slacken",
"description": "Metagenomic indexes for use with the Slacken taxonomic classification tool",
"managed_by": "Johan Nyström-Persson",
"update_frequency": "These indexes are currently what was used in our 2025 publication introducing the concept of 2-step classification and comparing Kraken2 with Slacken. We aim to update the data at least once per year, resources permitting.",
"tags": "genomic,metagenomics,microbiome,bioinformatics,biology,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "smaht",
"name": "Somatic Mosaicism across Human Tissues (SMaHT)",
"description": "The Somatic Mosaicism across Human Tissues (SMaHT) project is an NIH Common\nFund consortium (2023-) aimed to comprehensively characterize somatic variation\n(\"mosaicism\") in normal human tissues. While most genetic studies have relied\non blood-derived DNA, SMaHT captures the full spectrum of DNA variation across\ncell types, tissues, and organs from phenotypically normal individuals to\nbetter understand the role of somatic mosaicism in human development, aging,\nand disease progression.\n\nResearcher",
"managed_by": "SMaHT Data Analysis Center (DAC)",
"update_frequency": "Bi-annually",
"tags": "biology,bioinformatics,genetic,genomic,imaging,life sciences,whole genome sequencing,bam,aws-pds",
"license": "NIH Genomic Data Sharing Policy - https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "smithsonian-open-access",
"name": "Smithsonian Open Access",
"description": "The Smithsonian’s mission is the \"increase and diffusion of knowledge\" and has been collecting since 1846. The Smithsonian, through its efforts to digitize its multidisciplinary collections, has created millions of digital assets and related metadata describing the collection objects. On February 25th, 2020, the Smithsonian released over 2.8 million CC0 interdisciplinary 2-D and 3-D images, related metadata, and additionally, research data from researches across the Smithsonian. The 2.8 million ",
"managed_by": "[SI](http://www.si.edu/)",
"update_frequency": "New / updated metadata and image files will be pushed weekly.",
"tags": "aws-pds,art,history,culture,museum,encyclopedic",
"license": "CC0",
"deprecated": false
},
{
"slug": "smn-ar-wrf-dataset",
"name": "SMN Hi-Res Weather Forecast over Argentina",
"description": "The Servicio Meteorológico Nacional de Argentina (SMN-Arg), the National Meteorological Service of Argentina, shares its deterministic forecasts generated with WRF 4.0 (Weather and Research Forecasting) initialized at 00 and 12 UTC every day.\n
\n
\nThis forecast includes some key hourly surface variables –2 m temperature, 2 m relative humidity, 10 m wind magnitude and direction, and precipitation–, along with other daily variables, minimum and maximum temperature.\n
\n
\nThe forecast ",
"managed_by": "[SMN](http://www.smn.gov.ar/)",
"update_frequency": "New data is added as soon as it's available. Two forecast cycles a day initialized at 00 UTC and 12 UTC.",
"tags": "aws-pds,earth observation,natural resource,weather,meteorological",
"license": "[Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org/licenses/by/2.5/ar)",
"deprecated": false
},
{
"slug": "snpeff",
"name": "SnpEff & SnpSift Genomic Variant Annotation Databases",
"description": "SnpEff is a variant annotation and effect prediction tool that annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes). It supports over 38,000 genomes and provides comprehensive genomic databases for variant annotation. The databases include reference genomes, gene annotations, protein sequences, and regulatory elements from trusted sources like ENSEMBL, RefSeq, and UCSC. SnpSift complements SnpEff by providing tools to annotate genomic variants",
"managed_by": "[Pablo Cingolani](http://www.linkedin.com/in/pablocingolani)",
"update_frequency": "Monthly",
"tags": "life sciences,genomic,variant annotation,bioinformatics,genetic,genome,cancer,protein,vcf,whole genome sequencing",
"license": "[MIT License](https://opensource.org/licenses/MIT)",
"deprecated": false
},
{
"slug": "socialgene",
"name": "SocialGene RefSeq Databases",
"description": "Precomputed SocialGene Neo4j graph databases of various sizes built from RefSeq genomes and MIBiG BGCs.",
"managed_by": "University of Wisconsin-Madison",
"update_frequency": "This database is currently what was published in our 2024 paper introducing SocialGene, but an updated database may be added in the future.",
"tags": "metagenomics,genomic,bioinformatics,microbiome,chemical biology,pharmaceutical,graph,protein,amino acid",
"license": "Where applicable, SocialGene data is released under CC0 (https://creativecommons.org/public-domain/cc0/), some individual components of the database may have their own license (see https://socialgene.github.io/precomputed_databases/2023_v0.4.1/general)",
"deprecated": false
},
{
"slug": "sofar-spotter-archive",
"name": "Sofar Spotter Archive",
"description": "This dataset includes archival hourly data from the [Sofar Spotter buoy global network] (https://weather.sofarocean.com/) from 2019 to March 2022.
",
"managed_by": "[Sofar Ocean](https://www.sofarocean.com/company/contact-us)",
"update_frequency": "As available",
"tags": "aws-pds,climate,meteorological,sustainability,weather,oceans,environmental,oceans",
"license": "[Sofar Data Access Agreement](https://sofarocean.notion.site/sofarocean/Sofar-Data-Access-Agreement-fe4e091fa0b5453d9a8bd97c4b6940d5)",
"deprecated": false
},
{
"slug": "software-heritage",
"name": "Software Heritage Graph Dataset",
"description": "[Software Heritage](https://www.softwareheritage.org/) is the largest\nexisting public archive of software source code and accompanying\ndevelopment history. The Software Heritage Graph Dataset is a fully\ndeduplicated Merkle DAG representation of the Software Heritage archive.\n\nThe dataset links together file content identifiers, source code\ndirectories, Version Control System (VCS) commits tracking evolution over\ntime, up to the full states of VCS repositories as observed by Software\nHeritage dur",
"managed_by": "Software Heritage",
"update_frequency": "Data is updated yearly",
"tags": "aws-pds,source code,open source software,free software,digital preservation",
"license": "The term \"Software Heritage Graph Dataset\" designates the internal structure of the Software Heritage archive, and explicitly excludes the file contents.\nThe \"Software Heritage Graph Dataset\" is distributed under the Creative Commons Attribution 4.0 International license.\nFor terms of use of all oth",
"deprecated": false
},
{
"slug": "sondehub-telemetry",
"name": "SondeHub Radiosonde Telemetry",
"description": "SondeHub Radiosonde telemetry contains global radiosonde (weather balloon) data captured by SondeHub from our participating radiosonde_auto_rx receiving stations. radiosonde_auto_rx is a open source project aimed at receiving and decoding telemetry from airborne radiosondes using software-defined-radio techniques, enabling study of the telemetry and sometimes recovery of the radiosonde itself.\nCurrently 313 receiver stations are providing data for an average of 384 radiosondes a day. The data w",
"managed_by": "[SondeHub](https://sondehub.org/)",
"update_frequency": "Data is updated as we receive it",
"tags": "aws-pds,climate,environmental,weather,GPS",
"license": "Creative Commons BY-SA 2.0",
"deprecated": false
},
{
"slug": "sorel-20m",
"name": "Sophos/ReversingLabs 20 Million malware detection dataset",
"description": "A dataset intended to support research on machine learning\ntechniques for detecting malware. It includes metadata and EMBER-v2\nfeatures for approximately 10 million benign and 10 million malicious\nPortable Executable files, with disarmed but otherwise complete\nfiles for all malware samples. All samples are labeled using Sophos\nin-house labeling methods, have features extracted using the\nEMBER-v2 feature set, well as metadata obtained via the pefile\npython library, detection counts obtained via",
"managed_by": "Sophos AI",
"update_frequency": "At most annually",
"tags": "aws-pds,cyber security,deep learning,labeled,machine learning",
"license": "See the [Terms of Use](https://github.com/sophos-ai/SOREL-20M/blob/master/Terms%20and%20Conditions%20of%20Use.pdf)",
"deprecated": false
},
{
"slug": "southern-california-earthquakes",
"name": "Southern California Earthquake Data",
"description": "This dataset contains ground motion velocity and acceleration seismic waveforms recorded by the Southern California Seismic Network (SCSN) and archived at the Southern California Earthquake Data Center (SCEDC). A Distributed Acousting Sensing (DAS) dataset is included.",
"managed_by": "[Southern California Earthquake Data Center](https://scedc.caltech.edu)",
"update_frequency": "Daily",
"tags": "aws-pds,earth observation,earthquakes,seismology",
"license": "SCEDC herby grants the non-exclusive, royalty free, non-transferable, worldwide right and license to use, reproduce and publicly display in all media public data from the Southern California Seismic Network. Please cite the SCEDC (doi:10.7909/C3WD3xH1) and SCSN (doi:10.7914/SN/CI) for any research p",
"deprecated": false
},
{
"slug": "spacenet",
"name": "SpaceNet",
"description": "SpaceNet, launched in August 2016 as an open innovation project offering a repository of freely available\nimagery with co-registered map features. Before SpaceNet, computer vision researchers had minimal options\nto obtain free, precision-labeled, and high-resolution satellite imagery. Today, SpaceNet hosts datasets\ndeveloped by its own team, along with data sets from projects like IARPA’s Functional Map of the World (fMoW).\n",
"managed_by": "[SpaceNet](https://spacenet.ai/)",
"update_frequency": "New imagery and features are added quarterly",
"tags": "aws-pds,geospatial,computer vision,machine learning,earth observation,disaster response,satellite imagery",
"license": "Various (See [here](https://spacenet.ai/datasets/) for more details)",
"deprecated": false
},
{
"slug": "sparc",
"name": "SPARC: Datasets bridging the body and the brain",
"description": "The SPARC Datasets comprise a collection of scientific data that is focused\non bridging the body and the brain. The datasets focus on neural connectivity,\norgan innervation and detailed anatomical mapping of the peripheral nervous system.\nSPARC datasets distinguish themselves from other data resources through its multi-modal\napproach to scientific data and integrates molecular, imaging, timeseries and other\ndatatypes associated with the interaction between the peripheral nervous system and \norga",
"managed_by": "[The SPARC Data and Resource Center](https://sparc.science/about)",
"update_frequency": "Continually adding new datasets and releasing versions of datasets",
"tags": "bioinformatics,life sciences,microscopy,neurophysiology,neuroscience,electrophysiology",
"license": "CC-BY-4.0",
"deprecated": false
},
{
"slug": "spartan-cloud",
"name": "SPARTAN Data",
"description": "SPARTAN (Surface PARTiculate mAtter Network) measures and provides surface ambient particulate matter (PM2.5 and PM10) concentration and the chemical composition around the world, with the purpose of connecting ground-based PM2.5 and satellite remote sensing.",
"managed_by": "The [Atmospheric Composition Analysis Group](https://sites.wustl.edu/acag/)",
"update_frequency": "New measurement or estimation products will be added when available, usually multiple times a month.",
"tags": "aws-pds,environmental,air quality",
"license": "SPARTAN data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1)",
"deprecated": false
},
{
"slug": "spatial-ucr",
"name": "Geosnap Data, Center for Geospatial Sciences",
"description": "This bucket contains multiple datasets (as Quilt packages) created by the\nCenter for Geospatial Sciences (CGS) at the University of California-Riverside.\nThe data in this bucket contains the following:\n\n1) Tabular and geographic data from the US Census\n2) Land Cover imagery collected from [Multi-Resolution Land Characteristics Consortium](https://www.mrlc.gov/)\n3) Road network data processed from OpenStreetMap\n",
"managed_by": "[UCR Center for Geospatial Sciences](https://spatial.ucr.edu)",
"update_frequency": "Annually",
"tags": "aws-pds,urban,geospatial,demographics",
"license": "BSD",
"deprecated": false
},
{
"slug": "spatiam-nlra-iss-experiments",
"name": "Spatiam Corporation National Lab Research Announcement International Space Station Technology Demonstration",
"description": "Experiment data for the Spatiam DTN Network Platform Technology demonstration carried to test a Delay and Disruption Tolerant Network between the ISS and Earth.",
"managed_by": "[Spatiam Corporation](https://www.spatiam.com/)",
"update_frequency": "No updates envisioned after the conclusion of the experiment.",
"tags": "network traffic,telecommunications",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "speedtest-global-performance",
"name": "Speedtest by Ookla Global Fixed and Mobile Network Performance Maps",
"description": "Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results c",
"managed_by": "[Ookla](https://www.ookla.com/ookla-for-good)",
"update_frequency": "Quarterly",
"tags": "analytics,aws-pds,broadband,cities,civic,disaster response,geospatial,global,government spending,infrastructure",
"license": "\"[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)\"\n",
"deprecated": false
},
{
"slug": "spherex-qr",
"name": "SPHEREx Quick Release (QR): An All-Sky Spectral Survey",
"description": "The Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is a NASA Astrophysics Medium-class Explorer (MIDEX) mission launched in March 2025. During its planned two-year mission, SPHEREx will perform the first ever all-sky spectral survey in the optical to near-infrared (0.75-5 microns). SPHEREx data will be used to probe inflation and the early universe, trace the history of galactic light production, and investigate the origin of planetary syst",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "SPHEREx QR is updated weekly. The data may also be presented in new ways as the products become available.",
"tags": "aws-pds,astronomy,imaging,object detection,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "spitzer-seip",
"name": "Spitzer Enhanced Imaging Products (SEIP) Super Mosaics",
"description": "Spitzer was an infrared astronomy space telescope with imaging from 3 to 160 microns and spectroscopy from 5 to 37 microns, launched into an Earth-trailing solar orbit as the last of NASA's Great Observatories. The SEIP Super Mosaics include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. Data from multiple programs are combined where appropriate. Cryogenic Release v3.0 includes Spitzer data taken during commissioning and cryogenic operations, in",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "This data set may be updated once or twice in the future.",
"tags": "aws-pds,astronomy,imaging,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "ssl4eo-multi-product-data",
"name": "SSL4EO S12 Landsat Multi Product Dataset",
"description": "This dataset combines SSL4EO-S12 and SSL4EO-L to create a multi-view dataset for multi-modal fusion using self-supervised learning for earth observation.",
"managed_by": "Sankranti Joshi",
"update_frequency": "Not updated",
"tags": "satellite imagery",
"license": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
"deprecated": false
},
{
"slug": "st-open-data",
"name": "SpaceEye-T VVHR EO Open Data",
"description": "SpaceEye-T satellite collects the highest resolution optical imagery among the commercial satellites, 25 cm resolution. The Open Data features various satellite images around the world for end users to experience the power of VVHR optical data.\n",
"managed_by": "[SI Imaging Services](https://www.si-imaging.com/)",
"update_frequency": "The dataset is frequently updated. The frequent updates include the time-series data for regular monitoring, and the data for disaster management. SI Imaging wants to provide the user expierence on what is possible with VVHR optical satellite data. If you have a suggestion for a new location, feedback on the dataset, or any questions, contact us.",
"tags": "aws-pds,satellite imagery,earth observation,disaster response,geospatial,image processing",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0).\nFor more information, See the document \"ST-1 Product Terms of Use\" at [our Terms of Use webpage](https://si-imaging.com/page/73)\n",
"deprecated": false
},
{
"slug": "stdpopsim_kern",
"name": "stdpopsim species resources",
"description": "Contains all resources (genome specifications, recombination maps, etc.) required for species specific simulation with the stdpopsim package. These resources are originally from a variety of other consortium and published work but are consolidated here for ease of access and use. If you are interested in adding a new species to the stdpopsim resource please raise an issue on the stdpopsim GitHub page to have the necessary files added here.",
"managed_by": "Andrew Kern & Jerome Kelleher",
"update_frequency": "Data will be added as new species, genome assemblies, and genetic map data for already included species become available.",
"tags": "aws-pds,genetic maps,life sciences,population genetics,recombination maps,simulations",
"license": "Please see the individual datasets compiled here for licensing details and make sure to cite the original sources of any elements of this data that you use.",
"deprecated": false
},
{
"slug": "steineggerlab",
"name": "Steinegger Lab Datasets",
"description": "The Steinegger Lab Dataset comprises biological databases and resources critical for protein sequence and structure analysis, developed to support ColabFold, MMseqs2, and Foldseek/Foldcomp—three high-performance computational tools widely used in bioinformatics.\n\nThe MMseqs2 dataset serves as the backbone for our fast structure prediction tool, ColabFold, and includes UniRef30, BFD, and the ColabFold environmental databases.\nThese datasets are specifically designed for the rapid generation of mu",
"managed_by": "[Steinegger Lab, Seoul National University](https://steineggerlab.com)",
"update_frequency": "Occasionally, where new data is available",
"tags": "open source software,protein,protein folding,bioinformatics,metagenomics,life sciences,aws-pds",
"license": "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "stoic2021-training",
"name": "STOIC2021 Training",
"description": "The STOIC project collected Computed Tomography (CT) images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence, based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This S3 bucket contains the training sample of the STOIC dataset ",
"managed_by": "Radboud University Medical Center",
"update_frequency": "The full training set was published at the release.",
"tags": "aws-pds,life sciences,computed tomography,computer vision,coronavirus,COVID-19,grand-challenge.org,imaging,SARS-CoV-2",
"license": "CC-BY-NC 4.0",
"deprecated": false
},
{
"slug": "sucho",
"name": "SUCHO Ukrainian Cultural Heritage Web Archives",
"description": "The dataset contains web archives of Open Access collections of digitised cultural heritage from more than 3,000+ websites of Ukrainian cultural institutions, such as museums, libraries or archives. The web archives have been produced by SUCHO, which is a volunteer group of more than 1,300 international cultural heritage professionals – librarians, archivists, researchers, programmers - who have joined forces to save as much digitised cultural heritage during the 2022 invasion of Ukraine before ",
"managed_by": "Saving Ukrainian Cultural Heritage Online (SUCHO)",
"update_frequency": "Periodically",
"tags": "ukraine,internet,cultural preservation,aws-pds",
"license": "Public Domain (CC0)",
"deprecated": false
},
{
"slug": "sudachi",
"name": "Sudachi Language Resources",
"description": "Japanese dictionaries and pre-trained models (word embeddings and language models) for natural language processing.\n[SudachiDict](https://github.com/WorksApplications/SudachiDict) is the dictionary for a Japanese tokenizer (morphological analyzer) [Sudachi](https://github.com/WorksApplications/Sudachi).\n[chiVe](https://github.com/WorksApplications/chiVe) is Japanese pretrained word embeddings (word vectors), trained using the ultra-large-scale web corpus NWJC by National Institute for Japanese L",
"managed_by": "[Works Applications](https://www.worksap.co.jp/about/csr/nlp/)",
"update_frequency": "The dictionaries are updated every few months to include neologism and fixes for the existing words.",
"tags": "aws-pds,natural language processing",
"license": "Apache-2.0",
"deprecated": false
},
{
"slug": "surface-pm2-5-v6gl",
"name": "SatPM2.5",
"description": "Fine particulate matter (PM2.5) concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the vari",
"managed_by": "https://sites.wustl.edu/acag/",
"update_frequency": "Yearly",
"tags": "aws-pds,atmosphere,netcdf,environmental,air quality,health",
"license": "Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "surftemp-sst",
"name": "Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1",
"description": "Global daily-mean sea surface temperatures, presented on a 0.05° latitude-longitude grid, with gaps between available daily observations filled by statistical means, spanning late 1981 to recent time. Suitable for large-scale oceanographic meteorological and climatological applications, such as evaluating or constraining environmental models or case-studies of marine heat wave events. Includes temperature uncertainty information and auxiliary information about land-sea fraction and sea-ice cove",
"managed_by": "[University of Reading, Department of Meteorology](https://www.reading.ac.uk/met/)",
"update_frequency": "yearly",
"tags": "aws-pds,earth observation,oceans,climate,environmental,global,geospatial",
"license": "Creative Commons Licence by attribution (https://creativecommons.org/licenses/by/4.0/). For attribution cite www.nature.com/articles/s41597-019-0236-x.",
"deprecated": false
},
{
"slug": "surya-bench",
"name": "Surya Bench",
"description": "This dataset provides machine learning (ML)-ready solar data curated from NASA’s Solar Dynamics Observatory (SDO), covering observations from May 13, 2010, to July 31, 2024. It includes Level-1.5 processed data from:\n- Atmospheric Imaging Assembly (AIA):\n- Helioseismic and Magnetic Imager (HMI):\nThe dataset is designed to facilitate large-scale ML applications in heliophysics, such as solar activity forecasting, unsupervised representation learning, and scientific foundation model development.\n",
"managed_by": "NASA IMPACT",
"update_frequency": "This is the final version of the Dataset.",
"tags": "machine learning,solar,aws-pds,heliophysics",
"license": "Creative Commons Attribution 4.0 International.\n",
"deprecated": false
},
{
"slug": "synthea-coherent-data",
"name": "Synthea Coherent Data Set",
"description": "This is a synthetic data set that includes FHIR resources, DICOM images, genomic data, physiological data (i.e., ECGs), and simple clinical notes. FHIR links all the data types together.",
"managed_by": "[The MITRE Corporation](https://www.mitre.org)",
"update_frequency": "Rarely",
"tags": "aws-pds,health,bioinformatics,life sciences,medicine,csv,dicom,genomic,imaging",
"license": "[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "synthea-omop",
"name": "Synthea synthetic patient generator data in OMOP Common Data Model",
"description": "The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the [OMOP Common Data Model](https://www.ohdsi.org/data-standardization/) format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from co",
"managed_by": "[Amazon Web Sevices](https://aws.amazon.com/)",
"update_frequency": "Not updated",
"tags": "aws-pds,bioinformatics,health,life sciences,natural language processing,us",
"license": "https://github.com/synthetichealth/synthea/blob/master/LICENSE",
"deprecated": false
},
{
"slug": "tabula-muris-senis",
"name": "Tabula Muris Senis",
"description": "Tabula Muris Senis is a comprehensive compendium of single cell transcriptomic data from the model organism *Mus musculus* comprising more than 500,000 cells from 18 organs and tissues across the mouse lifespan. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of ag",
"managed_by": "[Chan Zuckerberg Biohub](https://www.czbiohub.org/)",
"update_frequency": "This is the first version of the dataset and it will be updated after the manuscript has been peer reviewed.",
"tags": "aws-pds,biology,encyclopedic,genomic,health,life sciences,medicine,single-cell transcriptomics",
"license": "https://github.com/czbiohub/tabula-muris-senis/blob/master/LICENSE",
"deprecated": false
},
{
"slug": "tabula-muris",
"name": "Tabula Muris",
"description": "Tabula Muris is a compendium of single cell transcriptomic data from the model organism *Mus musculus* comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations, and allow for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approac",
"managed_by": "[Chan Zuckerberg Biohub](https://www.czbiohub.org/)",
"update_frequency": "This is the final version of the dataset, it will not be updated.",
"tags": "aws-pds,biology,encyclopedic,genomic,health,life sciences,medicine",
"license": "https://github.com/czbiohub/tabula-muris/blob/master/LICENSE",
"deprecated": false
},
{
"slug": "tabula-sapiens",
"name": "Tabula Sapiens",
"description": "Tabula Sapiens is a benchmark, first-draft human cell atlas of over 1.1M cells from 28 organs of 24 normal human subjects. \nThis work is the product of the Tabula Sapiens Consortium. \nTaking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects, \nand allows detailed analysis and comparison of cell types that are shared between tissues. \nOur work creates a detailed portrait of cell types as well as their distribution and variation in gene ex",
"managed_by": "[Chan Zuckerberg Biohub](https://www.czbiohub.org/)",
"update_frequency": "This is the second version of the dataset. We expect to update every 1-2 years until project completion.",
"tags": "aws-pds,biology,encyclopedic,genetic,genomic,health,life sciences,medicine,single-cell transcriptomics",
"license": "https://tabula-sapiens.sf.czbiohub.org/whereisthedata",
"deprecated": false
},
{
"slug": "talend-covid19",
"name": "COVID-19 Harmonized Data",
"description": "A harmonized collection of the core data pertaining to COVID-19 reported cases by geography, in a format prepared for analysis",
"managed_by": "[Talend / Stitch](http://www.stitchdata.com/)",
"update_frequency": "New COVID-19 data is added twice daily",
"tags": "aws-pds,COVID-19,coronavirus,life sciences",
"license": "There are no restrictions on the use of this data.",
"deprecated": false
},
{
"slug": "target",
"name": "Therapeutically Applicable Research to Generate Effective Treatments (TARGET)",
"description": "Therapeutically Applicable Research to Generate Effective Treatments (TARGET) is the collaborative effort of a large, diverse consortium of extramural and NCI investigators. The goal of the effort is to accelerate molecular discoveries that drive the initiation and progression of hard-to-treat childhood cancers and facilitate rapid translation of those findings into the clinic.\nTARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initia",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,cancer,genomic,life sciences,whole genome sequencing,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "targetepigenomics",
"name": "Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET)",
"description": "The TaRGET (Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription) Program is a research consortium funded by the National Institute of Environmental Health Sciences (NIEHS). The goal of the collaboration is to address the role of environmental exposures in disease pathogenesis as a function of epigenome perturbation, including understanding the environmental control of epigenetic mechanisms and assessing the utility of surrogate tissue analysis in mouse models o",
"managed_by": "TaRGET II Data Coordination Center (TaRGET-DCC)",
"update_frequency": "TaRGET-DCC offers monthly data releases, although this dataset may not be updated at every release.",
"tags": "biology,bioinformatics,genetic,genomic,life sciences,environmental,epigenomics,aws-pds",
"license": "External data users may freely download, analyze, and publish results based on any TaRGET II data provided here without restrictions.",
"deprecated": false
},
{
"slug": "tcga",
"name": "The Cancer Genome Atlas",
"description": "The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer. TCGA has analyzed matched tumor and normal tissues from 11,000 patients, allowing for the comprehensive characterization of 33 cancer types and subtypes, including 10 rare cancers.\nThe dataset contains open Clinical Supplement, Biosp",
"managed_by": "[Center for Translational Data Science at The University of Chicago](https://ctds.uchicago.edu/)",
"update_frequency": "Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers monthly data releases,\nalthough this dataset may not be updated at every release.\n",
"tags": "aws-pds,life sciences,cancer,genomic,whole genome sequencing,STRIDES",
"license": "NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access-policies",
"deprecated": false
},
{
"slug": "terrafusion",
"name": "Terra Fusion Data Sampler",
"description": "The Terra Basic Fusion dataset is a fused dataset of the original Level 1 radiances\nfrom the five Terra instruments. They have been fully validate to contain the original\nTerra instrument Level 1 data. Each Level 1 Terra Basic Fusion file contains one full\nTerra orbit of data and is typically 15 – 40 GB in size, depending on how much data was\ncollected for that orbit. It contains instrument radiance in physical units; radiance\nquality indicator; geolocation for each IFOV at its native resolution",
"managed_by": "University of Illinois",
"update_frequency": "Static, with a planned update for years 2016-2020 in the future.",
"tags": "aws-pds,geospatial,satellite imagery",
"license": "Creative Commons Level 0",
"deprecated": false
},
{
"slug": "terrain-tiles",
"name": "Terrain Tiles",
"description": "A global dataset providing bare-earth terrain heights, tiled for easy usage and provided on S3.",
"managed_by": "Mapzen, a Linux Foundation project",
"update_frequency": "New data is added based on community feedback",
"tags": "aws-pds,agriculture,elevation,earth observation,geospatial,disaster response",
"license": "https://github.com/tilezen/joerd/blob/master/docs/attribution.md",
"deprecated": false
},
{
"slug": "tgs-opendata-poseidon",
"name": "Poseidon 3D Seismic, Australia",
"description": "Near, mid, far, full stack (with AGC) imaged 3D seismic data. We also include the decimated\nstacking velocity field. The dataset is used in oil and gas exploration. Survey size is\napproximately 2,900 km2.\n\nCoordinate system used is: GDA94 / MGA Zone 51 Petrel, 700004\n\nDatasets are converted to open-source MDIO format (v1 specification).\n\nOriginal SEG-Y files are licensed as CC BY 3.0 AU and are downloaded from\n[Google Drive](https://drive.google.com/drive/folders/0B7brcf-eGK8Cbk9ueHA0QUU4Zjg?res",
"managed_by": "[TGS](https://www.tgs.com)",
"update_frequency": "Dataset is static.",
"tags": "seismology,geophysics,exploration",
"license": "CC BY 4.0",
"deprecated": false
},
{
"slug": "tiger",
"name": "TIGER Training",
"description": "\"This dataset contains the training data for the [Tumor InfiltratinG lymphocytes in breast cancER or TIGER](https://tiger.grand-challenge.org) challenge. TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathology slides. TILs are proving to be an important biomarker in cancer patients as they can play a part in killing tumor cells, particularly in some types of breast cancer. Identifying and measuring TILs can help to bette",
"managed_by": "Radboud University Medical Center",
"update_frequency": "As required",
"tags": "aws-pds,life sciences,cancer,computational pathology,grand-challenge.org,histopathology,deep learning,computer vision",
"license": "CC BY-NC 4.0",
"deprecated": false
},
{
"slug": "topical-chat-enriched",
"name": "Enriched Topical-Chat Dataset for Knowledge-Grounded Dialogue Systems",
"description": "This dataset provides extra annotations on top of the publicly released\nTopical-Chat dataset(https://github.com/alexa/Topical-Chat) which will help in reproducing the results in our paper\n\"Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems\" (https://arxiv.org/abs/2005.12529?context=cs.CL). \nThe dataset contains 5 files: train.json, valid_freq.json, valid_rare.json, test_freq.json and test_rare.json. \nEach of these files will have additional annotations on top of the",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,machine learning,conversation data",
"license": "[CDLA-Sharing](https://cdla.io/sharing-1-0/)",
"deprecated": false
},
{
"slug": "tsbench",
"name": "TSBench",
"description": "TSBench comprises thousands of benchmark evaluations for time series forecasting methods. It provides various metrics (i.e. measures of accuracy, latency, number of model parameters, ...) of 13 time series forecasting methods across 44 heterogeneous datasets. Time series forecasting methods include both classical and deep learning methods while several hyperparameters settings are evaluated for the deep learning methods.\n\nIn addition to the tabular data providing the metrics, TSBench includes th",
"managed_by": "[Amazon Web Services](https://aws.amazon.com)",
"update_frequency": "Not expected to be updated",
"tags": "machine learning,deep learning,meta learning,benchmark,time series forecasting",
"license": "[CC BY](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "ubc-sunflower-genome",
"name": "University of British Columbia Sunflower Genome Dataset",
"description": "This dataset captures Sunflower's genetic diversity originating\nfrom thousands of wild, cultivated, and landrace sunflower\nindividuals distributed across North America.\n\nThe data consists of raw sequences and associated botanical metadata,\naligned sequences (to three different reference genomes), and sets of\nSNPs computed across several cohorts.\n",
"managed_by": "The Rieseberg Lab at the University of British Columbia",
"update_frequency": "Twice per year.",
"tags": "aws-pds,agriculture,biodiversity,bioinformatics,biology,food security,genetic,genomic,life sciences,whole genome sequencing",
"license": "Public Domain",
"deprecated": false
},
{
"slug": "ucsc-genome-browser",
"name": "UCSC Genome Browser Sequence and Annotations",
"description": "The UCSC Genome Browser is an online graphical viewer for genomes, a genome browser, hosted by the University of California, Santa Cruz (UCSC). The interactive website offers access to genome sequence data from a variety of vertebrate and invertebrate species and major model organisms, integrated with a large collection of aligned annotations. This dataset is a copy of the MySQL tables in MyISAM binary and tab-sep format and all binary files in custom formats, sometimes referred as 'gbdb'-files.",
"managed_by": "University of California Santa Cruz Genome Institute",
"update_frequency": "Monthly",
"tags": "aws-pds,genetic,genomic,life sciences,bioinformatics,biology",
"license": "[Public domain, some tracks require attribution](https://genome.ucsc.edu/license/)",
"deprecated": false
},
{
"slug": "ucsf-bmsr",
"name": "The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset",
"description": "The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI dataset consisting of 560 brain MRIs from 412 patients with expert annotations of 5136 brain metastases. Data consists of registered and skull stripped T1 post-contrast, T1 pre-contrast, FLAIR and subtraction (T1 pre-contrast - T1 post-contrast) images and voxelwise segmentations of enhancing brain metastases in NifTI format. ",
"managed_by": "[UCSF Center for Intelligent Imaging](https://intelligentimaging.ucsf.edu/)",
"update_frequency": "ad hoc",
"tags": "aws-pds,cancer,life sciences,magnetic resonance imaging,medicine,medical imaging,radiology",
"license": "Custom, non-commerical, attribution, no redistribution, no re-identification. Full license is available [here](http://d1atcui2oic5ew.cloudfront.net/terms/2a109330-9769-44af-a18b-c4be64ee26b5).",
"deprecated": false
},
{
"slug": "ucsf-rmac",
"name": "UCSF Renal Mass CT Dataset",
"description": "This dataset provides a set of 831 3D Multiphase CT exams of renal masses, registered across phases with annotations identifying the masses",
"managed_by": "[UCSF Larson Advanced Imaging Lab](https://larsonlab.github.io/)",
"update_frequency": "ad hoc",
"tags": "aws-pds,cancer,life sciences,computed tomography,medicine,medical imaging,radiology",
"license": "https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "uk-met-office",
"name": "Retired – UK Met Office Atmospheric Deterministic and Probabilistic Forecasts",
"description": "As of the 13th July, no further Met Office forecasts or data will be available via this service.
\n\nMeteorological data reusers now have an exciting opportunity to sample, experiment and evaluate\nMet Office atmospheric model data, whilst also experiencing a transformative method of requesting\ndata via Restful APIs on AWS.\n\nFor information about the data see the [Met Office website](https://www.metoffice.gov.uk/services/data/met-office-data-for-reuse/discovery).\nFor examples of using the data c",
"managed_by": "[UK Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "Daily (with 24 hour delay)",
"tags": "aws-pds,agriculture,earth observation,climate,weather,meteorological",
"license": "This data is made available under a [Creative Commons licence](https://creativecommons.org/licenses/by-nc-nd/4.0/).\nThis data is free to use for non-commercial purposes under the terms of the licence. You must\nensure that you acknowledge the source of this data as prescribed by this licence. Data or",
"deprecated": true
},
{
"slug": "ukbb-ld",
"name": "UK Biobank Linkage Disequilibrium Matrices",
"description": "Linkage disequilibrium (LD) matrices of UK Biobank participants of a British ancestry, based on imputed genotypes.",
"managed_by": "[Omer Weissbrod](https://github.com/omerwe)",
"update_frequency": "None",
"tags": "aws-pds,genetic,genome wide association study,genomic,life sciences,population genetics",
"license": "[CC BY-4.0](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "ukbppp",
"name": "UK Biobank Pharma Proteomics Project (UKB-PPP)",
"description": "The UKB-PPP is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,219 UKB participants. As part of a collaborative analysis across the thirteen UKB-PPP partners, we conducted comprehensive protein quantitative trait loci (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 85% are newly discovered, in addition to ancestry-specific pQTL mapping in non-Europeans. We ident",
"managed_by": "Sage Bionetworks",
"update_frequency": "Never",
"tags": "aws-pds,genome wide association study,population genetics,life sciences",
"license": "[CC BY]",
"deprecated": false
},
{
"slug": "umbra-open-data",
"name": "Umbra Synthetic Aperture Radar (SAR) Open Data",
"description": "Umbra satellites generate the highest resolution Synthetic Aperture Radar (SAR) imagery ever offered from space, up to 16-cm resolution. SAR can capture images at night, through cloud cover, smoke and rain. SAR is unique in its abilities to monitor changes. The Open Data Program (ODP) features over twenty diverse time-series locations that are updated frequently, allowing users to experiment with SAR's capabilities. We offer single-looked spotlight mode in either 16cm, 25cm, 35cm, 50cm, or 1m re",
"managed_by": "[Umbra](http://umbra.space/)",
"update_frequency": "New data is added frequently. The frequent updates enable users to analyze the time-series data to detect changes in each location. Umbra's goal is to provide the user with a deep understanding on what's possible with our data.",
"tags": "aws-pds,synthetic aperture radar,stac,satellite imagery,earth observation,image processing,geospatial",
"license": "All data is provided with a Creative Commons License ([CC by 4.0](https://umbra.space/open-license)), which gives you the right to do just about anything you want with it.\n",
"deprecated": false
},
{
"slug": "uniprot",
"name": "UniProt",
"description": "The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt databases are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), and the UniProt Archive (UniParc). The UniProt consortium and host institutions [EMBL-EBI](https://www.ebi.ac.uk), [SIB Swiss Institute of Bioinformatics](https://www.sib.swiss) and [PIR](https://proteininformationresource.org/) are committed to the long-term preservation of the [Un",
"managed_by": "[SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the UniProt Consortium",
"update_frequency": "Under 1 months after a new UniProt release.",
"tags": "aws-pds,chemistry,protein,enzyme,molecule,life sciences,bioinformatics,biology,RDF,graph",
"license": "http://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "us-hiring-rates-pandemic",
"name": "Covid Job Impacts - US Hiring Data Since March 1 2020",
"description": "This dataset provides daily updates on the volume of US job listings filtered by geography industry job family and role; normalized to pre-covid levels.\n\nThese data files feed the business intelligence visuals at covidjobimpacts.greenwich.hr, a public-facing site hosted by Greenwich.HR and OneModel Inc.\nData is derived from online job listings tracked continuously, calculated daily and published nightly. On average data from 70% of all new US jobs are captured,\nand the dataset currently contain",
"managed_by": "[Greenwich.HR](http://www.greenwich.hr/)",
"update_frequency": "Daily",
"tags": "aws-pds,COVID-19,hiring,economics,financial markets,market data",
"license": "This data is made available under a [Creative Commons license](https://creativecommons.org/licenses/by-nc-nd/4.0/).\nThis data is free to use for non-commercial purposes under the terms of the license. You must\nensure that you acknowledge the source of this data as prescribed by this license. Data or",
"deprecated": false
},
{
"slug": "usearch-molecules",
"name": "USearch Molecules",
"description": "Collection of 7 billion small molecules in SMILES notation with 28 billion fingerprints, including MACCS, ECFP4, FCFP4, and PubChem, with pre-constructed USearch indexes over them.",
"managed_by": "[Ash Vardanian](https://ashvardanian.com)",
"update_frequency": "Not updated",
"tags": "aws-pds,life sciences,biology,chemical biology,pharmaceutical",
"license": "[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)",
"deprecated": false
},
{
"slug": "usgs-landsat",
"name": "USGS Landsat",
"description": "This joint NASA/USGS program provides the longest continuous space-based record of \nEarth’s land in existence. Every day, Landsat satellites provide essential information \nto help land managers and policy makers make wise decisions about our resources and our environment.\nData is provided for Landsats 1, 2, 3, 4, 5, 7, 8, and 9 (excludes Landsat 6).\n\nAs of June 28, 2023 ([announcement](https://www.usgs.gov/landsat-missions/news/changes-coming-landsats-simple-notification-service-amazon-web-servi",
"managed_by": "[United States Geological Survey](https://www.usgs.gov)",
"update_frequency": "New scenes are added daily.",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,natural resource,disaster response,stac,cog",
"license": "There are no restrictions on Landsat data downloaded from the USGS; it can be used or \nredistributed as desired. We do request that you include a [statement of the data source](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con) ",
"deprecated": false
},
{
"slug": "usgs-lidar",
"name": "USGS 3DEP LiDAR Point Clouds",
"description": "The goal of the [USGS 3D Elevation Program ](https://www.usgs.gov/core-science-systems/ngp/3dep) (3DEP) is to collect elevation data in the form of light detection and ranging (LiDAR) data over the conterminous United States, Hawaii, and the U.S. territories, with data acquired over an 8-year period. This dataset provides two realizations of the 3DEP point cloud data. The first resource is a public access organization provided in [Entwine Point Tiles](https://entwine.io/entwine-point-tile.html) ",
"managed_by": "[Hobu, Inc.](https://hobu.co)",
"update_frequency": "Periodically",
"tags": "aws-pds,agriculture,elevation,disaster response,geospatial,lidar,stac",
"license": "US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map",
"deprecated": false
},
{
"slug": "usgs_aqr",
"name": "Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States",
"description": "Aquatic reflectance produced with the dark spectrum fitting (DSF) algorithm as implemented in the Atmospheric Correction for OLI “lite” (ACOLITE) software (version 20221114.0). Aquatic reflectance is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by pi.",
"managed_by": "[United States Geological Survey](https://www.usgs.gov)",
"update_frequency": "New scenes are added daily.",
"tags": "aws-pds,earth observation,satellite imagery,geospatial,natural resource,cog,water",
"license": "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to ac",
"deprecated": false
},
{
"slug": "vadcr-crmp-aws",
"name": "DCR Office of Resilience Planning – Public File Repository",
"description": "The Virginia Department of Conservation and Recreation’s Office of Resilience Planning maintains this public file repository to provide access to flood resilience open data products. The repository is designed to house public data produced for the Virginia Coastal Resilience Master Plan (CRMP), Virginia Flood Protection Master Plan (VFPMP), and other purposes. At present, the repository hosts only data products produced for the CRMP Phase II (2025) and Phase I (2021). \n
\n",
"managed_by": "[Virginia Department of Conservation and Recreation](https://www.dcr.virginia.gov)",
"update_frequency": "Every 5 years or as data becomes available",
"tags": "aws-pds,coastal,floods",
"license": "Conditions of Release - Data is available by permission of the Virginia Department of Conservation and Recreation. Use of data in publications, either digital or hardcopy, must be cited as follows:. . Virginia Department of Conservation and Recreation.\n
\nDisclaimer -",
"deprecated": false
},
{
"slug": "venus-l2a-cogs",
"name": "VENUS L2A Cloud-Optimized GeoTIFFs",
"description": "The [Venµs science mission](https://www.theia-land.fr/en/product/venus/) is a joint research mission undertaken by CNES and ISA, \nthe Israel Space Agency. It aims to demonstrate the effectiveness of high-resolution multi-temporal observation optimised through \nCopernicus, the global environmental and security monitoring programme. Venµs was launched from the Centre Spatial Guyanais by a \nVEGA rocket, during the night from 2017, August 1st to 2nd. Thanks to its multispectral camera (12 spectral b",
"managed_by": "[EarthDaily Analytics](https://earthdaily.com/)",
"update_frequency": "New Venus data are added regularly",
"tags": "aws-pds,agriculture,earth observation,satellite imagery,geospatial,image processing,natural resource,disaster response,cog,stac",
"license": "https://creativecommons.org/licenses/by-nc/4.0/",
"deprecated": false
},
{
"slug": "vesuvius-challenge-herculaneum-scrolls",
"name": "Vesuvius Challenge - CT Scans of Herculaneum Papyri",
"description": "This dataset contains reconstructed micro-CT volumes of carbonized Herculaneum papyri\nproduced as part of the Vesuvius Challenge. The scanned scroll library survived the\neruption of Mount Vesuvius in AD 79. It is the only intact library known to have\nsurvived from antiquity.\nThe volumetric reconstructions are distributed as OME-Zarr multiscale datasets to support\nresearch in virtual unwrapping, segmentation, and text recovery of ancient scrolls.\nDeciphering these scrolls could forever change our",
"managed_by": "[Vesuvius Challenge](https://scrollprize.org/)",
"update_frequency": "As additional papyri are scanned and reconstructed.",
"tags": "aws-pds,cultural preservation,digital preservation,history,computed tomography,x-ray microtomography,x-ray tomography,x-ray,imaging,image processing",
"license": "[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)",
"deprecated": false
},
{
"slug": "vf-libraries",
"name": "AdaptiveFlow Ligand Libraries",
"description": "AdaptiveFlow Versions of Ligand Libraries in Ready-To-Dock Format",
"managed_by": "[AdaptiveFlow Project](https://adaptive-flow.ai/)",
"update_frequency": "Every ~3 years",
"tags": "aws-pds,structural biology,pharmaceutical,medicine,bioinformatics,life sciences",
"license": "There are no restrictions on the use of this data. Redistribution is not allowed without permission from Christoph Gorgulla. Keeping access credentials have to be kept confidential (each user gets their own access credentials).",
"deprecated": false
},
{
"slug": "virtual_shizuoka",
"name": "Virtual Shizuoka, 3D Point Cloud Data",
"description": "This dataset comprises high-precision 3D point cloud data that encompasses the entire Shizuoka prefecture in Japan, covering 7,200 out of its 7,777 square kilometers. The data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort.This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving.",
"managed_by": "[AIGID](https://aigid.jp/)",
"update_frequency": "Currently not scheduled",
"tags": "aws-pds,bathymetry,disaster response,elevation,geospatial,japanese,land,lidar,mapping",
"license": "Creative Commons Attribution 4.0 International (CC-BY 4.0) and Open Data Commons Open Database License (ODbL)",
"deprecated": false
},
{
"slug": "visa",
"name": "Visual Anomaly (VisA)",
"description": "Largest Visual Anomaly detection dataset containing objects from 12 classes in 3 domains across 10,821(9,621 normal and 1,200 anomaly) images. Both image and pixel level annotations are provided.",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Not updated",
"tags": "amazon.science,anomaly detection,classification,segmentation,industrial,fewshot",
"license": "https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "visym-cap",
"name": "Consented Activities of People",
"description": "The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the [Visym Collector](https://www.visym.com/collector/) platform.",
"managed_by": "[Visym Labs](https://visym.com)",
"update_frequency": "Not updated",
"tags": "video,computer vision,aws-pds,machine learning,labeled,privacy,activity recognition,activity detection",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0). Every subject in this dataset has consented to their personally identifable information to be shared publicly for the purpose of advancing computer vision research. Non-consented subjects have their faces blurred out.",
"deprecated": false
},
{
"slug": "vitaldb",
"name": "VitalDB",
"description": "VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients.",
"managed_by": "[VitalLab](https://vitallab.ai/)",
"update_frequency": "Not updated",
"tags": "aws-pds,health,biology,medicine,life sciences,signal processing",
"license": "Creative Commons Attribution 4.0 International Public License https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "vt-opendata",
"name": "Vermont Open Geospatial on AWS",
"description": "The State of Vermont has partnered with Amazon's Open Data Initative to make a wide range of geospatial data available in the public domain. Vermont acquires aerial imagery and LiDAR during leaf-off conditions. The imagery typically ranges from 30-centimeter to 15-centimeter in resolution and is available from Vermont's Amazon S3 bucket in a Cloud Optimized GeoTiff (COG) format. LiDAR data has been acquired and is available as USGS Quality Level-1 (QL1) and Level-2 (QL2) compliant datasets in CO",
"managed_by": "[Vermont Center for Geographic Information](https://vcgi.vermont.gov)",
"update_frequency": "Vermont acquires statewide imagery approximately once every other year. Lidar is acquired approximately once every 5-8 years. High-resolution landcover is generated once every other year.",
"tags": "earth observation,aerial imagery,geospatial,lidar,elevation,land cover",
"license": "Public Domain with Attribution\n",
"deprecated": false
},
{
"slug": "wb-light-every-night",
"name": "World Bank - Light Every Night",
"description": "Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized G",
"managed_by": "[World Bank Group](https://www.worldbank.org/en/home)",
"update_frequency": "Quarterly",
"tags": "disaster response,earth observation,satellite imagery,aws-pds,stac,cog",
"license": "[World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "wbg-cckp",
"name": "World Bank Climate Change Knowledge Portal (CCKP)",
"description": "CCKP provides open access to a comprehensive suite of climate and climate change resources derived from the latest generation of climate data archives. Products are based on a consistent and transparent approach with a systematic way of pre-processing the raw observed and model-based projection data to enable inter-comparable use across a broad range of applications. Climate products consist of basic climate variables as well as a large collection (70+) of more specialized, application-orientate",
"managed_by": "[World Bank Group](https://www.worldbank.org/en/home)",
"update_frequency": "Semi-annually",
"tags": "aws-pds,climate,climate model,earth observation,climate projections,CMIP6,netcdf",
"license": "[World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/by/4.0/)",
"deprecated": false
},
{
"slug": "whiffle-wins50",
"name": "Whiffle WINS50 Open Data on AWS",
"description": "Large Eddy Simulation (LES) data of the Winds of the North Sea in 2050 (WINS50) project.",
"managed_by": "[Whiffle](http://www.whiffle.nl/)",
"update_frequency": "No updates planned.",
"tags": "aws-pds,weather,sustainability,atmosphere,electricity,meteorological,model,zarr,turbulence",
"license": "CC BY-SA 4.0",
"deprecated": false
},
{
"slug": "wikisum",
"name": "WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation",
"description": "This dataset provides how-to articles from [wikihow.com](https://www.wikihow.com) and their summaries,\nwritten as a coherent paragraph.\nThe dataset itself is available at [wikisum.zip](https://wikisum.s3.amazonaws.com/WikiSumDataset.zip),\nand contains the article, the summary, the wikihow url, and an official fold (train, val, or test).\nIn addition, human evaluation results are available at\n[wikisum-human-eval.zip](https://wikisum.s3.amazonaws.com/HumanEvaluation.zip).\nIt consists of human evalu",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "amazon.science,natural language processing,machine learning",
"license": "Dataset is published under [CC-NC-SA-3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/).\nHuman evaluation is published under [CC-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n",
"deprecated": false
},
{
"slug": "wis2-global-cache-jma",
"name": "Global Cache of Japan",
"description": "Global real-time Earth system data deemed by the World Meteorological Organisation (WMO) as essential for provision of services for the protection of life and property and for the well-being of all nations. Data is sourced from all WMO Member countries / territories and retained for 24-hours. JMA operate this Global Cache service curating and publishing the dataset on behalf of WMO.",
"managed_by": "[Japan Meteorological Agency](https://www.wis-jma.go.jp/cms/index.html)",
"update_frequency": "Data is updated with new information every 24 hours or less.",
"tags": "aws-pds,atmosphere,climate,climate model,climate projections,climate risk,earth observation,forecast,hydrology,meteorological",
"license": "There are no restrictions on the use of this data. Attribution of original source is requested.",
"deprecated": false
},
{
"slug": "wis2-global-cache",
"name": "WIS2 Global Cache on AWS",
"description": "Global real-time Earth system data deemed by the World Meteorological Organisation (WMO) as essential for provision of services for the protection of life and property and for the well-being of all nations. Data is sourced from all WMO Member countries / territories and retained for 24-hours. Met Office and NOAA operate this Global Cache service curating and publishing the dataset on behalf of WMO.",
"managed_by": "[Met Office](https://www.metoffice.gov.uk/)",
"update_frequency": "New data added as soon as available from origin WIS2 Nodes.",
"tags": "aws-pds,atmosphere,forecast,geoscience,climate,earth observation,hydrology,meteorological,model,oceans",
"license": "There are no restrictions on the use of this data. Attribution of original source is requested.",
"deprecated": false
},
{
"slug": "wise-allsky",
"name": "All-Sky Data | Wide-field Infrared Survey Explorer (WISE)",
"description": "The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The All-Sky Release includes all data taken during the WISE full cryogenic mission phase, 7 January 2010 to 6 August 2010, in the 3.4, 4.6, 12, and 22 micron bands (i.e., W1, W2, W3, W4) that were processed with improved calibrations and reduction algorithms.",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "The All-Sky Data Release has been finalized and will not be updated.",
"tags": "aws-pds,astronomy,imaging,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "wise-allwise",
"name": "AllWISE Data | Wide-field Infrared Survey Explorer (WISE)",
"description": "The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The AllWISE Data Release combines data from all cryogenic and post-cryogenic survey phases and provides a comprehensive view of the mid-infrared sky. The Images Atlas includes 18,240 FITS image sets at 3.4, 4.6, 12 and 22 microns. The Source Catalog contains position, apparent motion, and flux informa",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "The AllWISE Data Release has been finalized and will not be updated. However, the data may be presented in new ways as the products become available.",
"tags": "aws-pds,astronomy,imaging,object detection,parquet,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "wise-cryo-3band",
"name": "3-Band Cryo Data | Wide-field Infrared Survey Explorer (WISE)",
"description": "The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The 3-Band Cryo Data Release contains 3.4, 4.6 and 12 micron (W1, W2, W3) imaging data that were acquired between 6 Aug and 29 Sept 2010 while the detectors were cooled by the inner cryogen tank following the exhaustion of the outer tank.",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "The WISE 3-Band Cryo Data Release has been finalized and will not be updated.",
"tags": "aws-pds,astronomy,imaging,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "wise-neowiser",
"name": "NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE)",
"description": "The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) is a NASA Medium-class Explorer satellite in low-Earth orbit conducting an all-sky astronomical imaging survey over two infrared bands. The NEOWISE Reactivation mission began in 2013 when the original WISE satellite was brought out of hibernation to learn more about the population of near-Earth objects and comets that could pose an impact hazard to the Earth. The data is also used to study a wide range of astrophysical phenomena",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "The NEOWISE-R dataset is updated annually. The data may also be presented in new ways as the products become available.",
"tags": "aws-pds,astronomy,imaging,object detection,parquet,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "wise-postcryo",
"name": "NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE)",
"description": "The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The NEOWISE Post-Cryo Data Release contains 3.4 and 4.6 micron (W1 and W2) imaging data that were acquired between 29 September 2010 and 1 February 2011 following the exhaustion of the inner and outer cryogen tanks.",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "The NEOWISE Post-Cryo Data Release has been finalized and will not be updated.",
"tags": "aws-pds,astronomy,imaging,satellite imagery,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "wise-unwise",
"name": "Unblurred Coadds of the Wide-field Infrared Survey Explorer (unWISE)",
"description": "unWISE is a reprocessing of Wide-field Infrared Survey Explorer (WISE) data which preserves the native angular resolution and is optimized for forced photometry. WISE was a NASA satellite producing all-sky imaging in four infrared bands centered at 3.4, 4.6, 12 and 22 microns (W1, W2, W3, and W4) starting in 2010 until the coolant was exhausted in 2011. It was reactivated in 2013 as NEOWISE and continued imaging in W1 and W2 until 2024.",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "The unWISE dataset is updated periodically to include new data released by NEOWISE. The data may also be presented in new ways as the products become available.",
"tags": "aws-pds,astronomy,object detection,parquet,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
},
{
"slug": "wizard-of-tasks",
"name": "Wizard of Tasks",
"description": "Wizard of Tasks (WoT) is a dataset containing conversations for Conversational Task Assistants (CTAs). A CTA is a conversational agent whose goal is to help humans to perform real-world tasks. A CTA can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. WoT contains about 550 conversations with ~18,000 utterances in two domains, i.e., Cooking and Home Improvement.",
"managed_by": "[Amazon](https://www.amazon.com/)",
"update_frequency": "Not currently being updated",
"tags": "conversation data,dialog,amazon.science,natural language processing,machine learning",
"license": "[cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/)",
"deprecated": false
},
{
"slug": "wpto-pds-us-wave",
"name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset",
"description": "Released to the public as part of the Department of Energy's Open Energy Data Initiative,\nthis is the highest resolution publicly available long-term wave hindcast\ndataset that – when complete – will cover the entire U.S. Exclusive Economic\nZone (EEZ).\n",
"managed_by": "[National Renewable Energy Laboratory](https://www.nrel.gov/)",
"update_frequency": "As needed",
"tags": "aws-pds,earth observation,energy,geospatial,meteorological,water",
"license": "https://github.com/openEDI/documentation/blob/master/US_Wave.md",
"deprecated": false
},
{
"slug": "wrf-alaska-snap",
"name": "Downscaled Climate Data for Alaska (v1.1, August 2023)",
"description": "This dataset contains historical and projected dynamically downscaled climate data for the State of Alaska and surrounding regions at 20km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions. This data was produced using the Weather Research and Forecasting (WRF) model (Version 3.5). We downscaled both ERA-Interim historical reanalysis data (1979-2015) and both historical and projected runs from 2 GCM’s from the Coupled Model Inter-compa",
"managed_by": "Scenarios Network for Alaska + Arctic Planning at the International Arctic Research Center, University of Alaska, Fairbanks",
"update_frequency": "as needed",
"tags": "aws-pds,agriculture,climate,coastal,earth observation,environmental,weather,aws-pds,sustainability",
"license": "https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "wrf-cmip6",
"name": "CMIP6 GCMs downscaled using WRF",
"description": "High-resolution historical and future climate simulations from 1980-2100",
"managed_by": "[UCLA Center for Climate Science](https://dept.atmos.ucla.edu/)",
"update_frequency": "New downscaled results are uploaded as soon as they become available",
"tags": "aws-pds,agriculture,atmosphere,climate,earth observation,environmental,model,oceans,simulations,weather",
"license": "Creative Commons Attribution 4.0 International License",
"deprecated": false
},
{
"slug": "wrf-se-alaska-snap",
"name": "High Resolution Downscaled Climate Data for Southeast Alaska",
"description": "This dataset contains historical and projected dynamically downscaled climate data for the Southeast region of the State of Alaska at 1 and 4km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions. This data was produced using the Weather Research and Forecasting (WRF) model (Version 4.0). We downscaled both Climate Forecast System Reanalysis (CFSR) historical reanalysis data (1980-2019) and both historical and projected runs from two GCM",
"managed_by": "Scenarios Network for Alaska + Arctic Planning at the International Arctic Research Center, University of Alaska, Fairbanks",
"update_frequency": "as needed",
"tags": "aws-pds,agriculture,climate,coastal,earth observation,environmental,weather,aws-pds,sustainability",
"license": "https://creativecommons.org/licenses/by/4.0/",
"deprecated": false
},
{
"slug": "xiph-media",
"name": "Xiph.Org Test Media",
"description": "Uncompressed video used for video compression and video processing research.",
"managed_by": "[Xiph.org](https://xiph.org/)",
"update_frequency": "New videos are added when contributors submit them.",
"tags": "aws-pds,computer vision,image processing,imaging,media,movies,multimedia,video",
"license": "Various. Most are under the CC-BY license. License text accompanies each sequence.",
"deprecated": false
},
{
"slug": "ycb-benchmarks",
"name": "Yale-CMU-Berkeley (YCB) Object and Model Set",
"description": "This project primarily aims to facilitate performance benchmarking in robotics research. The dataset provides mesh models, RGB, RGB-D and point cloud images of over 80 objects. The physical objects are also available via the [YCB benchmarking project](http://www.ycbbenchmarks.com/). The data are collected by two state of the art systems: UC Berkley's scanning rig and the Google scanner. The UC Berkley's scanning rig data provide meshes generated with Poisson reconstruction, meshes generated with",
"managed_by": "Yale University and Berkeley",
"update_frequency": "Yearly",
"tags": "aws-pds,robotics",
"license": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
"deprecated": false
},
{
"slug": "yt8m",
"name": "YouTube 8 Million - Data Lakehouse Ready",
"description": "This both the original .tfrecords and a Parquet representation of the [YouTube 8 Million dataset](https://research.google.com/youtube8m/). YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities. It comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. This dataset also includes the ",
"managed_by": "[Amazon Web Services](https://aws.amazon.com/)",
"update_frequency": "Google Research has not updated the dataset since 2019.",
"tags": "amazon.science,computer vision,machine learning,labeled,parquet,video",
"license": "https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attributions.txt",
"deprecated": false
},
{
"slug": "zinc15",
"name": "ZINC Database",
"description": "3D models for molecular docking screens.",
"managed_by": "[John Irwin](chemistry4biology@gmail.com)",
"update_frequency": "Monthly",
"tags": "aws-pds,life sciences,biology,chemical biology,pharmaceutical,molecular docking,protein",
"license": "ZINC is free as in beer. You may not redistribute without the written permission of [John Irwin](chemistry4biology@gmail.com)",
"deprecated": false
},
{
"slug": "ztf",
"name": "Zwicky Transient Facility (ZTF)",
"description": "The Zwicky Transient Facility (ZTF) is a time-domain astronomy survey that uses the Palomar 48 inch Schmidt telescope and a custom-built wide-field camera to image the night sky in three photometric filters (g, r, and i). It is a fully-automated survey aimed at a systematic exploration of optical transient phenomena. It completes a scan of the observable northern sky approximately every three nights.",
"managed_by": "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech",
"update_frequency": "ZTF datasets may be updated approximately twice per year. The data may also be presented in new ways as the products become available.",
"tags": "astronomy,aws-pds,object detection,parquet,survey",
"license": "https://irsa.ipac.caltech.edu/data_use_terms.html",
"deprecated": false
}
],
"meta": {
"title": "AWS Open Data Registry - Free Download",
"description": "Public datasets hosted on AWS (Registry of Open Data on AWS), including name, description, managed-by organization, update frequency, tags, and license. Contains 894 records. Download in Excel, CSV, PDF formats.",
"seo_title": "AWS Open Data Registry | ListBase",
"seo_description": "Download AWS Open Data Registry — 894 records. Available in Excel, CSV, PDF formats.",
"key_takeaways": [
"Contains 894 records / 8 fields",
"Available for free download in Excel, CSV, and PDF formats",
"Data sourced from: https://github.com/awslabs/open-data-registry"
]
}
}