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.
283
datasets available to search
ShareScore release 0.9.0
Dataset results
283 results for “ocean model”
Model results and observation data in Zhang et al. modeling of wave interference at Ocean Beach, CA
<p>The dataset contains modeling results and observation data supporting the manuscript of Phase-resolved modeling of wave interference and its effects on nearshore circulation in a large ebb shoal-beach system by Yu Zhang, Fengyan Shi, Jim Kirby, Xi Feng.</p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
Destructive potential of planetary meteotsunami waves: ATAL ocean model results
<p> Based on the well-documented Hunga Tonga–Hunga Ha'apai volcano explosive eruption on 15 January 2022, we developed the "Atmospheric Tsunami Associated with Lamb waves" or ATAL ocean model and performed 12 realistic and process oriented numerical simulations to assess the sea-level hazards posed by planetary meteotsunami waves. Here, we provide the ATAL model results of maximum sea-levels during day 1 and day 2 after the eruption for:</p> <p>- the baseline simulation: trying to reproduce the event as realistically as possible</p> <p>- the 10 resonance simulations (_r_): trying to derive the speed of the Lamb waves which will generate the maximum resonance (i.e., Proudman resonance) in the ocean basins by dividing the baseline speed by r = 1.25, 1.40, 1.50, 1.60, 1.65, 1.75, 2.00, 3.00, 4.00, 5.00. The full Proudman resonance was obtained for r = 1.50</p> <p>- the amplification simulation (_amp_): trying to match the Proudman resonance amplification by multiplying by 10 the Lamb waves amplitudes</p>
Variability in the global ocean carbon sink from 1959-2020 by correcting models with observations (LDEO-HPD)
<p><strong>* The latest versions of this dataset are maintained and available here: <a href="https://zenodo.org/record/7901433">https://zenodo.org/record/7901433</a> *</strong></p> <p>The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO2 and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO2 data to global coverage by using GOBMs as priors, applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO2 product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO2 growth and volcanic eruptions.</p> <p>This data is the final reconstruction of air-sea CO2 fluxes for 1959-2020 using the mean pCO2 from the corrected models. Both annual flux time series and spatially explicit fluxes are included. RIVERINE CARBON EFFLUX ADJUSTMENTS ARE NOT INCLUDED WITHIN THESE FILES. File metadata provides units.</p>
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model
<p>This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.</p> <p> </p> <p>This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.</p>
BSIOM Baltic Sea-Ice Ocean Model 1950-2022
<p>Daily temperature, dissolved oxygen and salinity concentration data from the <a name="_Hlk167710471"></a>Baltic Sea Ice Ocean Model (BSIOM) from 1950 to 2022. A detailed description of the equations and modifications made, necessary to adapt the model to the Baltic Sea, can be found in Lehmann et al. (see references below). The model is forced realistically using the ERA5 global re-analysis in the preliminary extension version back to 1950. The resolution of the original output from BSIOM is specified with vertical 60 levels, which enables to resolve the upper 100m by layers of 3 m thickness. The horizontal resolution of the model is 2.5km. The datasets here presented are divided into surface and bottom files. We calculated the sea surface temperature (SST) using the average values from the first three depth layers (upper nine meters), while the sea bottom temperature (SBT) was the average of the last three depth layers following the bathymetry of the Western Baltic Sea (lower nine meters). The values for dissolved oxygen and salinity concentration were calculated in the same manner. The data is spatially constrained to the area between 9° 45’ to 14° 45’ East and 53° 53’ to 56° 30’ North.</p> <p>In the datasets, the following data is available: </p> <ul> <li>Lat: latitude values in degrees (°)</li> <li>Long: longitude values in degrees (°)</li> <li>Depth: depth values (m). The surface file shows a constant value of 1.5, while the bottom file shows the maximum depth (m) for that pixel. To show the depth in reference to the sea-level reference (0 meters) the values should be multiplied by -1.</li> <li>temp: temperature (°C)</li> <li>SO: salinity (g/kg)</li> <li>O2: dissolved oxygen (ml/L-1)</li> <li>t: day of the year (YYYY-MM-DD)</li> <li>LongLat: string with the combination of longitute and latitude values (only for the bottom file)</li> <li>GridID: identification value for each set of coordinates (Long and Lat)</li> </ul>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
Files from barotropic and baroclinic idealized model runs of the Southern Indian Ocean
<p>These data files correspond to two idealized model runs of the Southern Indian Ocean using the Regional Ocean Modelling System (ROMS) as a framework. Both simulations are forced with monthly mean QuikSCAT winds and are run at a 1/3 degree resolution. </p> <p>The barotropic model is single layer with realistic ETOPO2 bathymetry, a two arc minute ocean-floor elevation data-set smoothed to a resolution of 55.2 km. The file corresponding to this simulation is named: roms_avg_barotropic.</p> <p>The baroclinic model is a 1 and a half layer model where the value of the pycnocline depth and the reduced gravity parameter is set at the initialization stage. Two simulations are presented, the first where 'relaistic' initialization parameters of H=800m and g'= 0.0134 m/s(^2), and the second where the density gradient between the active and passive layers is reduced to a g' of 0.0076 m/s(^2). The two data sets corresponding to these simulations are titled: roms_avg_800_0134 and roms_avg_800_0076</p> <p>Below find a list of variable names and descriptions:</p> <p>zeta=anomaly in thickness of active layer<br> ubar= mean zonal velocity of active layer<br> vbar= mean meridional velocity of active layerh=depth of bathymetry in barotropic model; pycnocline depth in baroclinic model<br> coast=coastline<br> lon_rho=longitude corresponding the density coordinates<br> lat_rho=latitude corresponding the density coordinates<br> lon_u=longitude corresponding the zonal velocities<br> lat_u=latitude corresponding the zonal velocities<br> lon_v=longitude corresponding the density velocities<br> lat_v=longitude corresponding the meridional velocities<br> time=days since model simualtion started</p>
Modelled isoscape data for: "Oceanographic and biogeochemical drivers cause divergent trends in the nitrogen isoscape in a changing Arctic Ocean"
<p>The data included in this repository includes the biogeochemical model output of nitrogen isotope fields. These data were generated by simulations with the NEMOv4.0 Ocean General Circulation Model, SI3 sea ice model, and Pelagic Interactions Scheme for Carbon and Ecosystem Studies version 2 (PISCESv2) biogeochemical model. Nitrogen isotopes were integrated within PISCESv2 for the purpoes of this study.</p> <p>All data here are in longitude, latitude and time cordinates. No depth coordinate is provided as all values are averaged over the upper 100 metres of the model.</p> <p> </p> <p>The file names mean the following:<br> </p> <p>ETOPO - refers to how the curvilinear, native grid of the model was re-gridded to a regular 360x180 longitude-latitude grid uisng the etopo60 coordinate system.</p> <p>JRA55 - these are the reanalysis-driven simulations, for which we used the Japanese Atmospheric Reanalysis (JRA55do).</p> <p>future - these are the emissions-driven simulations (historical from 1850-2005, then according to Representative Concentration Pathway 8.5 from 2006-2100.)</p> <p>picontrol - these are parallel to the emissions-driven simulations but do not include the increase in emissions.</p> <p>ndep - refers to if the historical increase in anthropogenic nitrogen deposition was included in the simulation</p> <p>d15Nno3 - isotopic composition of nitrate averaged over the upper 100 metres</p> <p>d15Npom - isotopic composition of particulate organic matter averaged over the upper 100 metres</p> <p>predictors - the average values of salinity, N* and particulate organic matter over the upper 100 metres</p> <p>annualave - annual averages, so that the data are inter-annual</p> <p>1970-1990ave_months - average monthy values over the period 1970-1990.</p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.
<p>This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.</p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)
<p>The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.</p>
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
<p>This is the underlying dataset of the publication "Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3" by Gürses et al. (in press), Geoscientific Model Development. In addition to unstructured mesh information, it contains the results of ocean biogeochemistry in the Regulated Ecosystem Model version 3 (REcoM3) coupled to the ocean and sea ice model FESOM2.1. The model simulations cover the period 1958 to 2021 and are forced with observed atmospheric CO<sub>2</sub> and JRA55-do atmospheric reanalyses. Three simulations are provided:</p> <p><strong>simulation A:</strong> with varying climate forcing conditions and varying atmospheric CO<sub>2</sub></p> <p><strong>simulation B:</strong> with constant climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p><strong>simulation D:</strong> with varying climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p>The following 2D/3D monthly-averaged fields (data period is given in parentheses) are provided on the native model grid:</p> <ul> <li><strong>Alk:</strong> Alkalinity (2012-2021)</li> <li><strong>CO2f:</strong> Air-Sea CO<sub>2</sub> flux (1800-2021)</li> <li><strong>DFe: </strong>Dissolved Iron concentration (2012-2021)</li> <li><strong>DIN:</strong> Dissolved Inorganic Nitrogen concentration (2012-2021)</li> <li><strong>DIC:</strong> Dissolved Inorganic Carbon concentration (1800, 1994-2021)</li> <li><strong>DSi:</strong> Dissolved Inorganic Silicon concentration (2012-2021)</li> <li><strong>DiaChl:</strong> Chlorophyll a concentration of diatoms (2012-2021)</li> <li><strong>DetC:</strong> Carbon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetCalc: </strong>Calcite concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetSi: </strong>Silicon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>idetz2c:</strong> Carbon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2calc:</strong> Calcite concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2si:</strong> Silicon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>HetC:</strong> Small zooplankton carbon biomass (2012-2021)</li> <li><strong>MLD:</strong> Mixed Layer Depth (2012-2021)</li> <li><strong>NPPn:</strong> Net Primary Production of small pyhtoplankton (2012-2021)</li> <li><strong>NPPd:</strong> Net Primary Production of diatoms (2012-2021)</li> <li><strong>O2:</strong> Dissolved Oxygen concentration (2012-2021)</li> <li><strong>PhyChl:</strong> Chlorophyll a concentration of small phytoplankton (2012-2021)</li> <li><strong>Zoo2C:</strong> Macrozooplankton carbon biomass (2012-2021)</li> <li><strong>pCO2s:</strong> Partial pressure of carbon dioxide of the surface ocean (1970-2021)</li> <li><strong>salt:</strong> Salinity (2012-2021)</li> <li><strong>temp:</strong> Temperature (2012-2021)</li> <li><strong>w:</strong> Vertical velocity (2012-2021)</li> </ul> <p>Please contact the corresponding author (ozgur.gurses@awi.de) for further information.</p>
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
<p>Model output of the global eddy-rich configuration used in the Geoscientific Model Development publication: "Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)" </p> <p>Abstract: This paper describes the global eddying ocean-sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16° and 98 vertical levels, and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment.<br> To access the accuracy of simulated 3D ocean fields, and highlight the relative benefits of mesoscale activities, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean-sea ice component of the fully coupled CMCC climate model. <br> The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling effort, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.<br> </p>
A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories
<p>Dataset presented in Figures 3-7, S1 and S3 in the recently submitted AGU paper "A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories".</p>
Arctic Ocean state estimates for 2009 using the GECCO model
<p>The dataset contains the 2009 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Dataset Rodríguez-Ros et al. 2020 / Isoprene in the Southern Ocean - ISOREMS model
<p>Dataset of isoprene measurements in the Southern Ocean for the manuscript submitted to the Journal <em>Atmosphere</em>. </p> <p>Rodríguez-Ros et al. 2020 (Submitted):</p> <p><em>Distribution and drivers of marine isoprene concentration across the Southern Ocean</em></p> <p><em>%%%%%%<br> Variables' names contained in "isorems_data.csv"<br> %%%%%%</em></p> <p><em>"id" = source of the data ("peg" = PEGASO cruise, "ace" = ACE Expedition, "pml" = ANDREXII, "ooki" = Ooki et al. 2015, "hack" = Hackemberg et al. 2017)</em></p> <p><em>"solar_time" = solar time estimated with solaR package on R.</em></p> <p><em>"iso_pm" = Isoprene concentration (pM)</em></p> <p><em>"chla_fluo" = Chlorophyll-a (fluorometric)</em></p> <p><em>"chla_matchup" = Chlorophyll-a (MODIS Aqua)</em></p> <p><em>"sst_matchup" = Sea Surface Temperature (MODIS Aqua)</em></p> <p><em>"zeu_matchup" = Depth of the Euphotic Layer (MODIS Aqua)</em></p> <p><em>"poc_matchup" = Particulate Organic Carbon (MODIS Aqua)</em></p> <p><em>"pic_matchup" = Particulate Inorganic Carbon (MODIS Aqua)</em></p> <p><em>"mld_matchup" = Mixing Layer Depth (Holte et al. 2017)</em></p> <p><em>"par_matchup" = PAR radiation (MODIS Aqua)</em></p> <p><em>"lat" = Latitude (decimal degrees)</em></p> <p><em>"lon" = Longitude (decimal degrees)</em></p>
Data, Sensitivity of 21st-century projected ocean new production changes to idealized biogeochemical model structure
<p>Data for reproducing figures in journal article submitted to Biogeosciences in December 2020.</p> <p>Data generated from global 1-degree simulations of the CESM in an ocean-ice configuration.</p> <p>NP model by Brett. See 10.5281/zenodo.4361705 for code for NP model and to use this dataset to recreate paper figures.</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.