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310 results for “Era5”
Daily 500 hPa geopotential height from ERA5 for the period 1940-2022 for the North Atlantic domain - 2 degree resolution
<p>Data downloaded from the Copernicus Data Store from the `reanalysis-era5-pressure-levels` dataset. Data converted from hourly to daily and regridded using xarray. </p>
SMOS Level 2 v700 vs ERA5 v20190613
QA4SM validation: SMOS Level 2 v700 vs ERA5 v20190613. URL: https://qa4sm.eu/ui/validation-result/5a7439de-9dee-4b81-87e6-0599de0ffbb2. Produced on QA4SM (https://qa4sm.eu)
MAR-ERA5 reanalysis of the Arctic land ice surface mass balance between 1950 and 2020
<p>This archive provides monthly outputs of the surface mass balance variables over the Arctic land ice, as modeled by MAR forced by ERA5.</p><p>These outputs were produced as part of the publication "Maure, D., Kittel,C., Lambin, C., Delhasse, A. and Fettweis, X.: "Spatially heterogeneous effect of climate warming<br>on the Arctic land ice", The Cryosphere, accepted. (2023). The data comes from the 6km domains presented in Fig.1 of the study, reinterpolated to a single Pan-Arctic 6km grid.</p><p> </p><p>Contact: Damien Maure </p><p>damien.maure@uliege.be</p><p> </p><p>The MAR code is available at https://gitlab.com/Mar-Group/MARv3. The version used to generate this dataset is tagged as v3.11.5.</p><p>About the dataset:<br>It contains one file per year</p><p>MAR_arctic_ERA5_v1_<strong>*year*</strong>.nc</p><p>MAR311</p><p>SMB: surface mass balance<br>SF: snowfall<br>RF: rainfall<br>RU: runoff<br>ME: melt<br>SU: sublimation - deposition (positive values indicates mass losses through sublimation)<br>(units: mm we day)</p><p> </p><p> </p>
Combined ERA5 1981-2023 Dataset
Open the record for dataset details and reuse information.
2010_2024_ERA5_Precipitation_Rainfall_FourierProcessed_1k_WG
<p>This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:</p> <p>ERA5: Total Precipitation </p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the whole world<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2024. </p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2022. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algorithms to 1km resolution, then downloaded. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <h4>Process:</h4> <p>Image values were extracted from ERA5 ( Total precipitation) at 1 km resolution imagery from 2010 to 2024. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and errors measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Sea pixels were masked with a VIIRS land/sea layer. </p> <p> </p> <p>This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p>Projection + EPSG code:</p> <p>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Extent -180.0000000000000000,-90.0000000000000000 : 179.9999999999998295,89.9999999999999147</p> <h4>File names:</h4> <p><br>The wg at the start of each file name indicates that the image covers the whole world in the E4warning and is in geographic projection. 04 refers to the year timeline of 2010-2024.<br><br>The next two characters identify the channel:<br>20 - Monthly Total Precipitation<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
Atmospheric river variability over the last millennium driven by annular modes: Part 3 (AR tags for ERA5)
Open the record for dataset details and reuse information.
Precipitation identifiers for meteorological features combining global GPM-IMERG retrievals and ERA5 reanalysis
Open the record for dataset details and reuse information.
ERA5-Land monthly averaged dataset for Galaxy Panoply training
<p><strong>ERA5-Land monthly averaged data January 2019</strong></p> <p>Dataset has been retrieved on the Copernicus Climate data Store (<a href="https://cds.climate.copernicus.eu/#!/home">https://cds.climate.copernicus.eu/#!/home</a>) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".</p> <p>See <a href="https://training.galaxyproject.org/">https://training.galaxyproject.org/</a> (topic: climate) for more information.</p> <p><strong>Product type: </strong>Monthly averaged reanalysis</p> <p><strong>Variable:</strong></p> <p>10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth</p> <p><strong>Year:</strong></p> <p>2019</p> <p><strong>Month:</strong></p> <p>January</p> <p><strong>Time:</strong></p> <p>00:00</p> <p><strong>Format:</strong></p> <p>NetCDF (experimental)</p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1999–2015)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p>
Data and Code for Tropical Cyclone Seed Disturbances in ERA5
<p>Data used in the paper titled "Tropical Cyclone Seed Disturbances in ERA5" by Moon et al.</p> <p>The dataset consists of tracking and track files, with tracking conducted using TempestExtremes and NCL.</p> <p> </p> <p>The track dataset is available in two versions, depending on the reference dataset used for matching (ERA5TC and IBTrACS)</p> <p> </p>
C3S ERA5 for Netherlands - daily data for air temperature (daily avg, min, max) 1950-2022
<p>Data downloaded for the target area [53.9, 3, 50.7, 7.4] and then aggregated to daily. </p>
Data used in the study titled "Significant Tornado Environments In Canada Using ERA5-Derived Convective Parameters"
<p>These data include: (1) Observation-derived convective parameters at four Canadian sounding stations based on 1990-2020 data, and ERA5-derived convective parameters at grid points nearest to the same four Canadian sounding stations on 1990-2020 data - file called "ERA5 and Observation convective parameters.zip". (2) ERA5 vertical profile data, derived convective parameters, and skew-t images for the 166 Canadian F/EF2+ tornado events - file called "ERA5 profiles-parameters-skewt 166 cases.zip"</p>
PARASO ERA5 forcings
<p>External forcings from ERA5, used for forcing the PARASO configuration. Due to their size, only three months are provided here (from 01-JAN-2000 to 31-MAR-2000), for testing purposes. The data should be uncompressed with the uncpr_paraso.sh script.</p> <p><strong>Model description: </strong>Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.</p> <p><strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a></p> <p><strong>Other input data: </strong>Pelletier, Charles, Klein, François, Zipf, Lars, Vanden Broucke, Sam, Haubner, Konstanze, & Helsen, Samuel. (2021). Input data for PARASO, a circum-Antarctic fully-coupled 5-component model (v1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5588468">10.5281/zenodo.5588468</a></p> <p> </p> <p>The ERA5 data (Hersbach, 2018) was downloaded on 01-SEP-2019 from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Reference</strong>: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1979 to present, <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, downloaded from the Copernicus Climate Change Service (C3S); Climate Data Store (CDS) on 01-SEP-2019, 2018.</p>
Sample dataset of different format from ERA5
<p>Sample dataset of different format from ERA5</p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1967–1981)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1951–1966)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Total precipitation:<br> Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br> The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate proportion of ERA5-Land / aggregated CHELSA<br> 3. interpolate proportion with a Gaussian filter to 30 arc seconds<br> 4. multiply the interpolated proportions with CHELSA<br> Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.<br> Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.</p> <p>File naming:<br> Average of daily sum: <code>era5_land_prectot_avg_weekly_YYYY_MM_DD.tif</code><br> Sum of daily sum: <code>era5_land_prectot_sum_weekly_YYYY_MM_DD.tif</code></p> <p>The date in the file name determines the start day of the week (Saturday).</p> <p>Pixel values:<br> mm * 10<br> Example: Value 218 = 21.8 mm</p> <p>Coordinate reference system:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 1km</p> <p>Temporal resolution:<br> weekly</p> <p>Period:<br> 01/01/2016 - 12/31/2020</p> <p>Lineage:<br> Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original ERA5-Land dataset license:<br> <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Other resources:<br> <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a></p> <p>Format: GeoTIFF</p> <p>Representation type: Grid</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact:<br> mundialis GmbH & Co. KG, info@mundialis.de</p>
era5 monthly solar radiation, rain, 100m wind
<p>era5 monthly solar radiation, rain, 100m wind</p>
ERA5-Land selected indicators daily aggregates for Africa, 2005
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2005.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations with ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator (Pre-review)
<p>Due to its remote location and extreme weather conditions, atmospheric measurements are rare in Antarctica. Partially due to this lack of observational constraints, large biases in the representation of clouds have been identified over Southern hemisphere high latitudes in various grenerations of the Coupled Model Intercomparison Project (CMIP) models, numerical weather prediction models and reanalyses. It has been shown in previous studies that ground-based remote sensing measurements of cloud across the region are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements which are typically obscured by overlying cloud cover or are prone to ground clutter. Here we provide a dataset which includes CL51 ceilometer observations of low clouds made during the period 14th February 2022 and 31st December 2023 at Scott Base, Antarctica (77.8 S, 166.7 E). Complemeted by ERA5, JRA55 and MERRA2 reanalyses output that has been processed using an instrument simulator. These datasets allow direct comparison between the reanalyses datasets and the ceilometer observations.</p> <p> </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.