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2,152 results for “outputs”
Nimble Output for MCMC Estimation of Source Apportionment; Accompaniment for Journal Article
<p>Paper <em>A dependent Bayesian Dirichlet Process model for source apportionment of particle number size distribution</em> contributed to the <em>Environmetrics</em> Special Issue on <em>Environmental Data Science</em>. In this paper, a fairly extensive Bayesian model is run using the <em>Nimble</em> probabilistic programming language. To allow for user reproducibility, the output of this model (full, not summary), is provided here.</p>
SIMSI-Transfer: SIMSI and MaRaCluster output files
<p>Dataset 2 for the publication "SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering".</p> <p> </p> <p>This dataset contains all output files produced by SIMSI-Transfer, including the intermediate MaRaCluster output files.</p>
SIMSI-Transfer: MaxQuant output files
<p>Dataset 1 for the publication "SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering".</p> <p> </p> <p>This dataset contains all files generated by MaxQuant from database searches of the studies used for assessment of SIMSI-Transfer in the publication.</p>
UCIS4EQ: sample outputs
<p>ChEESE PD1 workflow, Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) performs complete end-to-end runs, from classifying an earthquake as an urgent computing candidate, through pre-processing, submitting HPC simulations, all the way to post-processing. The workflow is still in development and accessible only to developers, but here we present a sample output of the UCIS4EQ end-to-end run with Salvus (https://mondaic.com/) with maximum frequency at 5 Hz. With this run we were reproducing an alert for the Samos-Izmir October 30th, 2020 earthquake. Following a manual trigger of this past event, the workflow performed a set of 66 simulations that have then been post-processed together to generate a single synthetic estimate of the ground motion proxies that is uploaded automatically to B2DROP after the execution is finished. The files including the estimates are published here in netCDF format along with a short README about the content.</p>
Europe1k evaluation output data
<p>This repository contains the evaluation output from the PCR-GLOWB model runs over Europe at 1 km, 10 km, and 50 km. The data is hence not the original output data which is too large in its entirety. The here available data is the result of the model output evaluation and contains metrics per water province. </p>
Modeling output for "The dynamic atmospheric and aeolian environment of Jezero crater, Mars"
<p>This dataset contains meso- and microscale numerical modeling output supporting the findings presented in the paper, "Newman et al., The dynamic atmospheric and aeolian environment of Jezero crater, Mars, Science Advances"</p>
UAV outputs and associated field measurement of the herbaceous of a Sahelian Rangeland during the wet season in Northern Senegal
<p>This dataset contains UAV outputs (mosaic and digital surface model) and field measurement of vegetation (shapefile) that were made in northern Senegal.</p> <p><strong>Site gradient measurement</strong></p> <p>The data was collected on a plot of the Centre of Zootechnical Researches of Dahra / ISRA during 2020 rainy season (from July 19, 2020, to September 17, 2020). The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1. The vegetation in the field is a herbaceous savannah where <em>Vachellia tortilis</em> and <em>Balanites aegyptiaca</em> are the dominant trees.</p> <p><strong>Field measurement.</strong></p> <p><strong>UAV flight plan</strong></p> <p>We used two different drones : Bluegrass and Anafi of Parrot. The Bluegrass of Parrot was used from 19/07/2020 to 04/08/2020. The Bluegrass flights were done at 60 meters of altitude, with a speed of 2 m s<sup>-1</sup>, and 90% of overlap rate between images, on a double grid of 100 m x 100 m. Anafi of Parrot was used for the rest of the season. The Anafi flights were done at 60 meters of altitude, with a speed of 2 m s<sup>-1</sup>, and 90% of overlap rate between images, on a double grid of 100 m x 100 m and the angle of inclination of the camera fixed at 80°. The flights have been done with PIX4D capture application at earlier in the day every two days. A total of 61 drone flights were conducted over the rainy season.</p> <p><strong>Herbaceous Biomass</strong></p> <p>Every two days , after drone flight, herbaceous measurements were carried out, in three plots of 1 m² distributed respectively under the crown of a tree, at the edge of the crown, and at a distance from the edge of the crown equal to the height of the tree. These plots were rotated among the trees in the field until all four azimuths of trees were covered.We collected Fresh mass and dry mass.</p> <p><strong>Image analysis.</strong></p> <p>The drone images taken for each day of collect, were analyzed in the software PIX4DMapper (Pix4D SA, Lausanne, Switzerland) by the Structure from Motion method. We used precisely the 3D mapping option of the software. Then for each flight we computed and exported an orthophotograph and a digital surface model.</p> <p><strong>Data organization</strong></p> <p>The data contains :</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements</li> </ul>
EAMv1 simulation output from CondiDiag1.0 use case examples
<p>This archive contains data files from three EAMv1 simulations that demonstrate the usage of an online diagnostic tool called CondiDiag1.0. The three simulations are described in Section 6 in <a href="https://gmd.copernicus.org/preprints/gmd-2021-331/">Wan et al. (2022, GMDD)</a>. The model source code, run scripts, and post-processing scripts are available on Zenodo under the DOI <a href="https://doi.org/10.5281/zenodo.5530188">10.5281/zenodo.5530188</a>. EAMv1's master DOI is 10.11578/E3SM/dc.20180418.36, see <a href="https://www.osti.gov/doecode/biblio/10475">https://www.osti.gov/doecode/biblio/10475</a>.</p>
Hydrometeorological dataset and SWAT simulated outputs of Jaguari basin in the Cantareira reservoirs system
<p>Dataset used in the accepted paper "Hydrologic Impact of Climate Change in the Jaguari River in the Cantareira Reservoir System" (Domingues et al., 2022), from Water. A README.txt file is available within each folder with details of the dataset.</p>
SWMF/CIMI outputs used in Holappa and Buzulukova (2022) manuscript submitted to Geophys. Res. Lett.
<p>These data files and Python scripts are used to produce Figures 1 and 2 in Holappa and Buzulukova, Explicit IMF By-dependence of energetic protons and the ring current, submitted to Geophys. Res. Lett. (2022).</p> <p>PARAM.in.PosTilt and gitinfo.txt contain the settings and Git version references used to setup the SWMF model.</p> <p>Figure 1: PosBy_CimiFlux_n00000046_h.fls, NegBy_CimiFlux_n00000046_h.fls are CIMI output files containing proton fluxes for different energies and pitch angles. These data can be read with CIMIreader.py. Figures in the manuscript can be produced by Run_CIMI_reader.py. Files PosBy_CimiFlux_n00000046_e.fls, NegBy_CimiFlux_n00000046_e.fls can be used to produce corresponding figures for electrons.</p> <p>Figure 2: Data files CIMI_log_NegBy_PosTilt.dat and CIMI_log_PosBy_PosTilt.dat are CIMI output files containing the ring current energy and other variables. Figures can be produced by Plot_CIMI_Dst.py. </p> <p> </p>
A comprehensive dataset of the Spanish research output and its associated social media and altmetric mentions (2016-2020)
<p>Data on research publications authored by Spanish institutions between 2016 and 2020 with their associated social media and altmetric mentions, and on researchers affiliated to Spanish institutions whose work is highly mentioned in social media and non-academic outlets.</p> <p>Variables of the publications dataset:</p> <ul> <li><strong>id</strong> - Unique publication identifier</li> <li><strong>title</strong> - Full title of the publication</li> <li><strong>year</strong> - Year of publication</li> <li><strong>type</strong> - Document type</li> <li><strong>journal</strong> - Name of the journal</li> <li><strong>esi</strong> - ESI category of the publication</li> <li><strong>influscore</strong> - AAS value on March 3, 2021</li> <li><strong>news</strong> - Number of mentions in news media</li> <li><strong>blogs</strong> - Number of mentions in blogs</li> <li><strong>policy</strong> - Number of mentions in policy reports</li> <li><strong>patent</strong> - Number of mentions in patent</li> <li><strong>twitter</strong> - Number of mentions in Twitter</li> <li><strong>post_peer</strong> - Number of mentions in PubPeer and Publons</li> <li><strong>weibo</strong> - Number of mentions in Weibo</li> <li><strong>facebook</strong> - Number of mentions in Facebook</li> <li><strong>wikipedia</strong> - Number of mentions in Wikipedia</li> <li><strong>google</strong> - Number of mentions in Google+</li> <li><strong>linkedin</strong> - Number of mentions in LinkedIn</li> <li><strong>reddit</strong> - Number of mentions in Reddit</li> <li><strong>pinterest</strong> - Number of mentions in Pinterest</li> <li><strong>f1000</strong> - Number of mentions in F1000</li> <li><strong>stack_overflow</strong> - Number of mentions in Stack Overflow</li> <li><strong>youtube</strong> - Number of mentions in YouTube</li> <li><strong>syllabus</strong> - Number of mentions in Open Syllabus Project</li> </ul> <p>Variables of the top authors dataset:</p> <ul> <li><strong>name</strong> - Full name of the researcher</li> <li><strong>orcid</strong> - ORCID record</li> <li><strong>organization</strong> - Name of the institution of affiliation</li> <li><strong>publications</strong> - List of publication identifiers (id) connecting with the publications dataset</li> </ul>
Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards
<p>Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards (Advances in Statistical Climatology, Meteorology and Oceanography, May 2022)</p>
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
The model output for the hypothermia case during the 2021 Ultramarathon
<p>The datasets are WRF model outputs for the hypothermia case during the 2021 Ultramarathon. The essential datasets are selected to support the study "Simulation of the effect of small-scale mountains on weather conditions during the May 2021 Ultramarathon in Gansu Province, China".</p>
Model output for PI, MH and LIG based on AWIESM1 and AWIESM2
<p>This repository contains the AWIESM output of 3 PMIP4 simulations: pre-industrial (PI), mid-Holocene (MH) and last interglacial (LIG).</p> <p>List of variables:</p> <p>uwind => atmospheric zonal wind<br> vwind => atmospheric meridional wind<br> slp => sea level pressure<br> amoc => AMOC streamfuction<br> salinity => ocean salinity<br> temperature => ocean temperature_<br> temp2_pr_sic => surface temperature (tsurf), 2m air temperature (temp2), precipitation (aprc + aprl) and sea ice concentration (seaice)</p> <p> </p>
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
Model output data and code for Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality
<p>Model output data and code for "Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality" in Nature Communications.</p>
Supplementary Data: OpenCOVID model output underlaying Figures 1 and 2 of "Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden"
<p>Supplementary data files <strong>Figure_1.xlsx</strong> and <strong>Figure_2.xlsx</strong> contain the model simulation outcomes for Figures 1 and 2 of <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al</em></a> "<strong>Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden</strong>" (2022)</p> <ul> <li><strong>Figure 1</strong>: Peak daily hospital occupancy (number of beds per 100,000 population over the six-month simulation period) for three variant properties; infectivity (relative to Delta), immune evading capacity (%), and severity (relative to Delta)<br> </li> <li><strong>Figure 2</strong>: Percentage of COVID-19 infections and deaths averted by third-dose vaccines for adults and vaccinating 5-11-year-olds with doses one and two.<br> </li> <li>Open access source-codes of the associated plotting functions are published <a href="http://zenodo.org/record/6532404#.Yqw7cezMKdb">here</a> on Zenodo.<br> </li> <li>Open access source-codes for the OpenCOVID model of all analyses as presented in <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022)</a> are publicly available at <a href="https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src">https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src</a>.<br> </li> <li>Detailed model descriptions and model equations of individual-based transmission model <strong>OpenCOVID</strong> are described in <a href="https://pubmed.ncbi.nlm.nih.gov/34923396/">Shattock <em>et al</em>. (2022)</a> and <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022).</a></li> </ul>
RDA Data Stewardship Organisational Models Survey 2021 Output Dataset
<p>Data Stewardship comes in many forms and contexts, with the common goal of supporting data management. Yet that diversity can make it hard for the RDM community to further professionalise our work and services.</p> <p><br> The RDA Professionalising Data Stewardship Interest Group (PDS-IG) Models Task Group sought input from the research data community to help model different approaches to research data stewardship through an online survey in October to November 2021. An offline copy of the survey is available as a linked resource from this record.<br> <br> This dataset consists of a cleaned, anonymised responses from 136 respondents, though not all questions were answered by all respondents. The output are available in a CSV formatted file containing the full response set along with output from a thematic textual analysis undertaken on responses to a number of open-text questions in the survey.</p>
Model output and PTt marker data for van Agtmaal et al., 2022 (in review), Frontiers in Earth Science
<p>Model output for reproduction of key figures in the manuscript van Agtmaal et al. titled "Quantifying continental collision dynamics for Alpine-style orogens" currently under revision in Frontiers in Earth Science</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.