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.
617
datasets available to search
ShareScore release 0.7.1
Dataset results
617 results for “Climate models”
Occurrence records used to develop a climatic suitability model for emerald ash borer in DDRP
<p>Presence records used to calibrate and validate a climatic suitability model for emerald ash borer in the DDRP platform (Degree-Days, Risk, and Phenological event mapping) (Barker et al. 2023). The first sheet ("Records") of the Excel file provides the range (native or invaded), continent, country, state or province, locality, latitude, and longitude of origin for each record. The "Coords_est" column indicates whether the coordinates were estimated from city- or county-level information (1 = yes, 0 = no). The year in which the record was collected is provided if known. The second sheet of the Excel file ("References") provides a list of references for each record source.</p>
AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea
<p>The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria. </p> <p> </p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Asymmetry of AMOC Hysteresis in a State-of-the-Art Global Climate Model
<p>These directories contain Python (v3) scripts for plotting/analysing model output.</p> <p>Python scripts can be found in the directory 'Program'. Model output can be found in the directory 'Data'.</p> <p>The processed model output are stored as NETCDF files and using the relevant scripts one can regenerate all the figures. We provided the original model output (native grid) and is only converted to yearly-averaged data (due to storage limitations). Some scripts (e.g., FOV_index.py and AMOC_transport.py) use the original model output and running these script generates in the time series, which are presented in the manuscript.</p>
Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin , Alaska, 1921 to 2100.
Output data set of the MBL-GEM III model run for tussock tundra in the Kuparuk River Basin, Alaska, described in detail in Le Dizès, S., B. L. Kwiatkowski, E. B. Rastetter, A. Hope, J. E. Hobbie, D. Stow, and S. Daeschner, Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin (Alaska), J. Geophys. Res., 108(D2), 8165, doi:10.1029/2001JD000960, 2003. We ran the model at a 10 km x 10 km resolution for 123 cells at a yearly time step for 180 years, from 1921 to 2100. Two scenarios enabled the investigation of the effects of two opposing climate change scenarios for the 2001-2100 future period: warmer and wetter ("wet scenario" or Scenario 1) and warmer and drier ("dry scenario" or Scenario 2). These 246 files contain all simulation results for each scenario for individual cells in the Kuparuk River basin.
Assessment of the Vulnerability of Permafrost Carbon to Climate Change: A Sensitivity Analysis among Models
This activity is a comparison of how large-scale models represent permafrost carbon dynamics into the future (2010-2299). Model responses were evaluated at several temporal scales. To the extent possible, we standardized driver data and simulation procedures among the models. However, the protocol has been set up so that each model can build upon the procedures used to produce the outputs for historical analysis (1960- 2009) that was published in McGuire et al. 2016 (Global Biogeochemical Cycles 30:1015-1037, doi:10.1002/2016GB005405). Note that this comparison is an offline model comparison in which we assessed the sensitivity of the responses of the models to somewhat standardized forcing data. The activity compared among the models: Carbon dynamics: Predictions of average annual C fluxes (GPP, NPP, RH, CH4 fluxes, disturbance-related emissions, dissolved organic carbon export, lateral land used fluxes, etc.) and major pools for the northern permafrost region for the 2010-2299 period. Soil thermal dynamics: Predictions of annual soil thermal and hydrological dynamics at prescribed depths and the maximum annual active layer depth (in permafrost locations) for the 2010-2299 time period. The spatial simulation data for this project are are available through the National Snow and Ice Data Center (doi: 10.5067/ZRL5WJKN01XM).
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
Database and model code for "Material efficiency and climate change mitigation of passenger vehicles"
<p>This record provides all data points and model code necessary to compute the results presented in P. Wolfram, Q. Tu, N. Heeren, S. Pauliuk, E. Hertwich (2020) "Material efficiency and climate change mitigation of passenger vehicles", published in Journal of Industrial Ecology. All data is described in section 2 of the manuscript. The code can be run in MATLAB. </p>
Reply to comment on "High-resolution, multi-layer modelling of Singapore's urban climate incorporating local climate zones"
<p>This data is for the publication submitted to the Journal of Geophysical Research Atmospheres</p>
Data and code for: Acute heat priming promotes short-term climate resilience of early life stages in a model sea anemone
<p>Across diverse taxa, sublethal exposure to abiotic stressors early in life can lead to benefits such as increased stress tolerance upon repeat exposure. This phenomenon, known as hormetic priming, is largely unexplored in early life stages of marine invertebrates, which are increasingly threatened by anthropogenic climate change. To investigate this phenomenon, larvae of the sea anemone and model marine invertebrate <em>Nematostella vectensis</em> were exposed to control (18°C) or elevated (24°C, 30°C, 35°C, or 39°C) temperatures for 1 hour at 3 days post-fertilization (DPF), followed by return to control temperatures (18°C). The animals were then assessed for growth, development, metabolic rates, and heat tolerance at 4, 7, and 11 DPF. Priming at intermediately elevated temperatures (24°C, 30°C, or 35°C) augmented growth and development compared to controls or priming at 39°C. Indeed, priming at 39°C hampered developmental progression, with around 40% of larvae still in the planula stage at 11 DPF, in contrast to 0% for all other groups. Total protein content, a proxy for biomass, and respiration rates were not significantly affected by priming, suggesting metabolic resilience. Heat tolerance was quantified with acute heat stress exposures, and was significantly higher for animals primed at intermediate temperatures (24°C, 30°C, or 35°C) compared to controls or those primed at 39°C at all time points. To investigate a possible molecular mechanism for observed changes in heat tolerance, the expression of heat shock protein 70 (HSP70) was quantified at 11 DPF. Expression of HSP70 significantly increased with increasing priming temperature, with the presence of a doublet band for larvae primed at 39°C, suggesting persistent negative effects of priming on protein homeostasis. Interestingly, primed larvae in a second cohort cultured to 6 weeks post-fertilization continued to display hormetic growth responses, whereas benefits for heat tolerance were lost; in contrast, negative effects of short-term exposure to extreme heat stress (39°C) persisted. These results demonstrate that some dose-dependent effects of priming waned over time while others persisted, resulting in heterogeneity in organismal performance across ontogeny following priming. Overall, these findings suggest that heat priming may augment the climate resilience of marine invertebrate early life stages via the modulation of key developmental and physiological phenotypes, while also affirming the need to limit further anthropogenic ocean warming.</p>
Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"
<p>Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5)</p><p> </p><p>We use bash, CDO, and python.</p><p>SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.</p><p>Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.</p><p>Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.</p><p>If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp</p><p> </p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 1
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects
<p><strong>Premise </strong></p> <p>Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO<sub>2</sub> exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO<sub>2</sub> exchange rates of these poikilohydric organisms change through time as a function of weather conditions.</p> <p><strong>Methods</strong></p> <p>PoiCarb simulates diel fluctuations of CO<sub>2</sub> exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO<sub>2</sub>-exchange responses to light, temperature, atmospheric CO<sub>2</sub> concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO<sub>2</sub> exchange to such courses from field measurements on the tropical lichen <em>Crocodia aurata</em>. To demonstrate the model's usefulness, we simulated potential climate-change effects.</p> <p><strong>Results </strong></p> <p>Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.</p> <p><strong>Conclusions</strong></p> <p>Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"
<p>These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ". </p> <p>(1) AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC. </p> <p>(2) BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22.</p> <p>(3) BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs.</p> <p>(4) BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies.</p> <p>(5) BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies.</p> <p>All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here.</p> <p><strong>Note:</strong> AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019).</p> <p><strong>References</strong></p> <p>d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. <em>Journal of Applied Meteorology and Climatology</em>,<em> 22</em>(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. <em>Atmospheric Chemistry and Physics</em>,<em> 19</em>(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. <em>Atmospheric Chemistry and Physics</em>,<em> 17</em>(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017</p> <p>Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287</p>
Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"
<p>This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics. <br>It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content. <br>A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past. <br>Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis. </p>
Dataset for 'Global declines in net primary production underestimated by climate models'
<p>The dataset provided here is to be used in conjuction with the JuPyTer notebook provided here: https://github.com/tjryankeogh/global_npp_trends/tree/main</p> <p> </p> <p>Download the file and uncompress in a root directory where there is a folder 'FIGURES'. When running the notebook make sure to change this root directory when importing packages.</p> <p> </p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Data and scripts for the publication "A case for open communication of bugs in climate models"
<p>Primary data and scripts for the publication "A case for open communication of bugs in climate models" (submitted to GMDD as EGUSPHERE-2024-3493)</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.