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180 results for “Downscaling”
ECOSTRESS Tiled Downscaled Soil Moisture Instantaneous L3 Global 70 m V002
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found on the [ECOSTRESS website](https://ecostress.jpl.nasa.gov/science).The ECOSTRESS Tiled Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3T_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled soil moisture estimates are recorded into the ECO_L3T_SM data product and tiled using a modified version of the Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution.The ECO_L3T_SM Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains three layers including SM, cloud mask, and water mask.Known Issues* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.* Missing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the ‘cloud mask’ layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide).* Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A [list of scenes](https://lpdaac.usgs.gov/documents/2249/obst_all.sort.gz) confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024.* The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved t
A microRNA-129-5p/Rbfox crosstalk coordinates homeostatic downscaling of excitatory synapses.
GEO Series GSE81437. Rattus norvegicus. 12 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Data and R code for the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code used in the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery" (Møller, 2020). This is the data and code for the preprint before submission for peer review. The data and code for the revised manuscript are available at <a href="https://doi.org/10.5281/zenodo.3959005">https://doi.org/10.5281/zenodo.3959005</a>.</p> <p>Code originally written for R version 3.6.2.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p> <p> </p>
Residual Matters: How Residual Learning Can Improve Climate Downscaling
<p><strong>Title</strong>: Residual Matters: How Residual Learning Can Improve Climate Downscaling</p> <p><strong>Description</strong>:<br>This repository contains the code and data used in the project "<em>Residual Matters: How Residual Learning Can Improve Climate Downscaling</em>." This project explores how residual networks, particularly models like EDSR and VDSR, can improve the downscaling of climate data. The goal is to enhance the spatial resolution of climate variables—such as ERA5 2m temperature data—using deep learning approaches. By leveraging residual learning techniques, this project achieves higher accuracy in climate downscaling, particularly in complex terrain regions, providing more reliable data for climate predictions and research.</p> <p>The repository includes:</p> <ul> <li>Python scripts implementing EDSR and VDSR architectures for temperature data downscaling.</li> <li>Requirements file (<code>requirements.txt</code>) listing all dependencies for reproducibility.</li> <li>Sample data preprocessing and model training scripts.</li> <li>Instructions for evaluating model performance.</li> <li><code>LICENSE</code> file under the GNU General Public License (GPL) v3, permitting reuse and modification.</li> </ul> <p><strong>License</strong>: GNU General Public License v3.0</p> <p><strong>Keywords</strong>: Climate Downscaling, Deep Learning, Super-Resolution, Residual Networks, EDSR, VDSR, ERA5</p> <p><strong>Usage Notes</strong>:<br>To run the code, set up a Python environment using the dependencies listed in <code>requirements.txt</code>. The provided data files can be used as input examples, and the README offers detailed instructions on running the models. For further usage guidance, please refer to the README or the paper associated with this project.</p> <p><strong>Acknowledgments</strong>: This work is conducted as part of research at IIT Mandi, focusing on improving climate downscaling techniques with deep learning.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
RCubed WP3 empirical downscaling results, common ensemble
<p>NetCDF4 files containing the seasonal output of empirically downscaled GCM data under the RCP8.5 trajectory, has been produced in the Norwegian Research Council Project RCubed, work package 3, using a hybrid dynamical-statistical downscling approach. The data covers most of South Norway, and is available in a lat-lon projection with a resolution of 0.1° in longitude and 0.05° in latitude. The data is produced using the R software package <a href="https://github.com/metno/esd/wiki">esd</a>. The downscaled variables are seasonal mean 2-meter temperature (T2), seasonal wet-day mean (mu - the precipitation intensity on rainy (>1 mm) days), and seasonal wet-day frequency (fw - the frequency of days where precipitation is 1 mm or more). The data here is the common ensemble, that is GCM model runs which were used for downscaling T2, mu, and fw. From mu and fw seasonal mean precipitation (excluding days where precipitation is less than 1 mm) is derived. The data is also available from MET Norway's thredds server with <em>OPeNDAP</em> <a href="http://thredds.met.no/thredds/catalog/metusers/helenebe/catalog.html">http://thredds.met.no/thredds/catalog/metusers/helenebe/catalog.html</a></p> <p>The data is downscaled using ouput from a dynamical downscaling (see Pontoppidan, 2018) as reference data. This means that the data may contain biases during historical time. The precipitation fields inter-annual variation is not fully captured at the fine grid scale for which the data is produced, so we recommend applying some upscaling/areal averaging if the inter-annual correlation is of importance. The long-term trend is likely captured for the fields at the resolution of the data. An article describing the work behind the data is underway, and will be added here once published.</p> <p>Pontoppidan, M., Kolstad, E. W., Sobolowski, S., & King, M. P. ( 2018). Improving the reliability and added value of dynamical downscaling via correction of large‐scale errors: A Norwegian perspective. <em>Journal of Geophysical Research: Atmospheres</em>, 123, 11,875– 11,888. <a href="https://doi.org/10.1029/2018JD028372">https://doi.org/10.1029/2018JD028372</a></p> <p> </p> <p>The GCM runs downscaled:</p> <p>"1" "ACCESS13_r1i1p1"<br> "2" "bcccsm11_r1i1p1"<br> "3" "CanESM2_r1i1p1"<br> "4" "CanESM2_r2i1p1"<br> "5" "CanESM2_r3i1p1"<br> "6" "CanESM2_r4i1p1"<br> "7" "CanESM2_r5i1p1"<br> "8" "CCSM4_r1i1p1"<br> "9" "CCSM4_r2i1p1"<br> "10" "CCSM4_r3i1p1"<br> "11" "CCSM4_r4i1p1"<br> "12" "CCSM4_r5i1p1"<br> "13" "CCSM4_r6i1p1"<br> "14" "CESM1BGC_r1i1p1"<br> "15" "CESM1CAM5_r1i1p1"<br> "16" "CNRMCM5_r10i1p1"<br> "17" "CNRMCM5_r1i1p1"<br> "18" "CNRMCM5_r2i1p1"<br> "19" "CNRMCM5_r4i1p1"<br> "20" "CNRMCM5_r6i1p1"<br> "21" "CSIROMk360_r10i1p1"<br> "22" "CSIROMk360_r1i1p1"<br> "23" "CSIROMk360_r3i1p1"<br> "24" "CSIROMk360_r5i1p1"<br> "25" "CSIROMk360_r6i1p1"<br> "26" "CSIROMk360_r8i1p1"<br> "27" "GFDLCM3_r1i1p1"<br> "28" "GFDLESM2M_r1i1p1"<br> "29" "GISSE2H_r1i1p1"<br> "30" "GISSE2H_r1i1p2"<br> "31" "GISSE2H_r1i1p3"<br> "32" "GISSE2R_r1i1p1"<br> "33" "GISSE2R_r1i1p2"<br> "34" "GISSE2R_r1i1p3"<br> "35" "HadGEM2CC_r1i1p1"<br> "36" "HadGEM2ES_r1i1p1"<br> "37" "HadGEM2ES_r3i1p1"<br> "38" "HadGEM2ES_r4i1p1"<br> "39" "inmcm4_r1i1p1"<br> "40" "IPSLCM5ALR_r1i1p1"<br> "41" "IPSLCM5ALR_r2i1p1"<br> "42" "IPSLCM5ALR_r3i1p1"<br> "43" "IPSLCM5ALR_r4i1p1"<br> "44" "IPSLCM5AMR_r1i1p1"<br> "45" "IPSLCM5BLR_r1i1p1"<br> "46" "MIROC5_r1i1p1"<br> "47" "MIROC5_r2i1p1"<br> "48" "MIROC5_r3i1p1"<br> "49" "MIROCESM_r1i1p1"<br> "50" "MIROCESMCHEM_r1i1p1"<br> "51" "MRICGCM3_r1i1p1"<br> "52" "NorESM1M_r1i1p1"<br> "53" "NorESM1ME_r1i1p1"</p>
Global 15 x 15 Minute Grids of the Downscaled Population Based on the SRES B2 Scenario, 1990 and 2025
The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per Unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Global 15 x 15 Minute Grids of the Downscaled GDP Based on the SRES B2 Scenario, 1990 and 2025
The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100
The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds). An algorithm was developed and validated to downscale the 1/8-degree resolution data set to 1-km resolution. For a given decade, the downscaling algorithm allocates the 1/8-degree decadal amount of urban land expansion to 1-km grid cells in proportion to their total urban land amounts at the beginning of the decade. The algorithm uses an iterative process to collect any overflows from already highly-developed 1-km grid cells, and then allocates them to 1-km grid cells that are not yet fully developed. This iterative process repeats itself until all 1/8-degree amounts of urban land expansion are allocated to 1-km grid cells with no overflow. The downscaling process is applied decade by decade throughout the 21st century for each urban land expansion scenario. The final product is a set of global maps displaying the 1-km fraction of urban land, updated at decadal intervals throughout the 21st century, for five different urban land expansion scenarios consistent with the Shared Socioeconomic Pathways (SSPs).
Country-Level GDP and Downscaled Projections Based on the SRES A1, A2, B1, and B2 Marker Scenarios, 1990-2100
The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Country-Level Population and Downscaled Projections Based on the SRES A1, B1, and A2 Scenarios, 1990-2100
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Country-Level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01
The Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total populaton for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds), consistent both quantitatively and qualitatively with the SSPs. This 1-km data set is a downscaled version of the one-eighth degree (7.5 arc-minutes) data published in Jones and O'Neill (2016). The downscaling methods were published in Gao (2017). Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).
Amazon Web Services: Downscaled Climate Projections (NEX-DCP30)
The NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011] developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The dataset includes downscaled projections from 33 models, as well as ensemble statistics calculated for each RCP from all model runs available. The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).
Amazon Web Services: NASA Earth Exchange (NEX) Global Daily Downscaled Projections (NEX-GDDP)
The NASA Earth Exchange (NEX) Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011] developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The dataset is an ensemble of projections from 21 different models and two RCPs (RCP 4.5 and RCP 8.5), and provides daily estimates of maximum and minimum temperatures and precipitation using a daily Bias-Correction - Spatial Disaggregation (BCSD) method (Thrasher, et al., 2012). The data spans the entire globe with a 0.25 degree (~25-kilometer) spatial resolution for the periods from 1950 through 2005 (Historical) and from 2006 to 2100 (Climate Projections).
SMAP-Derived 1-km Downscaled Surface Soil Moisture Product V001
This data product contains global daily 1 km resolution surface soil moisture derived from the SMAP L-band radiometer. Specifically, MODIS land surface temperature data is used with the SMAP Enhanced L2radiometer Half-Orbit 9 km EASE-Grid Soil Moisture product in a downscaling algorithm to estimate soil moisture. The data set is validated by in situ soil moisture measurements from dense soil moisture networks representing different global land cover types.
High Mountain Asia 4-km Dynamically Downscaled Meteorological Data, 2000-2015 V001
This High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions – 36 km, 12 km, and 4 km – and 9 pressure levels spanning 1000 hPa – 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.
High Mountain Asia Daily 5 km Downscaled SPEAR Precipitation and Air Temperature Projections V001
This data set consists of daily, 5 km resolution precipitation and mean, near-surface air temperature projections from 2015 through 2100 for the High Mountain Asia (HMA) region. The data were generated by statistically downscaling 0.5° resolution model data from the Geophysical Fluid Dynamic Laboratory (GFDL) Seamless System for Prediction and EArth System Research (SPEAR) 30-member ensemble climate model. Projections are provided for two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5 8.5. The historical model run (1990 through 2014) used to initialize the SPEAR projections is also available.
High Mountain Asia 1 km 6-hourly Downscaled Meteorological Data 2003 to 2018 V001
This data set provides downscaled six-hourly atmospheric forcings from European Centre for Medium-Range Weather Forecasts (ECMWF) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation from 2003 to 2019 at a spatial resolution of ~1km across High Mountain Asia.
Data to: Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China
<p>This dataset contains the data to generate the results in the manuscript "Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China". The downscaled results (BCSD, BCCI, BCCAQ, and CDF-t) are created by R.</p>
ScienceDex guides
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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.