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180 results for “Downscaling”
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014): Daily fields (Part 2)
<p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <strong><em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em></strong>, submitted to Earth System Science Data (ESSD).</p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain, the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs.</p> <p>Here (Daily fields - Part 2), we provide the daily snowfall time series resulting from the downscaling of the 7 first members of CESM2, using ERA-Interim and RACMO2.3p5.5 for training.</p>
Downscaled and bias corrected 10 km water withdrawal of China, 1981-2010
<p>This dataset contains</p> <p>(1) MonthlyDomIndcon_CN_0.1deg_1981-2010.nc -- the 0.1° gridded monthly domestic & industrial water consumption of China</p> <p>(2) MonthlyDomInduse_CN_0.1deg_1981-2010.nc -- the 0.1° gridded monthly domestic & industrial water withdrawal of China</p> <p>(3) MonthlyIrrigation_CN_0.1deg_1981-2010.nc -- the 0.1° gridded monthly irrigation water withdrawal of China</p> <p>These data are first spatially downscaled from Huang et al. (2018) (https://doi.org/10.5281/zenodo.1209296) based on population density, GDP and irrigation area, and then bias corrected against provincial-level statistics published by local water agencies. Refer to Huang et al. (2018) and Dong et al. (2022) for more details.</p> <p>Dong, N., Wei, J., Yang, M., Yan, D., Yang, C., Gao, H., et al. (2022). Model estimates of China's terrestrial water storage variation due to reservoir operation. Water Resources Research, 58, e2021WR031787. <a href="https://doi.org/10.1029/2021WR031787">https://doi.org/10.1029/2021WR031787</a></p>
CRISI-ADAPT II: free downscaled climate projection layers
<p>CRISI-ADAPT II project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision making of the related stakeholders. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas.</p> <p>For further details, find here a brief of the methodology followed:</p> <p> </p> <p><strong> Methodology</strong></p> <p>Information provided by 10 models belonging to CMIP6 have been included. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: </p> <p><em>Table. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives. </em></p> <table> <tbody> <tr> <td> <p><strong>CMPI6 MODELS</strong> </p> </td> <td> <p><strong>Resolution</strong> </p> </td> <td> <p><strong>Responsible Centre</strong> </p> </td> <td> <p><strong>References</strong> </p> </td> </tr> <tr> <td> <p><strong>BCC-CSM2-MR</strong> </p> </td> <td> <p>1,125º x 1,121º </p> </td> <td> <p>Beijing Climate Center (BCC), China Meteorological Administration, China. </p> </td> <td> <p>Wu, T. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>CanESM5</strong> </p> </td> <td> <p>2,812º x 2,790º </p> </td> <td> <p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </p> </td> <td> <p>Swart, N.C. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>CNRM-ESM2-1</strong> </p> </td> <td> <p>1,406º x 1,401º </p> </td> <td> <p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </p> </td> <td> <p>Seferian, R. (2019) </p> </td> </tr> <tr> <td> <p><strong>EC-EARTH3</strong> </p> </td> <td> <p>0,703º x 0,702º </p> </td> <td> <p>EC-EARTH Consortium </p> </td> <td> <p>EC-Earth Consortium. (2019) </p> </td> </tr> <tr> <td> <p><strong>GFDL-ESM4</strong> </p> </td> <td> <p>1,250º x 1,000º </p> </td> <td> <p>National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. </p> </td> <td> <p>Krasting, J.P. et al. (2018) </p> </td> </tr> <tr> <td> <p><strong>MPI-ESM1-2-HR</strong> </p> </td> <td> <p>0,938º x 0,935º </p> </td> <td> <p>Max-Planck Institute for Meteorology (MPI-M), Germany. </p> </td> <td> <p>Von Storch, J. et al. (2017) </p> </td> </tr> <tr> <td> <p><strong>MRI-ESM2-0</strong> </p> </td> <td> <p>1,125º x 1,121º </p> </td> <td> <p>Meteorological Research Institute (MRI), Japan. </p> </td> <td> <p>Yukimoto, S. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>UKESM1-0-LL</strong> </p> </td> <td> <p>1,875º x 1,250º </p> </td> <td> <p>Uk Met Office, Hadley Centre, United Kingdom </p> </td> <td> <p>Good, P. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>NorESM2-MM</strong> </p> </td> <td> <p>1,250º x 0,942º </p> </td> <td> <p>Norwegian Climate Centre (NCC), Norway. </p> </td> <td> <p>Bentsen, M. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>ACCESS-ESM1-5</strong> </p> </td> <td> <p>1,875º x 1,250º </p> </td> <td> <p>Australian Community Climate and Earth System Simulator (ACCESS), Australia </p> </td> <td> <p>Ziehn, T. et al. (2019)</p> </td> </tr> </tbody> </table> <p>Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo <em>et al.</em> 2016). Other variables such as fog and wave height requires to be obtained from model post-processing. </p> <p>In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps: </p> <ol> <li> <p>Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors. </p> </li> <li>For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050. </li> <li> <p>The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively. </p> </li> <li>For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model. </li> <li> <p>Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method. </p> </li> <li>Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind. </li> <li> <p>Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013). </p> </li> </ol>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
Downscaled 1 km CESM2 data used in CESM2 Greenland SMB evaluation paper (HIST-EC)
<p>Monthly data from CESM2 simulation HIST-EC over the period 1960-1999, downscaled to the 1 km RACMO grid using elevation class output.</p> <p>Variable 'QICE' represents SMB as calculated internally by CLM.</p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain". Training predictors from the ERA5 reanalysis, projecting predictors from CORDEX EUR11, and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rds" format, to be read from R, except for CORDEX files in NetCDF.</p>
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
Downscaled Base, Sector and Fuel based PM2.5 from Stretched Grid Simulations using GEOS-Chem High Performance over South Asia.
<p>The <a href="https://zenodo.org/api/files/aed6de20-c761-4560-9cb8-b8f9e4e7b038/Gridded_Base_Sector_Fuel_PM25_South_Asia.mat">Gridded_Base_Sector_Fuel_PM25_South_Asia.mat</a> file that contains LAT_South_Asia, LON_South_Asia, PM25_base, PM25_sectors,PM25_fuels</p> <p>This is the order of the gridded sectors*:</p> <p>PM25_sectors(:,:,1) = AFCID;</p> <p>PM25_sectors(:,:,2) = OPEN_FIRES;</p> <p>PM25_sectors(:,:,3) = INDUSTRY;</p> <p>PM25_sectors(:,:,4) = POWER GENERATION;</p> <p>PM25_sectors(:,:,5) = RESIDENTIAL COMBUSTION;</p> <p>PM25_sectors(:,:,6) = TRANSPORT;</p> <p>PM25_sectors(:,:,7) = WASTE ;</p> <p>PM25_sectors(:,:,8) = AGRICULTURE ;</p> <p>PM25_sectors(:,:,9) = OTHER ;</p> <p>PM25_fuels(:,:,1) = BIOFUEL;</p> <p>PM25_fuels(:,:,2) = REMAINING_SOURCES;</p> <p>PM25_fuels(:,:,3) = COAL;</p> <p>PM25_fuels(:,:,4) = OIL_AND_GAS;</p> <p>PM25_fuels(:,:,5) = DUST_AND_FIRES;</p> <p>* The sectors here have been customized to prioritize particular sectors by adding lesser contributing sectors together. Please contact the author for more information on this. </p> <p>The NetCDFs contain the scaled ratios between CEDS 2019 and 2017 for Emissions for 31 species that were used in the simulations.</p>
A downscaled 0.05-Degree Monthly Solar-Induced Chlorophyll Fluorescence Product derived using AVHRR Data in East Asia (1995-2003)
<p>Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. Here, we generated a new monthly SIF product, DSIF_EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. The random forest kriging (RFK) approach was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. The unit of SIF is mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>. The selected variables were daily maximum values of air temperature (T<sub>air</sub>), skin temperature (T<sub>skin</sub>), fraction of absorbed photosynthetically active radiation (fPAR), near-infrared reflectance of vegetation (NIRv), downward shortwave radiation (SR<sub>down</sub>), and precipitation. To verify the reliability of DSIF_EA0.05 and the advantages over original GOME SIF, this dataset has been validated with the original GOME SIF, ground gross primary productivity (GPP) data from eight flux sites, and two other SIF products at 1-degree and 0.05-degree resolutions from SCIAMACHY SIF and downscaled SCIAMACHY SIF datasets.</p>
High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)
<p>This dataset comprises high-resolution land use data downscaled from LUH2 scenarios for Belgium at both 10 m and 100 m resolutions. These datasets were generated based on research conducted by Rashidi et al. in 2023 and published in the Land Journal. We employed the GLOBIO land allocation routine to downscale fractional land use data, originally at a 0.25° resolution (approximately 25 km), into discrete land use maps at 10 m and 100 m resolutions. This process utilized three distinct reference land cover maps: ESA WorldCover at 10 m resolution, ESA WorldCover upscaled to 100 m resolution, and CORINE land cover at 100 m resolution.</p> <p>During the downsizing process, we considered three SSP-RCP scenarios to model land use trends for both the present and the year 2050 on a national scale in Belgium. Key components of the model included regional land use demand, an assessment of grid cells' suitability for various land use types, and a reference land cover map. It's important to note that the classification system used in the reference maps differs from that of LUH2. To ensure comparability for land use simulations, we conducted a reclassification process following the methodologies outlined by Pérez-Hoyos et al. (2012), Dong et al. (2018), and Liao et al. (2020). This reclassification consolidated land use classes, except for water, into seven general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) secondary vegetation, 6) undefined, and 7) natural.</p> <p>The raw data consists of three folders corresponding to the three reference maps, each containing four TIFF files (.tif), one for each scenario type.</p>
Dynamically downscaled 15-minute U.S. West Coast precipitation (Part 2/3)
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High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)
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Dynamically downscaled 15-minute U.S. West Coast precipitation (Part 1/3)
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Dynamically downscaled 15-minute U.S. West Coast precipitation (Part 3/3)
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Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model
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Downscaled climate grids of California at 90m for a variety of bioclimatic variables from 1971-2000, derived from historical climate grids
This dataset is comprised of 90 Geotiff images of selected bioclimatic variables for the state of California (extended past state lines to river basin boundaries). Originally created to model plant species distributions in California (Franklin et al. 2013. Modeling plant species distributions under future climates: how fine-scale do climate projections need to be? Global Change Biology 19: 473-483).
sif-downscaling-essd-supporting-data
<p>This repository contains supporting data associated to the following paper:</p> <p><strong>Duveiller G., Filipponi F., Walther S., Köhler P., Frankenberg C., Guanter L., and Cescatti A.</strong> 2020. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. <em>Earth System Science Data</em>. <a href="https://doi.org/10.5194/essd-2019-121">https://doi.org/10.5194/essd-2019-121</a></p> <p>The actual dataset that the paper describes, and which is considered the final result of the study, it available in its dedicated repository in the JRC Data Catalogue at <a href="https://data.jrc.ec.europa.eu/dataset/21935ffc-b797-4bee-94da-8fec85b3f9e1">https://data.jrc.ec.europa.eu/dataset/21935ffc-b797-4bee-94da-8fec85b3f9e1</a> (DOI:10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1). Instead, the present repository contains associated data that falls in two categories:</p> <ol> <li>Post-processed data necessary to reproduce the figures in the paper using the code in the Github repository: <a href="https://github.com/GregDuveiller/sif-downscaling-essd/">https://github.com/GregDuveiller/sif-downscaling-essd/</a> (DOI:10.5281/zenodo.3753521).</li> <li>Pre-processed data that is normally availiable from other sources but which had to be especially tailored for the study. They are composed of OCO-2 and TROPOMI observations that are gridded and composited to the similar format to MODIS data used in the paper. See the paper for more details.</li> </ol> <p> </p>
Daily downscaled PRISM data (tmin and tmax) from monthly data for 1961-1990
<p>This data set is associated with the study "DDRP: real-time phenology and climatic suitability modeling of invasive insects" by <a href="https://doi.org/10.1371/journal.pone.0244005">Barker et al. (2020)</a>. We temporally downscaled monthly PRISM estimates for 1961‒1990 because DDRP requires daily data and PRISM daily temperature data for years prior to 1980 are not available. For each month of a given year, a bilinear interpolation method was used to assign each day an average temperature value that was iteratively smoothed and then adjusted so that the monthly averages were correct. Code for this analysis was written partially in Perl and partially in Octave, and is available at https://github.com/bbarker505/dailynorms. We temporally downscaled monthly PRISM estimates for 1961‒1990 because DDRP requires daily data and PRISM daily temperature data for years prior to 1980 are not available. For each month of a given year, a bilinear interpolation method was used to assign each day an average temperature value that was iteratively smoothed and then adjusted so that the monthly averages were correct. Code for this analysis was written partially in Perl and partially in Octave, and is available at https://github.com/bbarker505/dailynorms and https://doi.org/10.5281/zenodo.3601671.</p>
Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.