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12 results for “Extreme Sea Level”
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
GTSM-ERA5-E dataset - Data underlying the paper "Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis"
<p>Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/full" target="_blank" rel="noopener">(Muis et al. 2020)</a>. The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.</p> <p>Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.</p> <p>Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.</p>
Extreme sea level rise along the Indian Ocean coastline: Observations and 21st century projections
<p>This file include all the data sets used to make the figures in the paper " <strong><em>Extreme sea level rise along the Indian Ocean coastline: Observations and 21<sup>st</sup> century projections</em></strong> "</p>
Coastal extreme sea levels in the Caribbean Sea induced by tropical cyclones
<p>Previous version contained the return levels of coastal significant wave height and sea surface elevation along the Caribbean coastlines corresponding to the periods of 10, 50, 100, 200 and 500 years. The return periods have been computed fitting a Generalised Pareto Distribution to a set of hydrodynamic-wave coupled ocean simulations forced with 1000 synthetic tropical cyclones. See the paper Martin et al (under review) for details. </p> <p> The file (Return_levels.mat) contains a Matlab table (with header names) indicating latitude, longitude and the return levels described above, named as Hs (significant wave height) and SSE (sea surface elevation). </p> <p>Three other datasets have been included, with the subsample of the Tropical Cyclones selected for the study (Subsample.mat), the geographic data of the coastal grid points used for our analysis (Coastaline.mat), and finally a dataset with the results of the analysis presented in the paper (Results_4_runs.mat). All datasets are provided in a .mat file, generated using Matlab. </p> <p>First dataset contains a Matlab table (with header names) indicating latitude and longitude, radius of maximum wind speed, minimum pressure and maximum wind speed a long the track of the Tropical Cyclone, for the 1000 samples selected. </p> <p>Second dataset provides both Latitude and Longitude of the coastal grid points used for the analysis. </p> <p>The last dataset contains a Matlab table (with header names) where the first column represents the Tropical Cyclone, columns 2,3 and 4 contain the maximum of sea surface elevation (SSE) during the lifetime of the Tropical Cyclone for each coastal point, for the 3 decoupled runs, wind-forced only, pressure-forced only and wind and pressure respectively. The next 4 columns contain the values of the variables for the coupled simulations with WWM-III, maximum significant wave height (Hs), maximum SSE, median of Peak Direction (Dp) and median of Peak period (Tp). All these variables contain a description in the dataset as variable information, and are provided for all coastal points of our grid. </p>
Coastal flood maps and extreme sea levels for the German Baltic Sea coast
<p>The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).</p> <p>The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will also be accessible via the preprint given below):</p> <p>Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: A new modelling framework for regional<br> assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast,<br> Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-275, in review, 2023.</p> <p>The dataset contains:</p> <p>- the location and names of flood boundary stations</p> <p>- the boundary conditions provided by the coastal ocean model at each of the flood boundary stations for all storm surge events simulated in the study cited above</p> <p>- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model</p> <p>- the spatially explicit results of the extreme value analysis for every grid cell in the coastal ocean model</p> <p>- the modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model</p> <p>- the modelled timeseries of water levels during the storm surge from January 2nd 2019 and the entire hindcast period (1961-2018) for all tide gauges along the German Baltic Sea coast</p> <p>For further information, we refer the reader to the readme file in this dataset or the publication itself.</p> <p> </p> <p> </p> <p> </p>
Antarctic Ice Sheet and emission scenario controls on 21st-century extreme sea-level changes
<p>These files accompany the paper: 'Antarctic Ice Sheet and emission scenario controls on 21st-century extreme sea-level changes'.</p> <p>Please cite the accompanying paper if you find this data useful.</p> <p><strong>Contents</strong><br> This dataset contains netCDF files with all the mean sea level scenarios and the accompanying uncertainties. The file 'esl_results.xlsx' contains the estimated GPD parameters for each tide-gauge site, as well as the estimated 100-year amplification factor and allowance. The files result_concise_table_af.pdf and result_concise_table_al.pdf contain easy-to-access overviews of the amplification factors and allowances sorted per station.<br> </p> <p>(c) 2019 California Institute of Technology. U.S. Government sponsorship acknowledged.<br> This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Improved version: A global dataset of spatially-dependent extreme sea levels
<p>These files provide an improved version of the global dataset of spatially-dependent extreme sea levels (Li et al. 2023), and can be used to assess coastal flood risk considering realistic spatial dependence structure in any coastal regions around the globe.</p> <p>We made improvements by:</p> <ul> <li>Estimating inter-cluster dependence by considering cluster connectivity;</li> <li>Calculating the year information for generated sythetic events.</li> </ul> <p>More details on the new dataset can be found in Li et al. (2024) of which a preprint doi will be provided soon. If you plan to use it or have used it, feel free to contact me and please cite our 2024 paper.</p> <p> </p> <p>References:</p> <p>Li, H., Haer, T., Couasnon, A., Enríquez, A. R., Muis, S., & Ward, P. J. (2023). A spatially-dependent synthetic global dataset of extreme sea level events. Weather and Climate Extremes, 41, 100596. https://doi.org/10.1016/j.wace.2023.100596</p> <p>Li, H., Eilander, D., Ward, P. J., & Haer, T. (2024). Improving global-scale coastal risk estimates by considering spatial dependence. Submitted. Preprint: <a href="https://doi.org/10.22541/essoar.172641608.83190937/v1">10.22541/essoar.172641608.83190937/v1</a>. </p> <p> </p>
CoDEC Dataset - Data underlying the paper "A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections "
<p>The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. The CoDEC-ERA5 dataset is the successor of GTSR <a href="https://www.nature.com/articles/ncomms11969">(Muis et al., 2016)</a> and is based on the next generation climate and hydrodynamic models. The main improvements are summarized in Table 2 of the accompanying paper <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/abstract">(Muis et al., 2020)</a>.</p> <p><br> </p>
Data files for the article "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region"
<p>This repository contains R data files required for reproducing the results from the article by Räty et al (2021) "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region", submitted to Nat. Hazards Earth Syst. Sci. See the README file for more details on the content of the files.</p>
Results of the study "Untangling the Waves: Decomposing Extreme Sea Levels in a non-tidal basin, the Baltic Sea"
<p>This archive stores the data of the study "Untangling the Waves: Decomposing Extreme Sea Levels in a non-tidal basin, the Baltic Sea" submitted to the journal Natural Hazards and Earth System Sciences.</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)
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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.