Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
20
datasets available to search
ShareScore release 0.9.0
Dataset results
20 results for “climate hazards”
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.</p> <p>Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.<br><br></p> <p><strong>The dataset includes: </strong></p> <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C<br><br>For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.<br><br></li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period <br><br>For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul> <p> </p> <p>Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896. </p>
A standardised climate change hazard vocabulary for heritage
<p>This dataset (.xlsx) is a vocabulary of climate change hazards for heritage. Hazards are the potential occurrences of natural or physical events that may cause damage or loss. Previously there had been no definitive list of climate hazards for heritage that were directly connected to changing climatic processes. This project addresses this gap by linking the created hazards to the Climatic Impact-Drivers (CIDs) produced by the Intergovernmental Panel on Climate Change (IPCC). The vocabulary consists of 52 primary and key related hazards for heritage. It is international in its remit.</p> <p>The list will be published as a vocabulary on <a href="https://www.heritage-standards.org.uk/fish-vocabularies/" target="_blank" rel="noopener">the Forum on Information Standards in Heritage (FISH)</a> where it can be accessed in multiple formats <a href="https://heritagedata.org/live/schemes/38076.html">including linked data</a>. This .xlsx format places the hazards in relationship to each other and in their CID context. Candidate terms can be submitted to the research group Heritage Environmental Risk and Data Analytics <a href="mailto:herada@ucl.ac.uk" target="_blank" rel="noopener noreferrer">herada@ucl.ac.uk</a> (terms submitted to <a href="mailto:Terminologies@HistoricEngland.org.uk">Terminologies@HistoricEngland.org.uk</a> will be directed to the research group for approval). An accompanying <a href="https://historicengland.org.uk/research/results/reports/13-2024?search=13%2F2024&searchType=research+report">Historic England Research Report</a> provides more information, including the methodology of the project (available in both English and Welsh).</p> <p>The authors are interested in hearing from users of the vocabulary, specifically those that link the hazards to observed impacts of climate change on parts of the historic environment. This dataset was produced as part of a funded 6-month project between Historic England and the UCL Institute for Sustainable Heritage on developing a standardised vocabulary of climate change hazards for the historic environment. The Welsh version of the dataset was translated in 2025 by Lingo Soar, in collaboration with Fforest Fawr UNESCO Geopark, and as part of the UK National Commission for UNESCO's Climate Change and UNESCO Heritage project.</p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
Prosociality as response to slow- and fast-onset climate hazards
<p>This repository contains the information videos used in the treatments, as well as the data and code that replicates tables and figures for the following paper:<br> <strong>Title:</strong> Prosociality as response to slow- and fast-onset climate hazards<br> <strong>Authors:</strong> Ivo Steimanis<sup>1</sup> & Björn Vollan<sup>1,*</sup><br> <strong>Affiliations:</strong> <sup>1</sup> Department of Economics, Philipps University Marburg, 35032 Marburg, Germany<br> <strong>*Correspondence to:</strong> Björn Vollan <a href="mailto:bjoern.vollan@wiwi.uni-marburg.de">bjoern.vollan@wiwi.uni-marburg.de</a><br> <strong>ORCID:</strong> Steimanis: 0000-0002-8550-4675; Vollan: 0000-0002-5592-4185<br> <strong>Classification:</strong> Social Sciences, Economic Sciences<br> <strong>Keywords:</strong> climate hazards, prosociality, in-group favoritism, antisociality</p> <p> </p>
Estimating durations of disruptions to freight transport due to climate-related hazards
<p>A table of hazard intensity thresholds and notes on how to estimate the duration of disruption to freight transport (road and rail) due to climate-related hazards (extreme heat, flooding and tropical cyclone winds).</p>
D2.1 MIRACA Climate Hazard Database
<p><span>The MIRACA Climate Hazard Database is a collection of meteorological, geological and hidrological hazards datasets and indicators at a Pan-European and global level. This dabase is the Deliverable 1.2 of the MIRACA Project (Multi-hazard infrastructure Risk Assessment for Climate Adaptation).</span></p>
UK climate hazard and climate change adaptation resources for heritage
<p><span>This dataset (.xlsx) is a compendium of climate change hazard data and adaptation resources for cultural heritage. It was created by JBA Consulting for Historic England and is accompanied by a <a href="https://historicengland.org.uk/research/results/reports/16-2024">research report</a> which provides the background, methodology, and results of the project. One aim of the project was to identify and compile climate hazard resources (data and tools) that could assist those managing the UK historic environment, with specific attention paid to data availability, spatial resolution, and format. </span></p> <p><span> </span><span>The project identified 73 datasets and 38 tools. The datasets were linked to relevant climate hazards from a standardised hazard vocabulary (<a href="../records/10868587">Thomas, 2024</a>). The attached pdf file provides further details on how to use the dataset. Further information can be found in the report, and questions can be addressed to Kate Guest at <a href="mailto:Kate.Guest@HistoricEngland.org.uk">Kate.Guest@HistoricEngland.org.uk</a>. </span></p>
Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs
<p><strong>Community engagement in planning is essential for effective and just climate adaptation. However, historically underserved communities are often difficult to reach through traditional means of soliciting public input. The Climate Adaptation Solutions Accelerator (CASA) through School-Community Hubs project identifies public schools as promising sites for building both community engagement and community capacity for climate adaptation. To serve in this role, schools need information about the intersecting threats climate change poses to the communities they serve. The Climate Hazard Dashboard for California Schools is a platform that maps the current and future risks associated with five climate hazards, including wildfire, extreme heat days, wildfire extreme precipitation, flooding, and sea level rise, for the nearly 10,000 public schools serving Kindergarten through Grade 12 students in California. Each hazard is mapped and visualized at the school level, providing an accessible way for administrators, teachers, students, and neighborhoods to explore data reflecting the climate hazards they face, at a scale relevant to their communities. The dashboard also provides an aggregate summary hazard metric. </strong></p>
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Regional and global climate risks for reef corals: incorporating species-specific vulnerability and exposure to climate hazards
<p>Climate change is driving rapid and widespread erosion of the environmental conditions that formerly supported species persistence. Existing projections of climate change typically focus on forecasts of acute environmental anomalies and global extinction risks. The current projections also frequently consider all species within a broad taxonomic group together without differentiating species-specific patterns. Consequently, we still know little about the explicit dimensions of climate risk (i.e., species-specific vulnerability, exposure and hazard) that are vital for predicting future biodiversity responses (e.g., adaptation, migration) and developing management and conservation strategies. Here, we use reef corals as model organisms (n = 741 species) to project the extent of regional and global climate risks of marine organisms into the future. We characterise species-specific vulnerability based on the global geographic range and historical environmental conditions (1900–1994) of each coral species within their ranges and quantify the projected exposure to climate hazard beyond the historical conditions as climate risk. We show that many coral species will experience a complete loss of pre-modern climate analogs at the regional scale and across their entire distributional ranges, and such exposure to hazardous conditions is predicted to pose substantial regional and global climate risks to reef corals. Although high-latitude regions may provide climate refugia for some tropical corals until the mid-21st century, they will not become a universal haven for all corals. Notably, high-latitude specialists and species with small geographic ranges remain particularly vulnerable as they tend to possess limited capacities to avoid climate risks (e.g., via adaptive and migratory responses). Predicted climate risks are amplified substantially under the SSP5-8.5 compared with the SSP1-2.6 scenario, highlighting the need for stringent emission controls. Our projections of both regional and global climate risks offer unique opportunities to facilitate climate action at spatial scales relevant to conservation and management.</p>
Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs
Open the record for dataset details and reuse information.
Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
Open the record for dataset details and reuse information.
Regional and global climate risks for reef corals: incorporating species-specific vulnerability and exposure to climate hazards
Open the record for dataset details and reuse information.
African Climate Hazard Assessment; The impacts of climate change and the vulnerability of African nations
<p>The ACHA Index assesses the vulnerability to climate hazards for African nations. The uploaded datasets include the final index rankings as well as the individual aggregations of each hazard.</p>
Multivariate hazard assessment for nonstationary seasonal flood extremes considering climate change
<p>Processed streamflow data used to create the plots shown in the paper ("Multivariate hazard assessment for nonstationary seasonal flood extremes considering climate change" published on JGR_A) for reproducibility</p>
Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019
<p>A (quasi) global gridded hazard and socioeconomic exposure database, spanning 2000 to 2019, covering 14 climate impact drivers (CIDs) and 3 geophysical hazards.</p> <p>The Platform for Economic Analysis of Climate Hazards (PEACH) provides monthly data at an approximate 10 × 10 km resolution, with full coverage between 2004 and 2015. Unlike event-based datasets, PEACH avoids rigid inclusion thresholds whenever possible, enabling flexible definition of “extreme events” and analysis across the full hazard intensity spectrum. Six complementary socioeconomic and environmental variables are included: population, nighttime light, relative wealth, critical infrastructure, vegetation indices, and land use.</p> <p><strong>If you use PEACH in your work, please cite the paper:</strong><br>R. Reinhardt (2025) <em>The Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019, </em> CES Working Paper, Université Paris 1 Panthéon-Sorbonne <a href="https://shs.hal.science/halshs-05314666v1" target="_blank" rel="noopener">⟨halshs-05314666⟩</a></p> <p><strong>Data Description</strong><br>The main dataset is provided in Apache Arrow Feather format, partitioned into world subregions based on the UN M49 classification. Each file contains monthly hazard exposure and socioeconomic variables per 10 km grid cell. Variable definitions, formats, and usage notes are detailed in the accompanying README.</p> <p>For ease of use, see also the <em>ConversionScript.zip</em> files, which include exemplary code for loading the data in R and Python.</p> <p><strong>Overview</strong></p> <ul> <li><strong>Temporal resolution:</strong> Monthly (2000–2019; full coverage 2004–2015)</li> <li><strong>Spatial resolution:</strong> 10 × 10 km grid cells (global coverage, rainfall quasi-global: 50°N–50°S)</li> <li><strong>Projection:</strong> EPSG:4326 (WGS 84, standard Mercator)</li> <li><strong>File format:</strong> Apache Arrow Feather (.feather), partitioned by UN M49 subregions</li> <li><strong>Hazards included:</strong></li> <ul> <li><em>Climate impact drivers (14):</em> mean air temperature, cold spells, extreme heat, mean precipitation, heavy precipitation, floods (river/pluvial), landslides, drought, fire weather & wildfires, severe windstorms, tropical cyclones, sand and dust environments (proxy indicator, not direct dust storm records), hail, air pollution (PM2.5/PM10).</li> <li><em>Geophysical hazards (3):</em> earthquakes, volcanic eruptions, tsunamis</li> </ul> </ul> <p> </p> <p><strong>Access information:</strong></p> <p><em>Access to the PEACH dataset is granted upon request for non-commercial academic and policy research. Requests will be processed as timely as possible, typically within a few business days.</em></p>
A Survey of State Hazard Mitigation Officers About Integrating Climate Change into Hazard Mitigation Planning
<p>56 U.S. state hazard mitigation officers (SHMOs) were invited by email to participate in a brief, 25 question online survey administered through SurveyMonkey (SurveyMonkey Inc., San Mateo, CA, USA: <a href="http://www.surveymonkey.com/">www.surveymonkey.com</a>) in March & April 2018. Thirty-five (62.5%) SHMOs responded to the survey. The survey sought to assess climate change integration into state hazard mitigation plans, as well as barriers and facilitators to such integration.</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.