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2,837 results for “Climate Data”
Supporting Data for Hahn et al. J. Climate: Contribution of AMOC Decline to Uncertainty in Global Warming via Ocean Heat Uptake and Climate Feedbacks
<p>This dataset includes CESM2 model output for the dehose4x experiment in Hahn et al.: “Contribution of AMOC Decline to Uncertainty in Global Warming via Ocean Heat Uptake and Climate Feedbacks” submitted to Journal of Climate. The piControl and abrupt-4xCO2 experiments for CESM2 and other CMIP6 models can be found in the Earth System Grid Federation (ESGF) repository at <a href="https://esgf-node.llnl.gov/projects/esgf-llnl/" target="_blank" rel="noopener">https://esgf-node.llnl.gov/projects/esgf-llnl/</a>.</p>
Data used to generate results for Meireles et al. 2024. The Miners of South America: impacts of climate change on the distribution of Geositta miners along elevational gradients
<p><span>Data used to generate results for Meireles et al. 2024. The Miners of South America: impacts of climate change on the distribution of <em>Geositta miners</em> along elevational gradients </span></p> <p><span> </span><span>Here we included all the data used in this paper.</span></p> <p><strong><span>Table1</span></strong><span>. Occurrence records for the seven species of <em>Geositta</em> miners, their elevation and climate suitability values per record for the present and future in different climate models (GCM: MPI-ESM1-2-HR and MRI-ESM2-0) and different scenarios (Optimistic: ssp245 and Pessimistic: ssp585).</span></p>
Data from: Stand Diversity does not Mitigate Increased Herbivory on Climate-Matched Oaks in an Assisted Migration Experiment
<p>Assisted migration is a tree planting method where tree species or populations are translocated with the aim of establishing more climate-resilient forests. However, this might potentially increase susceptibility of translocated trees to herbivory. Stand diversification through planting trees in species or genotypic mixtures may reduce the amount of damage by insect pests, but its effectiveness in mitigation of excess herbivory on climate-matched trees has seldom been explored.</p>
Supplementary data: "Speed of technological transformations required in Europe to achieve different climate goals"
<p>This repository includes results discussed in the paper "<a href="https://arxiv.org/abs/2109.09563">Speed of technological transformations required in Europe to achieve different climate goals</a>"</p> <p>In the paper, we used the open energy modelling framework <a href="https://pypsa.org/">PyPSA</a>, the model <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec v0.5.0</a> and the cost and technology assumptions included in the repository <a href="https://github.com/PyPSA/technology-data">technology-data v0.2.0</a></p> <p>The directory 'version-baseline' includes the network objects obtained as an outcome of the optimization for the different years and carbon budgets in the baseline scenario.</p> <p>This dataset is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Supporting data for "Climate Outcomes of Earth-Similar Worlds as a Function of Obliquity and Rotation Rate"
<p>Supporting data for the Astrophysical Journal Paper "Climate Outcomes of Earth-Similar Worlds as a Function of Obliquity and Rotation Rate":</p> <p>The datafiles contain various processed output from ROCKE-3D climate model runs described in the manuscript. In each case, the files contain "data behind the figure" as described below:</p> <p>The file "ROCKE_global_mean_data_all.csv" contains the overall global means of the 50-year averaged climatology from the various runs presented in the manuscript. These data were used directly in Figures 2, 4, 8 and 10.</p> <p>The files "ROCKE_mapdata*.csv" contain the two dimensional maps of the key habitability variables (surface temperature and precipitation) from the 50-year average climatologies. The maps are provided in the equal-angle latitude-longitude grids native to the ROCKE-3D simulations. These files are provided for a set of 9 runs that span the parameter space (Rotation 2, 8, and 128 days; Obliquity 0, 45, and 90 degrees), as well as the run with Earth-similar parameters (Rotation 1 day, Obliquity 25 degrees). The rotation periods and obliquities are denoted by the filename. These data were used directly in Figure 3. The habitability fractions displayed in Figures 1 and 9 can be derived by applying the criteria from Section 1 of the manuscript to the temperature and precipitation maps.</p> <p><br> System requirements:</p> <p>The data are provided as delimited plain text files (Comma-Separated-Variable (CSV)) formats, and should be readable by any standard tools. Each file contains two header lines, the first containing the variable name and the second containing the data units.<br> </p>
Data from: An artificial habitat facilitates a climate-mediated range expansion into a suboptimal novel ecosystem
[No abstract entered]
Daily climate and rainfall data for Niger 1983-2021, for use in SARRA-O crop simulation model
<p>This dataset contains daily rainfall and climate data for Niger, that can be used as input of the <a href="https://github.com/SARRA-cropmodels/SARRA-O">SARRA-O spatialized crop simulation model</a>. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for Niger, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²) at 0.1° spatial resolution from 01/01/1981 to 31/12/2021</li> <li>TAMSAT v3.0 (doi:<a href="http://doi.org/10.1038/sdata.2017.63">10.1038/sdata.2017.63</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.0375° spatial resolution from 01/01/1983 to 31/12/2021</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/1981 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>, on June 14th and 15th, 2023.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Daily climate and rainfall data for northern Cameroon 2020-2022, for use in SARRA-Py crop simulation model
<p>This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Data of Sherriff-Tadano et al. 2024 Climate of the Past
<p>The zip file contains a csv files, which summarises all the parameter sets and results for creating figures, and simulation results of from BISICLES (ice sheet model) and FAMOUS (atmospheric model). It also contains the sea surface temperature file, which was used as the target for our slab ocean simulations.</p>
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Hourly Balancing Authority Transfers, Streamflows, and Climate Data for Carolinas Region
<p>The data contain the electricity transfers and relevant indicators associated with nine exchanges between balancing authorities in the Carolinas region. The information comes from the following sources:</p> <p>Hydrology Dataset from USGS:</p> <p>CPLE<span> </span>02084557- Van Swamp near Hoke, NC</p> <p><span> </span>02089000- Neuse River near Goldsboro, NC</p> <p><span> </span>02087324-Crabtree Creek at US 1 at Raleigh, NC</p> <p>DUK<span> </span>0212427947- Reedy Creek at SR2803 near Charlotte, NC</p> <p><span> </span>0212430653- McKee Creek at SR2804 near Wilgrove, NC</p> <p><span> </span>02124080- Clarke Creek near Harrisburg, NC</p> <p>SC<span> </span>02171500- Santee River near Pineville, SC</p> <p><span> </span>02131010- Pee Dee River below Pee Dee, SC</p> <p><span> </span>02130980- Black Creek near Quinby, SC</p> <p>SCEG<span> </span>02175500- Salkehatchie River near Miley, SC</p> <p><span> </span>02176500- Coosawhatchie River near Hampton, SC</p> <p>YAD<span> </span>02121500- Abbotts Creek at Lexington, NC</p> <p>CPLW<span> </span>02140991- Johns River at Arneys Store, NC</p> <p>*CPLE = Duke Energy Progress East; DUK = Duke Energy Carolina; SC = Santee Cooper; SCEG = South Carolina Electric & Gas Company; YAD = Yadkin, Inc.; CPLW = Duke Energy Progress West</p> <p> </p> <p>Balancing Authority Data</p> <p><span>Nugent J, Chini C M, Peer R A M and Stillwell A S 2023 Monthly virtual water transfers on the U.S. electric grid Environ. Res. Infrastruct. Sustain. 3 035006</span></p> <p><span>Balancing Authority Climate Data</span></p> <p>Burleyson C, Thurber T and Vernon C 2023 Projections of hourly meteorology by balancing authority based on the IM3/HyperFACETS thermodynamic global warming (TGW) simulations (v1.0.0) [Data set] MSD-LIVE Data Repos.</p> <p>NOAA National Centers for Environmental Information 2024 U.S. Air Force 14th weather squadron (2013): United States Air Force 14th weather squadron surface weather observations (restricted). NCEI DSI 9966</p> <p> </p>
Data and Codes for "Clouds Attenuate the Increase of Downwelling Longwave Radiation Over Land in Climate Warming"
<p>This folder contains the data and codes generated for the manuscript titled "Clouds Attenuate the Increase of Downwelling Longwave Radiation Over Land in Climate Warming."</p>
Data supporting the publication of "Interactive effects of climate change and land-use change on mammal range retraction in Great Britain"
<p><strong>Table S1 (Species records) provided as a separate .xlsx file in Supporting Information. </strong>List of species included in the sample with corresponding attributes, number of records and rates of change over time.</p> <p>Column A (Scientific name): species’ accepted scientific name (n = 43 species).</p> <p>Column B (Common name): species’ common name in Great Britain (n = 43 names).</p> <p>Column C (Order): species’ taxonomical Order (n = 6 Orders).</p> <p>Column D (Family): species’ taxonomical Family (n = 14 Families).</p> <p>Column E (Guild): species’ sampling guild (n = 3 Guilds, either Bats, Midlarge, or Small</p> <p>Column F (Distribution): species’ distribution status in Great Britain (n = 3 Statuses, either Native, Naturalised, or Non-Native).</p> <p>Column G (Habitat): species’ habitat preference (n = 2 Habitats, either Terrestrial or Freshwater).</p> <p>Column H (Records): total number of records per species from 1960 to 2016 (average = 10,931).</p> <p>Column I (1960s): total number of records per species from 1960 to 1969 (average = 420).</p> <p>Column J (1970s): total number of records per species from 1970 to 1979 (average = 457).</p> <p>Column K (1980s): total number of records per species from 1980 to 1989 (average = 423).</p> <p>Column L (1990s): total number of records per species from 1990 to 1999 (average = 641).</p> <p>Column M (2000s): total number of records per species from 2000 to 2010 (average = 943).</p> <p>Column N (2010s): total number of records per species from 2011 to 2016 (average = 870).</p> <p>Column O (Hectads TP1): number of hectads where the species has been recorded in Time Period 1, from 1960 to 1992 (average = 892).</p> <p>Column P (Hectads TP2): number of hectads where the species has been recorded in Time Period 2, from 2000 to 2016 (average = 1,117).</p> <p>Column Q (Hectads Total): number of hectads where the species has been recorder from 1960 to 2016 (average = 1,315).</p> <p>Column R (Extirpation rate): species’ extirpation rate, calculated as the ratio of extirpations over the sum of extirpations and persistences (average = 0.24). The sum of extirpation and persistence rates is always equal to 1.</p> <p>Column S (Persistence rate): species’ persistence rate, calculated as the ratio of persistences over the sum of extirpations and persistences (average = 0.76). The sum of persistence and extirpation rates is always equal to 1.</p> <p>Column T (Occupancy TP1): species’ occupancy estimate in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.395).</p> <p>Column U (Occupancy TP2): species’ occupancy estimate in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.403).</p> <p>Column V (Occupancy change): change in the species’ occupancy estimates between Time Periods 1 and 2, as calculated in Frescalo (average = 0.076).</p> <p>Column W (Occupancy change slope): average yearly change in the species’ occupancy estimates from 1960 to 2016, as calculated in Frescalo (average = -0.001).</p> <p>Column X (Frequency TP1): adjusted frequency of occurrence in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.527).</p> <p>Column Y (Frequency TP2): adjusted frequency of occurrence in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.461).</p> <p>Column Z (Frequency change): change in the adjusted frequency of occurrence between Time Periods 1 and 2, as calculated in Frescalo (average = -0.066).</p>
Storyline data used in the paper "Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes"
<p>We provide the storyline data (in NetCDF format) used in the paper:”<strong>Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes”. </strong>The data is structured five .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climates. The data includes the five ensemble members (E1 to E5) and ensemble mean variables at winter season (DJF) in 2020/2021.</p> <p>Files of simulation ensemble member data are named as:</p> <p><span> </span>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_E{ensemble member}_DJF-{years}_dailymean.nc”</p> <p>Files of simulation ensemble-mean data are names as:</p> <p>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_DJF-{years}_ensmean.nc”</p> <p>Files of free-run (CMIP6) data are names as:</p> <p>“freerun_{variable name}_DJF-{year}_ensmean_31days-runmean_11years-ydaymean.nc”</p> <p>Variables includes:<span> </span></p> <ul> <li>Mean 2m Temperature (t2m)</li> </ul> <ul> <li>Downward net surface solar radiation (srads)</li> </ul> <ul> <li>Total cloud cover (aclcov)</li> <li>Downward solar radiation at clear sky (rsdscs)</li> <li>sea ice concentration (friac)</li> </ul> <p>Only for present climate:</p> <ul> <li>Zonal/meridional wind at 850hPa (u850,v850)</li> <li>500 hPa Geopotential Height (z500)</li> </ul> <p><span> </span></p>
Pan-European Climate Database 4.1 - wind onshore data Pan-European Onshore Zones - Parquet format
<p>Data from <a href="https://cds.climate.copernicus.eu/datasets/sis-energy-pecd?tab=documentation">Climate and energy related variables from the Pan-European Climate Database derived from reanalysis and climate projections</a></p> <p>The python script to download using the CDS API are included.</p> <p>This is a Parquet (long and tidy) version of the original CSV files. </p>
Data from: Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015
Changes in vegetation phenology directly reflect the response of vegetation growth to climate change. In this study, using the Normalized Difference Vegetation Index (NDVI) dataset from 1982 to 2015, we extracted start date of vegetation growing season (SOS), end date of vegetation growing season (EOS) and length of vegetation growing season (LOS) in the middle and eastern Eurasia region and evaluated linear trends in SOS, EOS and LOS for the entire study area, as well as for four climatic zones. The results show that the LOS has significantly increased by 0.27 d/yr (days per year), mostly due to a significantly advanced SOS (-0.20 d/yr) and a slightly delayed EOS (0.07 d/yr) over the entire study area from 1982 to 2015. The vegetation phenology trends in the four climatic zones are not continuous throughout the 34 year period. Furthermore, discrepancies in the shifting patterns of vegetation phenology trend existed among different climatic zones. Turning points (TP) of SOS trends in the Cold zone, Temperate zone and Tibetan Plateau zone occurred in the mid or late 1990s. The advanced trends of SOS in the Cold zone, Temperate zone and Tibetan Plateau zone exhibited accelerated, stalled and reversed patterns after the corresponding TP, respectively. The TP did not occurred in Cold-Temperate zone, where the SOS showed a consistent and continuous advance. TPs of EOS trends in the Cold zone, Cold-Temperate zone, Temperate zone and Tibetan Plateau zone occurred in the late 1980s or mid-1990s. The EOS in the Cold zone, Cold-Temperate zone, Temperate zone and Tibetan Plateau zone showed weak advanced or delayed trends after the corresponding TP, which were comparable with the delayed trends before the corresponding TP. The shift patterns of LOS trends were primarily influenced by the shift patterns of SOS trends and were also heterogeneous within climatic zones.
Data from: Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data
Climate conditions, such as temperature or precipitation averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous U.S., and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability versus 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs.
Nonlinear sensitivity of glacier-mass balance to climate attested by temperature-index models; synthetic data
<p>Synthetic data and results of the PDD model used in the paper <a href="https://doi.org/10.5194/tc-2022-210">https://doi.org/10.5194/tc-2022-210</a></p>
Input data for the paper "Assessing the viability of CO2 storage in offshore formations of the Gulf of Mexico at a scale relevant for climate-change mitigation"
<p>This repository contains the input data necessary to reproduce the modeling results shown in the paper "Assessing the viability of CO2 storage in offshore formations of the Gulf of Mexico at a scale relevant for climate-change mitigation", published at the International Journal of Greenhouse Gas Control journal in May 2023.</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</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.