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202 results for “Climate projection”
Cloudiness delays projected impact of climate change on coral reefs
<p>The increasing frequency of mass coral bleaching and associated coral mortality threaten the future of warmwater coral reefs. Although thermal stress is widely recognized as the main driver of coral bleaching, exposure to light also plays a central role. Future projections of the impacts of climate change on coral reefs have to date focused on temperature change and not considered the role of clouds in attenuating the bleaching response of corals. In this study, we develop temperature- and light-based bleaching prediction algorithms using historical sea surface temperature, cloud cover fraction and downwelling shortwave radiation data together with a global-scale observational bleaching dataset observations. The model is applied to CMIP6 output from the GFDL-ESM4 Earth System Model under four different future scenarios to estimate the effect of incorporating cloudiness on future bleaching frequency, with and without thermal adaptation or acclimation by corals. The results show that in the low emission scenario SSP1-2.6 incorporating clouds delays the bleaching frequency conditions by multiple decades in some regions, yet the majority (>70%) of coral reef cells still experience dangerously frequent bleaching conditions by the end of the century. In the moderate scenario SSP2-4.5, however, thermal stress would overwhelm the mitigating effect of clouds by mid-century. Thermal adaptation or acclimation by corals could further shift the bleaching projections by up to 40 years, yet coral reefs would still experience dangerously frequent bleaching conditions by the end of century in SPP2-4.5. The findings show that multivariate models incorporating factors like light may improve the near-term outlook for coral reefs and help identify future climate refugia, but the long-term future of coral reefs remains questionable in moderate to higher emissions scenario.</p>
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
Climate and economic projections for the Arctic
<p>Deliverable D8.3 of the Nunataryuk project</p>
UK Climate Projections (UKCP18): Weather Patterns
<p>Provided here are the data files for the weather patterns generated for the <a href="https://www.metoffice.gov.uk/research/approach/collaboration/ukcp">2018 UK Climate Projections</a> (UKCP or UKCP18) Global ensemble (sometimes referred to as the land-gcm). The weather patterns are the same weather patterns as used in <a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/met.1563">Neal et al. (2016)</a> and used the same code to generate the weather patterns for the ensemble members. A historical equivalent of this dataset based on ERA5 can be found in <a href="https://doi.pangaea.de/10.1594/PANGAEA.942896">Neal, (2022)</a>.</p> <p>The UKCP Global model dataset features two ensembles:<br>The PPE-15 : A 15 member perturbed parameter ensemble of the HadGEM3.05 model<br>The CMIP5-13: A sub-selection of 13 members from the CMIP5 ensemble.</p> <p>The PPE-15 data is available using the RCP 2.6 and RCP 8.5 scenarios and the CMIP5-13 is available only using the RCP 8.5 scenario.</p> <p>When using these weather patterns please cite: <a href="https://doi.org/10.1007/s00382-021-06031-0">Pope et al., 2022</a>, Investigation of future climate change over the British Isles using weather patterns. Climate Dynamics. </p>
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
ECLIPS 1.1 - European CLimate Index ProjectionS database
<p>Based on the available bias corrected regional climate model results, which were created under the CORDEX project we calculated several climate indices both for past and future. (http://cordex.org/data-access/bias-adjusted-rcm-data/)</p> <p>This database contains ascii files for climate index maps with a horizontal resolution of 0.11 degree ( on a regular grid ) for Europe.</p> <p>Five GCM-RCM pairs for RCP4.5 and RCP8.5 scenarios are available for three future periods: 2041-2060, 2061-2080, 2081-2100 and one past period :1961-1990</p> <p>Additionally multi-model mean maps are available for the period 1961-1990 as a reference.</p> <p>The attached document gives information about the calculated indices and climate models ( Research paper is in prep.)</p> <p>21 zip files covers the database: 5-5 for each RCP scenarios (1 for each model) + 1 for the past period.</p> <p>File name conventions: <GCM name> <RCP scenario> <RCM name> <Index name> <period></p> <p>(GCM and RCM names are following the CORDEX name conventions)</p> <p> </p> <p>Contact: dobor.laura@gmail.com</p> <p> </p> <p> </p>
High-resolution projections of evapotranspiration and water availability for Europe under climate change
<p>Europe-wide high-resolution (1 km) gridded data of estimates of monthly and annual potential evapotranspiration (ET0), annual actual evapotranspiration (AET0) and water availability for a climate normal period largely preceding an anthropogenic warming signal (1961-1990) and for two CMIP5 multimodel future projections (2011-2040 and 2041-2070). In the ET0 calculation, the monthly and annual heat index <em>I</em> and annual <em>α</em> parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach.</p> <p>For citations and more details, please refer to "High-resolution projections of evapotranspiration and water availability for Europe under climate change" by Ştefan Dezsi, Marcel Mândrescu, Dănuţ Petrea, Praveen Kumar Rai, Andreas Hamann, Mărgărit-Mircea Nistor, published in <em>International Journal of Climatology</em> (<a href="https://doi.org/10.1002/joc.5537">https://doi.org/10.1002/joc.5537</a>)</p>
Hydro-climatic projections_Sweden
<p>The ensemble set consists of 16 available hydro-climatic (river flow (m3s-1), precipitation (mm), and temperature (oC)) projections for Sweden. It includes 3 different emission scenarios (A1B, B1 and A2), 5 GCMs (ECHAM5, ARPEGE, CCSM3, HadCM3Q and BCM), 6 RCMs (SMHI-RCA3, CNRM-ALADIN, KNMI-RACMO, MPI-REMO, HC-HadRM3, and DMI-HIRHAM), and two spatial resolutions (50 and 25 km). The results are provided for 1007 Swedish sub basins on a monthly, seasonal and annual scale. Different statistics (mean, 10th and 90th percentiles) are calculated and provided. The HBV model [Lindström, G., B. Johansson, M. Persson, M. Gardelin, and S. Bergström (1997), Development and test of the distributed HBV-96 hydrological model, <em>J. Hydrol.</em>, <em>201</em>, 272–288] was used to provide the hydrological projections. The results for the whole country of Sweden have been published in: Pechlivanidis, I.G., Gupta, H., and Bosshard, T. 2018. An information theory approach to identifying a representative subset of hydro-climatic simulations for impact modeling studies, Water Resources Research.</p>
Large projected decline in dissolved oxygen in a eutrophic estuary due to climate change
<p>This data includes the model codes and input files for the paper "Large projected decline in dissolved oxygen in a eutrophic estuary due to climate change" submitted to Journal of Geophysical Research-Oceans.</p> <p>It includes the input files and source code for ROMS and RCA model to produce simulations of Chesapeake Bay hypoxia during 1989-1998 and 2047-2098.</p> <p>ROMS (Regional Ocean Modeling System) model used in this study is version 3.4.</p> <p>RCA (Row-Column AESOP) water quality model used in this study is coupled with ROMS output, by UMCES group.</p> <p>For more details, please see the future publication.</p>
Fig. 2 in Projected Climate Change Effects On Nuthatch Distribution And Diversity Across Asia
Fig. 2. Model predictions of species distribution area retained (gray) and lost (black) due to climate change for two example species, Sitta tephronota (white triangles, western area) and S. frontalis (dotted squares, eastern area).
Fig. 1. Occurrence points for 12 in Projected Climate Change Effects On Nuthatch Distribution And Diversity Across Asia
Fig. 1. Occurrence points for 12 Sitta species and one Tichodroma species used in this study. Sitta solangiae and S. victoriae each had fewer than 5 occurrence points and were excluded from the analysis.
Fig. 3 in Projected Climate Change Effects On Nuthatch Distribution And Diversity Across Asia
Fig. 3. Model predictions regarding number and percent of nuthatch species lost due to climate change, along with estimated current and future species richness for nuthatches. Shading ramps range from white (minimum) to dark gray (maximum) as follows: number of species lost 0-5, percent of species lost 0-100, and current and future species richness 0-9 species.
MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections
<p>Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections.</p> <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> <p>The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337</p> <p>Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467</p> <p> </p> <p> </p>
Bias-corrected data from the preoperational MiKlip system for decadal climate predictions used in the PNRA-IPSODES project
<p>This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output.</p> <p>Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)</p>
Data and code to next-generation ensemble projections reveal higher climate risks for marine ecosystems
<p>Data products: <strong>Tittensor et al. (2021). Next-generation ensemble projections reveal higher climate risks for marine ecosystems, Nature Climate Change. DOI: <a href="https://doi.org/10.1038/s41558-021-01173-9">https://doi.org/10.1038/s41558-021-01173-9</a> </strong></p> <p>This data was produced using R scripts available on the GitHub repository <a href="https://github.com/Fish-MIP/CMIP5vsCMIP6">https://github.com/Fish-MIP/CMIP5vsCMIP6</a>, and was used for analysis and plotting in Tittensor et al. (2021). These R scripts are also available here as CMIP5vsCMIP6_code.zip </p> <p>Data_CMIP5.Rdata and Data_CMIP6.RData include all data used to produce global maps of percentage change in total consumer biomass. </p> <p>Data_trends_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in total consumer biomass. </p> <p>Data_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce global maps of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature. </p> <p>Data_trends_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature.</p> <p>The suffix _reducedModelSet refers to the case when only the subset of Fish-MIP models in Lotze et al. (2019) - Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change, PNAS, DOI: https://doi.org/10.1073/pnas.1900194116 - are considered. This data was used to produce some of the supplementary figures in Tittensor et al. (2021).</p> <p>Please contact Derek Tittensor (derek.tittensor@dal.ca), Camilla Novaglio (camilla.novaglio@gmail.com), or Julia Blanchard (julia.blanchard@utas.edu.au) for data interpretation and use. </p>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
Interaction matters: Bottom-up driver interdependencies alter the projected response of phytoplankton communities to climate change, links to model results
<p>This dataset provides the output of ten model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2023). In addition to information on the mesh, the dataset contains 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, carbonate system parameters (dissolved inorganic carbon, CO2 partial pressure, total alkalinity), temperature, photosynthetically active radiation, and mixed layer depths.</p> <p>File names refer to the figures in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</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.