The Human Sleep Project
bioinformaticsdeep learninglife sciencesmachine learningmedicineneurophysiologyneuroscience
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 signal processing approaches designed to provide better-than-human detection of conventional PSG...
Usage examples
- Automated Sleep Apnea Quantification Based on Respiratory Movement. International Journal of Medical Sciences 2014; 11(8):796-802. PMCID: PMC4057486. by Bianchi MT, Lipoma T, Darling C, Alameddine Y, Westover MB.
- How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics. 2022 Mar 28:10.1007/s12021-022-09572-9. doi: 10.1007/s12021-022-09572-9. Epub ahead of print. PMID: 35347570; PMCID: PMC9515245. by Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, et al.
- Effects of cholinergic neuromodulation on thalamocortical rhythms during NREM sleep: a model study. Frontiers in Computational Neuroscience. 2020 Jan 23;13:100. doi: 10.3389/fncom.2019.00100. eCollection 2019. PMCID: PMC6990259. by Li Q, Song JL, Li SH, Westover MB, Zhang R.
- Age estimation from sleep studies using deep learning predicts life expectancy. NPJ Digit Med. 2022 Jul 22;5(1):103. doi: 10.1038/s41746-022-00630-9. PMCID: PMC9307657. by Brink-Kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE, et al.
- Applications of a Capacitor-Based Respiratory Position Sensing Device: Implications for Radiation Therapy. Austin Journal of Medical Oncology 2014;1(2). PMCID: PMC6956860. by Weng Y, Westover MB, Speier C, Sharp G, Bianchi MT, Westover KD.
Common Crawl
encyclopedicinternetnatural language processingweb archive
A corpus of web crawl data composed of over 300 billion web pages.
Usage examples
- LAION-5B: An open large-scale dataset for training next generation image-text models by Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, et al
- Search the html across 25 billion websites for passive reconnaissance using common crawl by Ryan Elkins
- Coyo-700m: Image-text pair dataset by Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim
- mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, et al
- Learning word vectors for 157 languages by Facebook AI Research
The Cancer Genome Atlas
cancergenomiclife sciencesSTRIDESwhole genome sequencing
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. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantificati...
Usage examples
- Broad Institute FireCloud by The Broad Institute of MIT & Harvard
- Comprehensive Characterization of Cancer Driver Genes and Mutations by Matthew H. Bailey, Collin Tokheim, et al.
- Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines by Kyle Ellrott, Matthew H. Bailey, et al.
- TCGA Cancers Selected for Study by National Cancer Institute
- GDC Legacy Archive by National Cancer Institute
CCRS MODIS albedo over Canada | Albédo MODIS du CCT couvrant le Canada
analysis ready databroadbandcogearth observationsatellite imagery
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). Séries temporelles de produits d’albédo spectral et à large bande générés à des intervalles de 10 jours avec une résolut...
Usage examples
- Detection of North American land cover change between 2005 and 2010 with 250m MODIS data by Colditz, R.R., Pouliot, D., Llamas, R.M., Homer, C., Latifovic, R., Ressl, R.A., Tovar, C.M., Hern�ndez, A.V., Richardson, K.
- Perennial snow and ice variations (2000-2008) in the Arctic circumpolar land area from satellite observations by Fontana F.M.A., Trishchenko A.P., Luo Y., Khlopenkov K.V., Nussbaumer S.U., Wunderle S.
- Surface bidirectional reflectance and albedo properties derived by a land cover based approach from the MODIS observations. by Luo, Y., Trishchenko, Alexander P., Latifovic, R., Li, Z.
- Boreal lichen woodlands: a possible negative feedback to climate change in eastern North America by Bernier, P.Y., Desjardins, R.L., Karimi-Zindashty, Y., Worth, D., Beaudoin, A., Luo, Y., Wang, S.
- Influence of two management practices in the Canadian Prairies on radiative forcing by Liu, J., Worth, D.E., Desjardins, R.L., Haak, D., McConkey, B., Cerkowniak, D.
Foldingathome COVID-19 Datasets
alchemical free energy calculationsbiomolecular modelingcoronavirusCOVID-19foldingathomehealthlife sciencesmolecular dynamicsproteinSARS-CoV-2simulationsstructural biology
Folding@home is a massively distributed computing project that uses biomolecular simulations to investigate the molecular origins of disease and accelerate the discovery of new therapies. Run by the Folding@home Consortium, a worldwide network of research laboratories focusing on a variety of different diseases, Folding@home seeks to address problems in human health on a scale that is infeasible by another other means, sharing the results of these large-scale studies with the research community through peer-reviewed publications and publicly shared datasets. During the COVID-19 epidemic, Folding@h</></></></></>...
Usage examples
- SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) monomer simulations: A 6.4 ms dataset of the SARS-CoV-2 main viral protease (apo, monomer) in search of cryptic pockets by The Bowman lab at Washington University in St. Louis
- SARS-CoV-2 nsp9 simulations: A 9 ms dataset of the SARS-CoV-2 nsp9 protein in search of cryptic pockets by The Bowman lab at Washington University in St. Louis
- SARS-CoV-2 spike RBD with N501Y mutation bound to human ACE2 (953.7 µs) by The Chodera lab at the Memorial Sloan Kettering Cancer Center
- SARS-CoV-2 RBD antibodies that maximize breadth and resistance to escape by Tyler N. Starr, Nadine Czudnochowski, Zhuoming Liu, et al.
- SARS-CoV-2 COVID Moonshot absolute free energy calculations by The Voelz lab at Temple University
Therapeutically Applicable Research to Generate Effective Treatments (TARGET)
cancergenomiclife sciencesSTRIDESwhole genome sequencing
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. TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers.The dataset contains open Clinical Supplement, Biospecimen...
Usage examples
- ISB Cancer Genomics Cloud by Institute for Systems Biology
- Significance of TP53 Mutation in Wilms Tumors with Diffuse Anaplasia: A Report from the Children's Oncology Group by Ooms AH, Gadd S, Gerhard DS, et al.
- MLLT1 YEATS domain mutations in clinically distinctive Favourable Histology Wilms tumours by Perlman EJ, Gadd S, Arold ST, et al.
- The genetic landscape of high-risk neuroblastoma by Pugh TJ, Morozova O, Attiyeh EF, Asgharzadeh S, Wei JS, Auclair D, Carter SL, Cibulskis K, Hanna M, Kiezun A, Kim J, Lawrence MS, Lichenstein L, et al.
- TCF21 hypermethylation in genetically quiescent clear cell sarcoma of the kidney by Gooskens SL, Gadd S, Guidry Auvil JM, et al.
USGS Landsat
agriculturecogdisaster responseearth observationgeospatialnatural resourcesatellite imagerystac
This joint NASA/USGS program provides the longest continuous space-based record of
Earth’s land in existence. Every day, Landsat satellites provide essential information
to help land managers and policy makers make wise decisions about our resources and our environment.
Data is provided for Landsats 1, 2, 3, 4, 5, 7, 8, and 9 (excludes Landsat 6).As of June 28, 2023 (announcement),
the previous single SNS topic arn:aws:sns:us-west-2:673253540267:public-c2-notify was replaced with
three new SNS topics for different types of scenes.
Usage examples
- FME Landsat-8/Sentinel-2 File Selector by Safe Software
- Spectator - tracking Landsat 8, accessing the data and quick preview by Spectator
- Sentinel Hub WMS/WMTS/WCS Service for Landsat by Sinergise
- How to Work with Landsat and Sentinel-2 on AWS with Python by Martin D. Maas
- Integrate imagery from the full Landsat archive into your own apps, maps, and analysis with Landsat image services by Esri
Allen Cell Imaging Collections
biologycell biologycell imagingHomo sapiensimage processinglife sciencesmachine learningmicroscopy
This bucket contains multiple datasets (as Quilt packages) created by the Allen Institute for Cell Science. The types of data included in this bucket are listed below:
- Field of view or cropped images of cells
- Segmentations of structures in the images (e.g., boundaries of cells, DNA, other intracellular structures, etc.)
- Processed versions of the above images and segmentations
- Machine learning predictions and labels of the data listed above
- Models trained on the previously listed data
- Additional supporting non-image data related to the above listed data types (e.g., gene expression data, whole genome sequenc
...
Usage examples
- Download and train label-free models by Greg Johnson
- Allen Cell Structure Segmenter by Jianxu Chen, Liya Ding, Matheus P. Viana, Melissa C. Hendershott, Ruian Yang, Irina A. Mueller, Susanne M. Rafelski
- Allen Cell Feature Explorer by Allen Institute for Cell Science
- Visual Guide to Human Cells by Allen Institute for Cell Science
- Integrating single-cell sequencing and nuclear imaging data by Chengxiang Qiu and William Noble
Gabriella Miller Kids First Pediatric Research Program (Kids First)
cancergeneticgenomicHomo sapienslife sciencespediatricSTRIDESstructural birth defectwhole genome sequencing
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 as osteosarcoma, to more prevalent diagnoses, such as congenital heart defects. In 2018, Kids Fi...
Usage examples
- Kids First DRC Source Code by Kids First DRC
- Genomic Analyses Implicate Noncoding De Novo Variants in Congenital Heart Disease. by Felix Richter, Sarah U Morton, et al.
- Genome-Wide Association Study Identifies a Susceptibility Locus for Comitant Esotropia and Suggests a Parent-of-Origin Effect by Sherin Shaaban, Sarah MacKinnon et al.
- Kids First DRC Portal by Kids First DRC
- Phenotype delineation of ZNF462 related syndrome. by Paul Kruszka, Tommy Hu, et al.
NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17, 18 & 19
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
NEW 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-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distri...
Usage examples
- The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model by Shaowu Bao, Zhan Zhang, Evan Kalina, and Bin Liu
- Billions of Birds Migrate. Where Do They Go? by National Geographic
- Embracing the cloud for climate research by North Carolina State University’s North Carolina Institute for Climate Studies
- GOES Data Explorer by Craig Oliveira
- Solar irradiance forecasting for the solar powered future by Solcast
NASA Prediction of Worldwide Energy Resources (POWER)
agricultureair qualityanalyticsarchivesatmosphereclimateclimate modeldata assimilationdeep learningearth observationenergyenvironmentalforecastgeosciencegeospatialglobalhistoryimagingindustrymachine learningmachine translationmetadatameteorologicalmodelnetcdfopendapradiationsatellite imagerysolarstatisticssustainabilitytime series forecastingwaterweatherzarr
NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.
The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at </>...
Usage examples
- About the Prediction Of Worldwide Energy Resources (POWER) Project ArcGIS StoryMap. by The POWER Project
- Application of a global-to-beam irradiance model to the NASA GEWEX SRB dataset: An extension of the NASA Surface meteorology and Solar Energy datasets by Zhang, T., P. W. Stackhouse, W. S. Chandler, and D. J. Westberg
- A solar azimuth formula that renders circumstantial treatment unnecessary without compromising mathematical rigor: Mathematical setup, application and extension of a formula based on the subsolar point and atan2 function by Zhang, T., P. W. Stackhouse, B. Macpherson, and J. C. Mikovitz
- Association between solar insolation and a history of suicide attempts in bipolar I disorder by Bauer M, et al., Stackhouse PW Jr., et al.
- Application of Satellite Sensor Data and Models for Energy Management by Zell, E., J. Engel-Cox, R. Eckman, and P. Stackhouse
Cell Painting Gallery
bioinformaticsbiologycancercell biologycell imagingcell paintingchemical biologycomputer visioncsvdeep learningfluorescence imaginggenetichigh-throughput imagingimage processingimage-based profilingimaginglife sciencesmachine learningmedicinemicroscopyorganelle
The Cell Painting Gallery is a collection of image datasets created using the Cell Painting assay. The 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. The datasets can be used for diverse applications in basic biology and pharmaceutical research, such as identifying disease-associated phenotypes, understanding disease mechanisms, and predicting a drug’s activity, toxicity, or mechanism of action (Chandrasekaran et al 2020). This collection is maintained </></>...
Usage examples
- Systematic morphological profiling of human gene and allele function via Cell Painting by Rohban MH, Singh S, Wu X, Berthet JB, Bray M-A, Shrestha Y, Varelas X, Boehm JS, & Carpenter AE
- Image-based Profiling Handbook - for processing image-based profiling datasets using CellProfiler and pycytominer by Multiple Authors
- Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects by Singh S, Wu X, Ljosa V, Bray M-A, Piccioni F, Root DE, Doench JG, Boehm JS, & Carpenter AE
- Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling by Wawer MJ, Li K, Gustafsdottir SM, Ljosa V, BodycombeNE, Marton MA, Sokolnicki KL, Bray M-A, Kemp MM, Winchester E, Taylor B, Grant GB, Hon CSK, Duvall JR, Wilson JA, Bittker JA, Dancik V, Narayan R, Subramanian A, Winckler W, Golub TR, Carpenter AE, Shamji AF, Schreiber SL, & Clemons PA
- Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes by Bray M-A, Singh S, Han H, Davis CT, Borgeson B, Hartland C, Kost-Alimova M, Gustafsdottir SM, Gibson CC, & Carpenter AE
Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE)
cogearth observationgeophysicsgeospatialglobalicenetcdfsatellite imagerystaczarr
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. ITS_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, Sentinel-2 optical scenes, and Sentinel-1 SAR scenes. We have processed all land-ice intersecting image pai...
Usage examples
- Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years by Gardner, A. S., G. Moholdt, T. Scambos, M. Fahnstock, S. Ligtenberg, M. van den Broeke, and J. Nilsson
- Detecting seasonal ice dynamics in satellite images by Greene, C.A., A.S. Gardner, and L.C. Andrews
- Using xarray to examine cloud-based glacier surface velocity data by Emma Marshall
- ITS_LIVEpy: A Python client for ITS_LIVE glacier velocity data by Luis A. López and Mark Fahnestock
- Chad Greene's ITS_LIVE Matlab collection by Chad A. Greene
MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree
agricultureair temperatureatmospherebiodiversityclimatecoastaldatacenterecosystemsglobalhydrologyicelandmetadatanetcdfoceansopendapwater
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 ice water, liquid water). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC.ME...
Usage examples
- The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). by Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al.
- Land Surface Precipitation in MERRA-2. by Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka
- How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
- How to Read and Plot NetCDF MERRA-2 Data in Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
- 2015b: MERRA-2: Initial Evaluation of the Climate by Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, S. Pawson, O. Reale, S. D. Schubert, and M. Suarez
Genome Aggregation Database (gnomAD)
bioinformaticsgeneticgenomiclife sciencespopulationpopulation geneticsshort read sequencingwhole genome sequencing
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. The v4.1 data set (GRCh38) spans 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, of diverse ancestries, sequenced sequenced as part of various disease-specific and population genetic studies. The gnomAD Principal Investigators and team can be found Details →
Usage examples
NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
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 EPA’s Community Multiscale Air Quality (CMAQ) model, driven by meteorological fields from NCEP’s operational weather forecast models, ...
Usage examples
- Using VIIRS fire radiative power data to simulate biomass burning emissions, plume rise and smoke transport in a real-time air quality modeling system (Proc. 2017 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS0),Fort Worth, TX, IEEE, 2806–2808) by Ahmadov, R., and Coauthors
- An empirically derived emission algorithm for wind-blown dust (J.Geophys. Res., 115, D16212) by Draxler, R. R., P. Ginoux, and A. F. Stein
- Development of the next-generation air quality prediction system in the UFS framework: Enhancing predictability of wildfire air quality impacts (2024)(Bull. Amer. Meteor. Soc. In review) by Huang, J.P., I. Stajner, R. Montuoro, F. Yang, K. Wang, H.-C. Huang, C.-H. Jeon, B. Curtis, J. McQueen, H. Liu, B. Baker, D. Tong , Y. Tang, P. Campbell, G. Grell, G. Frost, R. Schwantes, S. Wang, S. Kondragunta, F. Li, and Y. Jung
- Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16 (2022, Geosci. Model Dev., 15, 3281–3313) by Campbell, P.C., and Coauthors
- Improving NOAA NAQFC PM2.5 predictions with a bias correction approach (2017, Wea. and Forecasting, 32(2), 407–421) by Huang, J., McQueen, J., Wilczak, J., Djalalova, I., Stajner, I., Shafran, P., Allured, D., Lee, P., Pan, L., Tong, D., Huang, H.-C., DiMego, G., Upadhayay, S., & Delle Monache, L
Protein Data Bank 3D Structural Biology Data
amino acidarchivesbioinformaticsbiomolecular modelingcell biologychemical biologyCOVID-19electron microscopyelectron tomographyenzymelife sciencesmoleculenuclear magnetic resonancepharmaceuticalproteinprotein templateSARS-CoV-2structural biologyx-ray crystallography
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 coordinates, sequences of biological macromolecules, information about any small molecules/ligan...
Usage examples
- Get to Know a Dataset: Protein Data Bank 3D Structural Biology Data by RCSB PDB
- File Download Services by RCSB PDB
- Announcing the worldwide Protein Data Bank by Berman, H., Henrick, K. & Nakamura, H.
- Protein Data Bank: the single global archive for 3D macromolecular structure data by wwPDB consortium
- PDB 101 by RCSB PDB
RACECAR Dataset
autonomous racingautonomous vehiclescomputer visionGNSSimage processinglidarlocalizationobject detectionobject trackingperceptionradarrobotics
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, obstacle avoidance, pit entry and exit at different speeds. The data is organized and released in bot...
Usage examples
- RACECAR Tutorials - ROS2 Visualization by Amar Kulkarni, Utkarsh Chirimar
- rosbag2nuscenes conversion library by John Chrosniak, Emory Ducote, John Link, Madhur Behl
- RACECAR--The Dataset for High-Speed Autonomous Racing by Amar Kulkarni, John Chrosniak, Emory Ducote, Florian Sauerbeck, Andrew Saba, Utkarsh Chirimar, John Link, Marcello Cellina, and Madhur Behl
- RACECAR Tutorials - nuScenes by John Chrosniak
- RACECAR Tutorials - ROS2 Localization by Amar Kulkarni
SPARC: Datasets bridging the body and the brain
bioinformaticselectrophysiologylife sciencesmicroscopyneurophysiologyneuroscience
The SPARC Datasets comprise a collection of scientific data that is focused on bridging the body and the brain. The datasets focus on neural connectivity, organ innervation and detailed anatomical mapping of the peripheral nervous system. SPARC datasets distinguish themselves from other data resources through its multi-modal approach to scientific data and integrates molecular, imaging, timeseries and other datatypes associated with the interaction between the peripheral nervous system and organs. SPARC data provides a unique integrated effort to develop next generation mapping of anatomical ...
Usage examples
- OSPARC by Esra Neufeld
- The SPARC Portal by Peter Hunter, Maryann Martone, Esra Neufeld, Joost Wagenaar
- The SPARC DRC: Building a Resource for the Autonomic Nervous System Community by Osanlouy M, Bandrowski A, de Bono B, Brooks D, Cassara A, Christie R, Ebrahimi N, Gillespie T, Grethe J, Guercio L, Heal M, Lin M, Kuster N, Martone M, Neufeld E, Nickerson D, Soltani E, Tappan S, Wagenaar J, Zhuang K, Hunter P
- The Pennsieve Data Management Platform by Joost Wagenaar
- Using sparc.client for data movement in SPARC by The SPARC Data and Resource Center
Sofar Spotter Archive
climateenvironmentalmeteorologicaloceansoceanssustainabilityweather
This dataset includes archival hourly data from the [Sofar Spotter buoy global network] (https://weather.sofarocean.com/) from 2019 to March 2022.
Usage examples
- Performance Characteristics of “Spotter,” a Newly Developed Real-Time Wave Measurement Buoy (2019) by K. Raghukumar, G. Chang, F. Spada, C. Jones, T. Janssen, A. Gans
- Analyzing Spotter data with CloudDrift by Milan Curcic
- Performance Statistics of a Real-Time Pacific Ocean Weather Sensor Network (2021) by I. Houghton, P. Smit, D. Clark, C. Dunning, A. Fisher, N. Nidzieko, P. Chamberlain, T. Janssen
- Exploring Bulk Variables from the Spotter Archive by Isabel A. Houghton
- Exploring Wave Spectra Variables from the Spotter Archive by Isabel A. Houghton
The Human Connectome Project
biologyimaginglife sciencesneurobiologyneuroimagingneuroscience
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.
Usage examples
- Exploring the Human Connectom by The Human Connectome Project
- The Human Connectome Project: A retrospective by Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JL, Burgess GC, Curtiss SW, et al.
- The Human Connectome Workbench by The Human Connectome Project
- The WU-Minn Human Connectome Project: an overview. by Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil, K, and the WU-Minn HCP Consortium.
- The minimal preprocessing pipelines for the Human Connectome Project by Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, et al.
ASKAP Radio Telescope
archivesastronomy
ASKAP is the CSIRO’s newest radio telescope. It is situated at the Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory on Wajarri Yamaji Country in the Murchison region of Western Australia, about 800 km north of Perth. ASKAP consists of 36 12m dishes, spread-out as far as 6km apart. It uses a new technology called Phased Array Feeds (PAFs), which allows it to see more of the sky at once. This novel technology allows ASKAP to achieve extremely high survey speed, making it one of the best instruments in the world for mapping the sky at radio wavelengths. Initial dataset...
Usage examples
- Rapid Askap Continuum Survey (RACS) Home Page by CSIRO, ATNF
- ASKAP Publication List by various, list maintained by CSIRO, ATNF
- CSIRO ASKAP Science Data Archive User Guide by CSIRO, ATNF
- ASKAP System Description paper by Hotan, A. et al.
BUSCO Datasets
assemblybacteriabioinformaticsgenomiclife sciencesmetagenomicsopen source softwareproteinvirus
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.
Usage examples
- BUSCO Update - Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. by Mosè Manni, Matthew R Berkeley, Mathieu Seppey, Felipe A Simão, Evgeny M Zdobnov
- BUSCO - from QC to gene prediction and phylogenomics by Matthew Berkeley
- BUSCO - assessing genomic data quality and beyond. by Mosè Manni, Matthew R. Berkeley, Mathieu Seppey, Evgeny M. Zdobnov
- OrthoDB and BUSCO update - annotation of orthologs with wider sampling of genomes. by Fredrik Tegenfeldt, Dmitry Kuznetsov, Mosè Manni, Matthew Berkeley, Evgeny M Zdobnov, Evgenia V Kriventseva
Basic Local Alignment Sequences Tool (BLAST) Databases
bioinformaticsbiologygeneticgenomichealthlife sciencesproteinreference indexSTRIDEStranscriptomics
A centralized repository of pre-formatted BLAST databases created by the National Center for Biotechnology Information (NCBI).
Usage examples
- BLAST+ Docker by NCBI BLAST
- BLAST+: Architecture and Applications by Christiam Camacho 1 , George Coulouris, Vahram Avagyan, Ning Ma, Jason Papadopoulos, Kevin Bealer, Thomas L Madden
- Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs by S F Altschul, T L Madden, A A Schäffer, J Zhang, Z Zhang, W Miller, D J Lipman
- BLAST on the Cloud with NCBI’s ElasticBLAST by Sixing Huang
Demand-Side Grid (dsgrid) Toolkit
data assimilationelectricityenergyenergy modelingindustrialmeteorologicalsolartransportation
Projects that use the dsgrid toolkit assemble bottom-up descriptions of electricity demand and related data that are highly resolved geographically, temporally, and sectorally. Typically modelers describe multiple scenarios of future energy use at hourly resolution, suitable for inclusion in long-term power system planning models, i.e., capacity expansion and production cost models.
Usage examples
- Demand-Side Grid Toolkit by Elaine Hale
- Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States by Arthur Yip, Christopher Hoehne, Paige Jadun, Catherine Ledna, Elaine Hale, and Matteo Muratori
- dsgrid Documentation by Elaine Hale
- GitHub Repository for Working with the dsgrid Projects by Elaine Hale
- The Demand-side Grid (dsgrid) Model Documentation by Elaine Hale, Henry Horsey, Brandon Johnson, et al.
Epigenomes of the Human Pangenome Reference Consortium (HPRC) Release 2
bioinformaticsbiologyepigenomicsgeneticgenomiclife sciences
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 supporting genome selection, data visualization, and bulk download capabilities. The portal integrates ...
Usage examples
- "Get To Know A Dataset: HPRC Epigenome" by HPRC Epigenome Browser
- "Modbed track: Visualization of modified bases in single-molecule sequencing" by Daofeng Li, Xiaoyu Zhuo, Jessica K. Harrison, Shane Liu, Ting Wang
- WashU Epigenome Browser update 2025 by Chanrung Seng, Shane Liu, Wenjin Zhang, Xiaoyu Zhuo, Daofeng Li, Ting Wang
- A draft human pangenome reference by Liao, WW., Asri, M., Ebler, J. et al.
FoMo - A Multi-Season Dataset for Robot Navigation in Forêt Montmorency
autonomous vehiclesbenchmarkcomputer visionenvironmentalextreme weathergeospatialGNSSIMUlidarlocalizationmappingmeteorologicalperceptionradarRINEXroboticssignal processing
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. Designed to support research on robust robot autonomy under adverse conditions, FoMo includes repeated traversals of six trajectories of varying complexity for ...
Usage examples
GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC
atmosphereclimatecoastaldatacenterglobalhydrologylandmetadatanetcdfopendap
Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The 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 observations from an international constellation of satellites using advanced techniques. IMERG can be used for globa...
Usage examples
- How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
- Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
- Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
- Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC
climatecoastaldatacenterglobalhydrologyicelandmetadatanetcdfopendap
Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The 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 observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applic...
Usage examples
- Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
- Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
- How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
- Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC
atmosphereclimatecoastaldatacenterglobalhydrologylandmetadatanetcdfopendap
Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The 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 observations from an international constellation of satellites using advanced techniques. IMERG can be used for ...
Usage examples
- How to Access GES DISC Data Using Python by James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha.
- Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System by Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao
- Kalman Filter Based CMORPH by Joyce, R. J., P. Xie, and J. E. Janowiak
- Calculation of Gridded Precipitation Data for the Global Land-Surface Using In-Situ Gauge Observations by Rudolf, B., and U. Schneider
High Resolution Canopy Height Maps by WRI and Meta
aerial imageryagricultureclimatecogearth observationgeospatialimage processingland covermachine learningsatellite imagery
Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.
Usage examples
- Global Canopy Height on Earth Engine by Meta and WRI
- Every tree counts: Large-scale mapping of canopy height at the resolution of individual trees by Jamie Tolan, Camille Couprie, and Tracy Johns
- Using Artificial Intelligence to Map the Earth’s Forests by Jamie Tolan, Camille Couprie, John Brandt, Justine Spore, Tobias Tiecke, Tracy Johns and Patrick Nease
- Sub-meter resolution canopy height maps using self-supervised learning and a vision transformer trained on Aerial and GEDI Lidar by Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Icechunk ERA5
atmosphereclimatedata assimilationenergyforecastgeosciencegeospatiallandmachine learningmeteorologicalmodelweatherzarr
An analysis-ready, cloud-optimized (ARCO) edition of ERA5, the fifth-generation global reanalysis from the Copernicus Climate Change Service (C3S) at ECMWF. Provided as a single Icechunk + Zarr v3 store on AWS S3, this version enables efficient, scalable access to global reanalysis data for weather, climate, and ML/AI applications. Managed by Earthmover.
ERA5 provides hourly estimates of atmospheric, land-surface, and ocean variables from 1940 to 2025 on a ~31 km (0.25°) global latitude-longitude grid. This Earthmover edition delivers 35 single-level (surface) variables, 8 pressure-level variables on 13 isobaric levels (50–1000 hPa), and a curated 500 hPa geopotential slice, all with CF-1.7 compliant metadata.
The store is organized into three top-level groups (single, pressure, and 500hPa). Each group i</>...
Usage examples
International Skin Imaging Collaboration (ISIC) Archive
biologycancerclassificationcomputational pathologydicomgrand-challenge.orghealthHomo sapiensimaginglife sciencesmachine learningmedical image computingmedical imagingmedicinemicroscopysegmentation
A public-access archive of skin lesion images, supporting teaching, research, and the development and evaluation of diagnostic algorithms.
Usage examples
- ISIC Archive Data Dictionary by International Skin Imaging Collaboration (ISIC)
- ISIC Archive Gallery by International Skin Imaging Collaboration (ISIC)
- International Skin Imaging Collaboration - Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling by Scope A, Liopyris K, Weber J, Barnhill R, Braun R, Curiel-Lewandrowski C, et al
- isic-cli - The official command line tool for interacting with the ISIC Archive by International Skin Imaging Collaboration (ISIC)
- A patient-centric dataset of images and metadata for identifying melanomas using clinical context by Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, et al
Mouse Brain Anatomy: MouseLight Imagery
biologyfluorescence imagingimage processingimaginglife sciencesmicroscopyneurobiologyneuroimagingneuroscience
This data set, made available by Janelia's MouseLight project, consists of images and neuron annotations of the Mus musculus brain, stored in formats suitable for viewing and annotation using the HortaCloud cloud-based annotation system.
Usage examples
- MouseLight NeuronBrowser by Tiago A. Ferreira, Jayaram Chandrashekar
- Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain by Johan Winnubst, Erhan Bas, Tiago A. Ferreira, Zhuhao Wu, Michael N. Economo, Patrick Edson, Ben J. Arthur, Christopher Bruns, Konrad Rokicki, David Schauder, Donald J. Olbris, Sean D. Murphy, David G. Ackerman, Cameron Arshadi, Perry Baldwin, Regina Blake, Ahmad Elsayed, Mashtura Hasan, Daniel Ramirez, Bruno Dos Santos, Monet Weldon, Amina Zafar, Joshua T. Dudman, Charles R. Gerfen, Adam W. Hantman, Wyatt Korff, Scott M. Sternson, Nelson Spruston, Karel Svoboda, Jayaram Chandrashekar
- MouseLight Project Website by Tiago A. Ferreira, Jayaram Chandrashekar
- HortaCloud by David Schauder, Donald J. Olbris, Jody Clements, Cristian Goina, Robert R. Svirskas, Konrad Rokicki
NOAA High-Resolution Rapid Refresh (HRRR) Model
agricultureclimatedisaster responseenvironmentalweather
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 continuing to publish ZARR versions of HRRR data. For information about data in the s3://hrrrzarr/ please contact &#x</>...
Usage examples
- The HRRR Zarr Archive Managed by MesoWest by Taylor Gowan
- Using the U.S. Climate Reference Network to Identify Biases in Near- and Sub-Surface Meteorological Fields in the High-Resolution Rapid Refresh (HRRR) Weather Prediction Model by Temple R. Lee, Ronald D. Leeper, Tim Wilson, Howard Diamond, Tilden P. Meyers, and David D. Turner
- HRRR-B Python package: download and read HRRR grib2 files by Brian Blaylock
- Using Cloud Computing to Analyze Model Output Archived in Zarr Format by Taylor A. Gowan, John D. Horel, Alexander A. Jacques, and Adair Kovac
NOAA's Coastal Ocean Reanalysis (CORA) Dataset: 1979-2022
agricultureagricultureclimatedisaster responseenvironmentaloceanstransportationweather
- Timeseries - 1979 to 2022
- Size - Approx. 44.6 TB
- Domain - Lat 5.8 to 45.8 ; Long -98.0 to -53.8
</></></></>...
Usage examples
- Using Python to Access Coastal Ocean Reanalysis (CORA) Data by NOAA's Center for Operational Oceanographic Products and Services
- NOAA Technical Report NOS CO-OPS 108: NOAA’s Coastal Ocean Reanalysis: Gulf of Mexico, Atlantic, and Caribbean (January 2025) by Keeney, Analise; Dusek, Gregory; Callahan, John; Ratcliff, John; Jima, Tigist; Brooks, William; Marcy, Doug; Blanton, Brian; Tilson, Jeffrey; Asher, Taylor G.; Leuttich, Richard A.; Widlansky, Matthew J.; Rose, Linta; Morse, Cheryl; Haddad, Jana; & Waring, Blake
- Assessment of water levels from 43 years of NOAA’s Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coasts by Rose, Linta; Widlansky, Matthew J.; Feng, Xue; Thompson, Thompson; Asher, Taylor G.; Dusek, Gregory; Blanton, Blanton; Luettich, Richard A. Jr.; Callahan, John; Brooks, William; Keeney, Analise; Haddad, Jana; Sweet, William; Genz, Ayesha; Hovenga, Paige; Marra, John & Tilson, Jeffrey
- Coastal Ocean Reanalysis Use cases by NOAA's Center for Operational Oceanographic Products and Services
NREL National Solar Radiation Database
earth observationenergygeospatialmeteorologicalsolar
Released to the public as part of the Department of Energy's Open Energy Data Initiative, the National Solar Radiation Database (NSRDB) is a serially complete collection of hourly and half-hourly values of the three most common measurements of solar radiation – global horizontal, direct normal, and diffuse horizontal irradiance — and meteorological data. These data have been collected at a sufficient number of locations and temporal and spatial scales to accurately represent regional solar radiation climates.
Usage examples
- Physics-guided machine learning for improved accuracy of the National Solar Radiation Database by Grant Buster, Mike Bannister, Aron Habte, Dylan Hettinger, Galen Maclaurin, Michael Rossol, Manajit Sengupta, Yu Xie
- HSDS Examples by Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer, Michael Rossol
- The National Solar Radiation Data Base (NSRDB) by Manajit Sengupta, Yu Xe, Anthony Lopez, Aron Habte, Galen Maclaurin, James Shelby
- NSRDB Viewer by Manajit Sengupta, Yu Xe, Anthony Lopez, Aron Habte, Galen Maclaurin, James Shelby, Paul Edwards
OVRO LWA Solar Observation
astronomyhdf5machine learningsolarspace weatherweather
This dataset contains long-term low-frequency solar radio imaging spectroscopy observations from the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA). The data are produced from solar radio interferometric observations and distributed as HDF5 spectral image products and FITS all-day spectrogram products, with metadata describing the observation time, frequency coverage, imaging products, and other relevant parameters. The dataset provides dynamic, imaging-based measurements of solar radio emission at low frequencies, which are closely connected to solar coronal activity, solar r...
Usage examples
- OVRO LWA Solar Data Portal by CSTR, New Jersey Institute of Technology
- OVRO-LWA Solar Data Products by CSTR, New Jersey Institute of Technology
- Reading and Plotting OVRO-LWA Spectral Image HDF Data by Peijin Zhang, New Jersey Institute of Technology
- Get To Know a Dataset: OVRO LWA Solar Observation by Peijin Zhang, New Jersey Institute of Technology
OpenAlex dataset
graphjsonmetadatascholarly communication
An open, comprehensive index of scolarly papers, citations, authors, institutions, and journals.
Usage examples
- Getting citation data from OpenAlex by DOI (Jupyter notebook) by Jens Peter Anderson
- OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts by Jason Priem, Heather Piwowar, Richard Orr
- Data Analysis and Knowledge Graph with OpenAlex dataset by Gabriel Bella Martini
- Download snapshot by OurResearch
OpenCell on AWS
Biohubbiologycell biologycell imagingcomputer visionfluorescence imagingimaginglife sciencesmachine learningmicroscopy
The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins using high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome. This dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library.These images can be interpreted both individually, to determine the localization of particular proteins of interest, and in aggregate, by training machine learning models to classify or quantify subcellular localization patterns.
Usage examples
- OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization by Nathan H. Cho, Keith C. Cheveralls, Andreas-David Brunner, Kibeom Kim, André C. Michaelis, Preethi Raghavan, et al.
- cytoself (an unsupervised ML model to quantify localization patterns) by Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
- Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization by Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
- OpenCell web portal by OpenCell team
OpenFold3 Training Data
life sciencesmsaopen source softwareopenfoldproteinprotein foldingprotein template
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 -
- PDB - 245k structures and alignments from the RCSB Protein Data Bank - https://www.rcsb.org/
- Long monomer distillation set - ~13 million long (sequence length >= 200 amino acids) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.
- Short monomer distillation set - 400k short (sequence length < 200 amino acid) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.
- Disordered </></></>
...
Usage examples
- OpenFold3-preview2 Technical Report by The OpenFold3 Team
- Deploying OpenFold3 with NVIDIA NIMs on Brev & AWS EC2 by Glòria Macià
- OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization by Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Xia, Qinghui; Gerecke, William; O'Donnell, Timothy J, et al
- Looking at an OpenFold3 MSA in a Browser-Based Notebook on Scigantic by Scigantic
Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1
climateearth observationenvironmentalgeospatialglobaloceans
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 coverage. For reference and citation see: www.nature.com/articles/s41597-019-0236-x.
Usage examples
- Adjusting for desert-dust-related biases in a climate data record of sea surface temperature (2020). by Merchant, C.J. and Embury, O.
- Working with surftemp-sst data - Tutorial 1 - Getting started by Niall McCarroll
- Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019). by Merchant, C.J., Embury, O., Bulgin, C.E., Block, T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R. and Donlon, C.
- Working with surftemp-sst data - Tutorial 2 - Analysing Marine Heatwaves by Niall McCarroll
Synthea synthetic patient generator data in OMOP Common Data Model
bioinformaticshealthlife sciencesnatural language processingus
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 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 cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and gov</>...
Usage examples
- Predict patient health outcomes using OHDSI and machine learning on AWS by James Wiggins
- OHDSIonAWS by James Wiggins
- Create data science environments on AWS for health analysis using OHDSI by James Wiggins
- Map clinical notes to the OMOP Common Data Model and healthcare ontologies using Amazon Comprehend Medical by James Wiggins
Umbra Synthetic Aperture Radar (SAR) Open Data
earth observationgeospatialimage processingsatellite imagerystacsynthetic aperture radar
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 resolution, and multi-looked spotlight mode. The ODP also features an assorted collection of over ...
Usage examples
- umbra-py example notebooks (search, load, change detection, chips, SICD) by umbra-py contributors
- umbra-py — Python toolkit to search, preview, load, and convert Umbra open SAR data by umbra-py contributors
- umbra-py documentation and interactive catalog showcase by umbra-py contributors
- umbra-py quickstart (search, preview, and load a scene) by umbra-py contributors
Version 2 High Resolution Canopy Height Maps by WRI and Meta
aerial imageryagricultureclimatecogearth observationgeospatialimage processingland covermachine learningsatellite imagery
Version 2 Global and regional Canopy Height Maps (CHMv2). Created using machine learning models on high-resolution worldwide Vantor satellite imagery.
Usage examples
- Get To Know A Dataset - CHMv2 by Meta
- CHMv2: Improvements in Global Canopy Height Mapping using DINOv3 by John Brandt, Seungeun Yi, Jamie Tolan, Xinyuan Li, Peter Potapov,Jessica Ertel, Justine Spore, Huy V. Vo, Michael Ramamonjisoa, Patrick Labatut, Piotr Bojanowski, and Camille Couprie
- DINOv3 by Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, Piotr Bojanowski
- Global Canopy Height on Earth Engine by Meta and WRI
Yale-CMU-Berkeley (YCB) Object and Model Set
robotics
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. 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 volumetric range image integration, textured versions of both meshes, Kinbody files for using the meshes with OpenRAVE, 600 </>...
Usage examples
- Pre-touch sensing for sequential manipulation by Boling Yang, Patrick Lancaster, Joshua R. Smith
- The Closure Signature: A Functional Approach to Model Underactuated Compliant Robotic Hands by Maria Pozzi, Gionata Salvietti, João Bimbo, Monica Malvezzi, Domenico Prattichizzo
- Label Fusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes by Pat Marion, Peter R. Florence, Lucas Manuelli, Russ Tedrake
- Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set by Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, Aaron M Dollar
iSDAsoil
agricultureanalyticsbiodiversityconservationdeep learningfood securitygeospatialmachine learningsatellite imagery
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 mapping process.
Usage examples
- iSDAsoil homepage - view soil property maps online by iSDA
- iSDAsoil Python tutorial by Matt Miller
- iSDAsoil liming demo app on Observable by Jamie Collinson
- African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning by Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, Luka Glušica, Achim Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean-Martial Johnson, Jordan Chamberlin, Francis B. T. Silatsa, Martin Yemefack, John Wendt, Robert A. MacMillan, Ichsani Wheeler & Jonathan Crouch
AG-LOAM Dataset
agriculturelidarlocalizationmappingrobotics
AG-LOAM dataset has been released to facilitate the evaluation of LiDAR-based odometry algorithms in agricultural environments.
- It was collected by a wheeled mobile robot at the Agricultural Experimental Station of the University of California, Riverside, during Winter 2022 and Winter 2023.
- It provides LiDAR point cloud data captured using a Velodyne VLP-16 sensor, along with ground-truth trajectories obtained from an RTK-GPS system.
- It consists of 18 sequences collected over three phases, covering diverse planting environments, terrain conditions, path patterns, and robot motion profiles.
- It
...
Usage examples
- Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms by Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis
- Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms by Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis
- Source code of the LiDAR-only odometry and mapping system by Hanzhe Teng et al.
AI3 Protein-Ligand Binding Affinity Dataset
healthlife sciencesmachine learningmolecular dynamicspharmaceuticalproteinsimulations
The rapid advancement of computing technologies, particularly artificial intelligence (AI), has revolutionized various domains, including drug discovery. Curated datasets are crucial for developing reliable, generalizable, and accurate models for practical applications. Generating experimental data on a large scale is an expensive and arduous process. In domains such as medical diagnostics where real-life data is hard to obtain, synthetic data has been shown to be extremely valuable. We, teams from IIIT Hyderabad, Intel, AWS, and Insilico Medicine, have performed physics-based calculations (mo...
Usage examples
- AI3: Protein-Ligand Binding Affinity Dataset by Deva Priyakumar Lab
- PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications by U. Deva Priyakumar
- PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications by U. Deva Priyakumar
Active Visual Semantics
brain imagescomputer visionelectrophysiologylife sciencesmachine learningmagnetic resonance imagingnatural language processingneuroimagingneuroscience
The Active Visual Semantics (AVS) Dataset is a multimodal neuroimaging dataset combining magnetoencephalography (MEG), eye-tracking, and structural MRI, recorded from 5 participants (sub-01-sub-05) as they actively explored 4,080 natural scenes (subsampled from the Natural Scenes Dataset, NSD) across 10 recording sessions each, yielding more than 200,000 fixation epochs in total. Unlike neuroimaging datasets that rely on passive viewing with enforced central fixation, AVS captures brain activity during active, self-directed scene exploration, including natural saccades and fixations. A semanti...
Usage examples
- Get To Know A Dataset: Active Visual Semantics (AVS) by Philip Sulewski
- pyAVS Colab Quickstart by Philip Sulewski
- Fixation duration on natural scenes is explained by memory encoding not processing demand by Philip Sulewski et al.
AdaptiveFlow Ligand Libraries
bioinformaticslife sciencesmedicinepharmaceuticalstructural biology
AdaptiveFlow Versions of Ligand Libraries in Ready-To-Dock Format
Usage examples
- AdaptiveFlow tutorial by Christoph Gorgulla
- AdaptiveFlow for Virtual Screening (AFVS) Module on GitHub by Christoph Gorgulla
- VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules by Christoph Gorgulla, AkshatKumar Nigam, Matt Koop, Süleyman Selim Çınaroğlu, Christopher Secker, Mohammad Haddadnia, Abhishek Kumar, Yehor Malets, Alexander Hasson, Roni Levin-Konigsberg, Dmitry Radchenko, Aditya Kumar, Minko Gehev, Pierre-Yves Aquilanti, Henry Gabb, Amr Alhossary, Gerhard Wagner, Al, Yurii S. Moroz, Konstantin Fackeldey, Haribabu Arthanari
Beat Acute Myeloid Leukemia (AML) 1.0
cancergeneticgenomicHomo sapienslife sciencesSTRIDES
Beat AML 1.0 is a collaborative research program involving 11 academic medical centers who worked collectively to better understand drugs and drug combinations that should be prioritized for further development within clinical and/or molecular subsets of acute myeloid leukemia (AML) patients. Beat AML 1.0 provides the largest-to-date dataset on primary acute myeloid leukemia samples offering genomic, clinical, and drug response.This dataset contains open Clinical Supplement and RNA-Seq Gene Expression Quantification data.This dataset also contains controlled Whole Exome Sequencing (WXS) and R...
Usage examples
- Genomic Data Commons by National Cancer Institute
- Functional Genomic Landscape of Acute Myeloid Leukemia by Jeffrey W. Tyner, Cristina E. Tognon, Dan Bottomly et al.
- Clinical resistance to crenolanib in acute myeloid leukemia due to diverse molecular mechanisms by Zhang H, Savage S, Schultz AR, Bottomly D, White L, Segerdell E, et al.
BraiDyn-BC: Cued lever-pull task dataset
calcium imagingimaginglife sciencesMus musculusneurosciencevideo
The BraiDyn-BC (Brain Dynamics underlying emergence of Behavioral Change) Database offers an extensive, multimodal dataset that links wide-field calcium imaging of the mouse neocortex to comprehensive behavioral measurements during a behavioral task. As one of the contents in this database, we newly provide a dataset that includes 15 sessions spanning two weeks of motor skill learning, in which 25 mice were trained to pull a lever to obtain water rewards. Simultaneous high-speed videography captures body, facial, and eye movements, and environmental parameters are monitored. The dataset also ...
Usage examples
- A set of libraries used for generating the dataset by Keisuke Sehara, Ryo Aoki, Shoya Sugimoto
- Detailed usage tutorials on Google Colab by Keisuke Sehara
- A multimodal dataset linking wide-field calcium imaging to behavior changes in mice during an operant lever-pull task by Kondo M, Sehara K, Harukuni R, Aoki R, Sugimoto S, Tanaka YR, Matsuzaki M, Nakae K
COBRA
cancercomputational pathologycomputer visiondeep learninghistopathologylife sciences
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 different types of skin dysplasia. The third group has 1,000 labelled risky cancer biopsies, includin...
Usage examples
Canopy Tree Height Map for the Amazon Forest (mean height composite 2020-2024) by CTrees.org
cogconservationdeep learningearth observationenvironmentalgeospatialimage processingland coverlidarsatellite imagery
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
Usage examples
- How to download the CTrees Amazon Canopy Height Map by Fabien H Wagner
- Is this the largest tree in the Amazon? A Q&A with CTrees scientist Fabien Wagner by Rachel Kovinsky
- High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model by Fabien H Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi CM Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean PHB Ometto, Lorena Alves, Cynthia Creze, Stephanie P George-Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G Santos, Sarah R Worden, Martin Brandt, Philippe Ciais, Stephen C Hagen, Sassan Saatchi
Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS
cell biologycomputer visionelectron microscopyimaginglife sciencesorganelle
High resolution images of subcellular structures.
Usage examples
- Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells. by David P. Hoffman, Gleb Shtengel, C. Shan Xu, Kirby R. Campbell, Melanie Freeman, Lei Wang, Daniel E. Milkie, H. Amalia Pasolli, Nirmala Iyer, John A. Bogovic, Daniel R. Stabley, Abbas Shirinifard, Song Pang, David Peale, Kathy Schaefer, Wim Pomp, Chi-Lun Chang, Jennifer Lippincott-Schwartz, Tom Kirchhausen1, David J. Solecki, Eric Betzig, Harald F. Hess
- Enhanced FIB-SEM systems for large-volume 3D imaging by C. Shan Xu, Kenneth J. Hayworth, Zhiyuan Lu, Patricia Grob, Ahmed M. Hassan, José G. García-Cerdán, Krishna K. Niyogi, Eva Nogales, Richard J. Weinberg, Harald F. Hess.
- Whole-cell organelle segmentation in volume electron microscopy by Lisa Heinrich, Davis Bennett, David Ackerman, Woohyun Park, Jon Bogovic, Nils Eckstein, et al.
CitrusFarm Dataset
agriculturecomputer visionIMUlidarlife scienceslocalizationmappingrobotics
CitrusFarm is a multimodal agricultural robotics dataset that provides both multispectral images and navigational sensor data for localization, mapping and crop monitoring tasks.
- It was collected by a wheeled mobile robot in the Agricultural Experimental Station at the University of California Riverside in the summer of 2023.
- 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 GPS-RTK data.
- It comprises seven sequences collected from three citrus tree fields, featuring various tree spe
...
Usage examples
- Python scripts used in the data collection and post-processing by Hanzhe Teng et al.
- Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms by Hanzhe Teng, Yipeng Wang, Xiaoao Song and Konstantinos Karydis
- Python script to download this dataset by Hanzhe Teng et al.
Clinical Trial Sequencing Project - Diffuse Large B-Cell Lymphoma
cancergenomiclife sciencesSTRIDEStranscriptomicswhole genome sequencing
The goal of the project is to identify recurrent genetic alterations (mutations, deletions, amplifications, rearrangements) and/or gene expression signatures. National Cancer Institute (NCI) utilized whole genome sequencing and/or whole exome sequencing in conjunction with transcriptome sequencing. The samples were processed and submitted for genomic characterization using pipelines and procedures established within The Cancer Genome Analysis (TCGA) project.
Usage examples
- Genomic Data Commons by National Cancer Institute
- Genetics and Pathogenesis of Diffuse Large B Cell Lymphoma by Roland Schmitz, Ph.D., George W. Wright, Ph.D., Da Wei Huang, M.D., Calvin A. Johnson, Ph.D., James D. Phelan, Ph.D., James Q. Wang, Ph.D., Sandrine Roulland, Ph.D., Monica Kasbekar, Ph.D., Ryan M. Young, Ph.D., Arthur L. Shaffer, Ph.D., Daniel J. Hodson, M.D., Ph.D., Wenming Xiao, Ph.D., et al.
- A multiprotein supercomplex controlling oncogenic signalling in lymphoma by Phelan JD, Young RM, Webster DE, Roulland S, Wright GW, Kasbekar M, Shaffer AL 3rd, Ceribelli M, Wang JQ, Schmitz R, Nakagawa M, Bachy E, Huang DW, Ji Y, Chen L, Yang Y, Zhao H, Yu X, Xu W, Palisoc MM, Valadez RR, Davies-Hill T, Wilson WH, Chan WC, Jaffe ES, Gascoyne RD, Campo E, Rosenwald A, Ott G, Delabie J, Rimsza LM, Rodriguez FJ, Estephan F, Holdhoff M, Kruhlak MJ, Hewitt SM, Thomas CJ, Pittaluga S, Oellerich T, Staudt LM
Finnish Meteorological Institute Weather Radar Data
agricultureearth observationmeteorologicalweather
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 from singe radars are also provided in HDF5 format with ODIM 2.3 conventions. Radar data becomes avail...
Usage examples
- Processing HDF5 data with python by Roope Tervo
- Handling data with QGIS by Markus Peura
- Processing GeoTIFF data with python by Roope Tervo
Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)
cancergenomiclife sciences
The Foundation Medicine Adult Cancer Clinical Dataset (FM-AD) is a study conducted by Foundation Medicine Inc (FMI). Genomic profiling data for approximately 18,000 adult patients with a diverse array of cancers was generated using FoundationeOne, FMI's commercially available, comprehensive genomic profiling assay. This dataset contains open Clinical and Biospecimen data.
Usage examples
- Genomic Data Commons by National Cancer Institute
- High-Throughput Genomic Profiling of Adult Solid Tumors Reveals Novel Insights into Cancer Pathogenesis by Ryan J. Hartmaier, Lee A. Albacker, Juliann Chmielecki, Mark Bailey, Jie He, Michael E. Goldberg, Shakti Ramkissoon, James Suh, Julia A. Elvin, Samuel Chiacchia, Garrett M. Frampton, Jeffrey S. Ross, Vincent Miller, Philip J. Stephens and Doron Lipson
- Targeted next-generation sequencing of advanced prostate cancer identifies potential therapeutic targets and disease heterogeneity. by Beltran H, Yelensky R, Frampton GM, Park K, Downing SR, MacDonald TY, Jarosz M, Lipson D, Tagawa ST, Nanus DM, Stephens PJ, Mosquera JM, Cronin MT, Rubin MA
Golden Retriever Lifetime Study: Whole genome genotyping of Golden Retrievers on Axiom HD Arrays
genomegenotypinggolden retriever lifetime studylife sciencesmorris animal foundation
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 conditions with high mortality in younger age. This dataset contains the data for ~1.1 million genetic marke...
Usage examples
- The Golden Retriever Lifetime Study: establishing an observational cohort study with translational relevance for human health by Michael K. Guy, Rodney L. Page, Wayne A. Jensen, Patricia N. Olson, J. David Haworth, Erin E. Searfoss, and Diane E. Brown
- GRLS GWAS Tutorial by Tamer Mansour
- Cohort profile: The Golden Retriever Lifetime Study (GRLS) by Julia Labadie, Brenna Swafford, Mara DePena, Kathy Tietje, Rodney Page, Janet Patterson-Kane
I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography Database
bioinformaticsdeep learninglife sciencesmachine learningmedicineneurophysiologyneuroscience
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 was determined using the Cerebral Performance Category scale.
Usage examples
- The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database by Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, et al.
- I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography Database by Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, et al.
- WFDB Software Package by Moody, G., Pollard, T., & Moody, B.
Imaging MIT Licensed data and models
biodiversityBiohubbioinformaticsbiologybiomolecular modelingbrain imagescell biologycell imagingimaginglife sciencesmachine learningmicroscopymodelproteinzarr
This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at Biohub and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.
Usage examples
- CELL-Diff: Unified diffusion modeling for protein sequences and microscopy images by Zheng Dihan, Bo Huang
- Quickstart Tutorial for CELL-Diff by Biohub
- Documentation for SubCell by Biohub
- Quickstart Tutorial for SubCell by Biohub
- SubCell: Vision foundation models for microscopy capture single-cell biology by Ankit Gupta, Zoe Wefers, Konstantin Kahnert, Jan N Hansen, William D. Leineweber, Anthony Cesnik, Dan Lu, Ulrika Axelsson, Frederic Ballllosera Navarro, Theofanis Karaletsos, Emma Lundberg
Indiana Statewide Elevation Catalog
agricultureearth observationgeospatialimaginglidarmappingnatural resourcesustainability
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 tile's lower left coordinate, facilitating accurate data management and retrieval. The AWS ...
Usage examples
- ArcGIS Online Indiana Lidar Viewer by Indiana Geographic Information Office (IGIO)
- Recording of 2025 - 2028 Indiana Imagery and Elevation Program Presentation by Indiana Geographic Information Office (IGIO)
- IGIO Elevation Opendata S3 Browser by Indiana Geographic Information Office (IGIO)
Indiana Statewide Leaf-on Digital Aerial Imagery Catalog
aerial imageryagriculturecogearth observationgeospatialimagingmappingnatural resourcesustainability
The State of Indiana Geographic Information Office (GIO) provides a statewide, enhanced NAIP, high-resolution 6-in (15 cm) leaf-on digital orthoimagery catalog. Captured during the summer of 2025, this imagery features an enhanced horizontal accuracy. Primary deliverables are hosted as standard GeoTIFF (.tif), JPEG, and highly performant Cloud Optimized GeoTIFF (COG) files to optimize cloud-native streaming and raster rendering.Managed by the Indiana Geographic Information Office (IGIO) and integrated directly into the State’s open data imagery repository on AWS, this public-domain dataset is ...
Usage examples
- Indiana Imagery Resouse Pages by Indiana Geographic Information Office (IGIO)
- ArcGIS Online Indiana Orthoimagery Viewer by Indiana Geographic Information Office (IGIO)
- IGIO Imagery Opendata S3 Browser by Indiana Geographic Information Office (IGIO)
MIMIC-III (‘Medical Information Mart for Intensive Care’)
bioinformaticshealthlife sciencesnatural language processingus
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. The MIMIC-I...
Usage examples
- Building predictive disease models using Amazon SageMaker with Amazon HealthLake normalized data by Ujjwal Ratan, Nihir Chadderwala, and Parminder Bhatia
- Perform biomedical informatics without a database using MIMIC-III data and Amazon Athena by James Wiggins, Alistair Johnson
- MIMIC-code GitHub repository by Alistair Johnson
Medical Segmentation Decathlon
computed tomographyhealthimaginglife sciencesmagnetic resonance imagingmedicineniftisegmentation
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 also missing a fully open source and comprehensive benchmark for general purpose algorithmic validati...
Usage examples
- MONAI: Getting Started by MONAI Development Team
- A large annotated medical image dataset for the development and evaluation of segmentation algorithms by Simpson A. L., Antonelli M., Bakas S., Bilello M., Farahana K., van Ginneken B., et al
- Pytorch-Integrated MSD Data Loader by MONAI Development Team
Met Office Global Ocean model on a 2-year rolling archive
forecastgeosciencegeospatialglobalmarinemodelnetcdfocean sea surface heightoceansweather
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 hourly fields for sea surface height, sea surface temperature and surface currents. The Met Office Glo...
Usage examples
Met Office Global Wave model on a 2-year rolling archive
forecastgeosciencegeospatialglobalmarinemodelnetcdfocean sea surface heightoceansweather
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 for wave characteristics defining height, period and direction of waves within a given sea-state. The ...
Usage examples
Met Office NWS Wave model on a 2-year rolling archive
forecastgeosciencegeospatialmarinemodelnetcdfocean sea surface heightoceansweather
Northwest European continental shelf regional wave model predicting sea-state and various sea and swell wave characteristics for waters surrounding the UK.The Met Office runs global and regional 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, whilst regional configurations are run in order to improve accuracy closer to the coast. The Met...
Usage examples
Met Office UK Deterministic (UKV)2km on a 2-year rolling archive
air temperatureatmosphereforecastgeosciencegeospatialmodelnear-surface air temperaturenear-surface relative humiditynetcdfweather
A high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.
Based on the Met Office UKV model, which is a deterministic, numerical weather prediction model for the UK and Ireland. It is a UK configuration of the Unified Model, which is the Met Office’s flagship Numerical Weather Prediction model.
The archive contains data from the past two years. The data is typically available approximately 3 to 6 hours after...
Usage examples
Multiview Extended Video with Activities (MEVA)
computer visionurbanusvideo
The Multiview Extended Video with Activities (MEVA) dataset consists video data of human activity, both scripted and unscripted, collected with roughly 100 actors over several weeks. The data was collected with 29 cameras with overlapping and non-overlapping fields of view. The current release consists of about 328 hours (516GB, 4259 clips) of video data, as well as 4.6 hours (26GB) of UAV data. Other data includes GPS tracks of actors, camera models, and a site map. We have also released annotations for roughly 184 hours of data. Further updates are planned.
Usage examples
- ActEV: Activities in Extended Video by National Institute of Standards and Technology (NIST)
- TinyAction Challenge: Recognizing Real-world Low-resolution Activities in Videos by Praveen Tirupattur, Aayush J Rana, Tushar Sangam, Shruti Vyas, Yogesh S Rawat, Mubarak Shah
- MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection by Kellie Corona, Katie Osterdahl, Roderic Collins, Anthony Hoogs
NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)
air temperatureclimateclimate modelclimate projectionsCMIP6cogearth observationenvironmentalglobalmodelNASA Center for Climate Simulation (NCCS)near-surface relative humiditynear-surface specific humiditynetcdfprecipitation
The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed...
Usage examples
- NASA Global Daily Downscaled Projections, CMIP6 by Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T. and Nemani, R.
- NEX-GDDP-CMIP6 Dashboard by NASA
- NASA and ASDI announce no-cost access to important climate dataset on the AWS Cloud by Dr. Manil Maskey and Ana Pinheiro Privette
NASA High Energy Astrophysics Mission Data
archivesastronomydatacenterimagingsatellite imageryx-ray
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.
This is a relatively modest total data volume but contains significant complexity and heterogeneity among the different missions. Data provided here are stored in the Flexible Imag...
Usage examples
NASA Space Biology Open Science Data Repository (OSDR)
bioinformaticsbiologyGeneLabgenomicimaginglife sciencesspace biology
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.
OSDR introduces access to data generated from spaceflight and space relevant experiments that explore ...
Usage examples
- NASA GeneLab: interfaces for the exploration of space omics data by Daniel C Berrios, Jonathan Galazka, Kirill Grigorev, Samrawit Gebre, Sylvain V Costes
- GeneLab: Omics database for spaceflight experiments by Shayoni Ray, Samrawit Gebre, Homer Fogle, Daniel C Berrios, Peter B Tran, Jonathan M Galazka, Sylvain V Costes
- Advancing the Integration of Biosciences Data Sharing to Further Enable Space Exploration by Ryan T. Scott, Kirill Grigorev, Graham Mackintosh, Samrawit G. Gebre, Christopher E. Mason, Martha E. Del Alto, Sylvain V. Costes
NOAA - hourly position, current, and sea surface temperature from drifters
climateenvironmentalmeteorologicaloceanssustainabilityweather
This dataset includes hourly sea surface temperature and current data collected by satellite-tracked surface drifting buoys ("drifters") of the NOAA Global Drifter Program. The Drifter Data Assembly Center (DAC) at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) has applied quality control procedures and processing to edit these observational data and obtain estimates at regular hourly intervals. The data include positions (latitude and longitude), sea surface temperatures (total, diurnal, and non-diurnal components) and velocities (eastward, northward) with accompanying uncertainty estimates. Metadata include identification numbe</></>...
Usage examples
- A global surface drifter dataset at hourly resolution (2016) by Elipot, S., R. Lumpkin, R. C. Perez, J. M. Lilly, J. J. Early, and A. M. Sykulski
- Working with GDP hourly data using python and xarray, a CloudDrift notebook by Shane Elipot
- A Dataset of Hourly Sea Surface Temperature From Drifting Buoys (2022) by Elipot, S., A. Sykulski, R. Lumpkin, L. Centurioni, and M. Pazos
NOAA IOOS MARACOOS Regional Ocean Modeling System (ROMS) "Doppio" Data Assimilative Reanalysis
coastalenvironmentalmarineoceans
This dataset, identified as Doppio Analysis Version 3 Release 3 (DopAnV3R3-ini2007) initialized January 2007, comprises outputs from a Regional Ocean Modeling System (ROMS) data assimilative reanalysis of ocean circulation in the Mid-Atlantic Bight and Gulf of Maine for 2007-2024.
A multi-year reanalysis (2007-2024) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf has been computed using the Regional Ocean Modeling System (ROMS) with four-dimensional variational (4D-Var) data assimilation (DA) of observations from satellites, land-based ocean surface current measuring radar, and all available in situ observations from the MARACOOS ...
Usage examples
- ROMS model outputs in AWS can be readily visualized and analyzed in Python using the xroms tools by XROMS
- Outputs from a Regional Ocean Modeling System (ROMS) data assimilative reanalysis (version DopAnV3R3-ini2007) of ocean circulation in the Mid-Atlantic Bight and Gulf of Maine for 2007-2021 by Wilkin, J. and Levin, J.
- A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models by Wilkin, J., Levin, J., Moore, A., Arango, H., López, A., and Hunter, E.
NOAA Multi-Radar/Multi-Sensor System (MRMS)
agricultureclimatemeteorologicalweather
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.
MRMS 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 models. Numerous two-dimensional multiple-sensor products offer assistance for hail, wind, tornado, quantitative precipitation estimations, co...
Usage examples
- Collection of Jupyter Notebooks using Python for working with MRMS Data by Project Pythia Community
- Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities by Travis M. Smith, Valliappa Lakshmanan, Gregory J. Stumpf, Kiel L. Ortega, Kurt Hondl, Karen Cooper, Kristin M. Calhoun, Darrel M. Kingfield, Kevin L. Manross, Robert Toomey, Jeff Brogden
- Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities by Jian Zhang, Kenneth Howard, Carrie Langston, Brian Kaney, Youcun Qi, Lin Tang, Heather Grams, Yadong Wang, Stephen Cocks, Steven Martinaitis, Ami Arthur, Karen Cooper, Jeff Brogden, David Kitzmiller
NOAA North American Multi-Model Ensemble (NMME)
climatemeteorologicalweather
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.
The 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 tailored regional prediction and decision support needs of a large community. The multi-model ens...
Usage examples
- NMME Articles by Multiple publications listed in the provided link
- NMME : Meeting Future Needs Workshop Report by Christine, Bassett, Jessie, Carman, D.K. Kang, and Mark Olsen
- Real-time Usage by NWS Climate Prediction Center NMME
National Climate Database (NCDB)
climate projectionsCMIP5CMIP6earth observationenergygeospatialmeteorologicalsolar
The National Climate Database (NCDB) seeks to be the definitive source of climate data for energy applications. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB seeks to maintain the inherent relationship between the various parameters that are needed to model solar, wind, hydrology and load and provide data for multiple important climate scenarios.