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.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
HANZE v2.0 exposure model input data
<p>This dataset provides all input data needed to run HANZE v2.0 model. The two ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" (variable "main_path" at the beginning of the file). For detailed description of the files, see the documentation provided with the code.</p>
On the formulation and implementation of extrinsic cohesive zone models with contact - data set
<p>This data set contains data relating to the paper "On the formulation and implementation of extrinsic cohesive zone models with contact", <a href="https://doi.org/10.1016/j.cma.2022.115545">https://doi.org/10.1016/j.cma.2022.115545</a> , specifically:<br> 1. the meshes used to conduct finite element analyses,<br> 2. the results of those finite element analyses (in the form of vtk files and numpy pickles), and<br> 3. some images of the meshes and the total displacement at the end of the analyses.<br> <br> The corresponding code to generate and read the data is available at https://github.com/nickcollins-craft/On-the-formulation-and-implementation-of-extrinsic-cohesive-zone-models-with-contact (which is the preferred method), or alternatively via https://doi.org/10.5281/zenodo.6939391.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Data set used in glacier algae and filamentous cyanobacteria models
<p>This is a data set for using the glacier algae and filamentous cyanobacteria models (Onuma et al., 2022). The content is as below.</p> <p>- data: observed data (bio-volume, cell count, mineral weight, EC, pH and meteorological conditions) on the bare ice surface in Qaanaaq Ice Cap (CSV files). And, model input and output data (CSV files). About the detailed information on each file, please see the readme files.</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the glacier algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel<br> <br> The article regarding the models is as below.<br> https://doi.org/10.1017/jog.2022.76</p>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Accurately modeling biased random walks on weighted networks using node2vec+ - Additional data
<p>Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks</p>
Data, scripts and model output to perform spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea
<p>This archive contains the input data, R scripts and final results of a mechanistic model that uses near real-time data from the Belgian Part of the North Sea (2011-2017) to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. Input data are zooplankton and phytoplankton abundances, nutrients, Sea Surface Temperature (SST), photosynthetically active radiation (PAR); from the LifeWatch data and infrastructure, funded by Research Foundation - Flanders (FWO). Water temperature data for one of the locations was obtained from Flemish Banks Monitoring Network at https://meetnetvlaamsebanken.be/. The R scripts are presented in a R Markdown file that can be executed in the Blue-Cloud Zoo and Phytoplankton EOV products Vlab at https://blue-cloud.d4science.org/web/zoo-phytoplankton_eov, operated by D4Science.org, www.d4science.org (Assante et al., 2019). </p>
StarDist Adipocyte Segmentation Training data, Training Notebook and Model
<p>Data from H&E human bone marrow whole slide scanner images used in the paper: "MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology" (https://doi.org/10.21203/rs.3.rs-1860140/v1)</p> <p> </p> <p>292 image patches</p> <p>Ground truth were manually annotated using QuPath and split into 263 images for training and 29 for validation.</p> <p>Training in StarDist was done on a Windows 10 PC with an RTX 2080 GPU. The requirements file for installing a Python 3.7 environment to run the attached notebooks is provided (<strong>stardist-val.txt</strong>).</p> <p>The StarDist model configuration can be found in the Jupyter Notebook :</p> <pre><code>Adipocyte Training.ipynb</code></pre> <p>Model validation and metrics can be performed by running the notebook after finishing the <strong>Adipocyte Training</strong> notebook.</p> <pre><code>Quality Control.ipynb</code></pre> <p> </p>
Data and Models for "Probabilistic Imaging of Tsunamigenic Seafloor Deformation During the 2011 Tohoku-oki Earthquake"
<p><strong>Directory "waveform_data"</strong> includes 13 tsunami time series data from different instruments: TM1, TM2, KPG1, KPG2, GB801, GB802, GB803, GB804, GB806, GB807, D21401, D21413, and D21418. Each data file (*.dat) has two columns for (1) the time since earthquake initiation (min) and (2) ocean surface or seafloor displacement amplitude (m).</p> <p><strong>Directory “kin_models”</strong> includes the following:</p> <p>1. Seafloor Mesh Geometry</p> <ul> <li>The entire seafloor mesh consists of two separate parts (422 and 136 nodes each; 558 in total) due to the need to resolve potential discontinuity at the trench. The files “*Pt{1,2}-SM2.PointCoord.txt” consists of six columns for the ID, longitude (deg), latitude (deg), East (km), North (km) and depth (km) of the nodes in each triangular mesh. The E/N coordinates are calculated in UTM projection system, relative to an arbitrary reference point.</li> <li>The files “*.ClipPath.txt” includes the ID/lon/lat of mesh boundary nodes, which can be used for plotting.</li> <li>Visualization of the mesh parts are provided in PDF files.</li> <li>The file “*Total-SM2.PointCoord.txt” excludes boundary nodes and contains seafloor locations (504 nodes) that are directly used in tsunami arrival time calculations.</li> </ul> <p>2. Posterior Mean Models</p> <ul> <li>Ensemble-averaged models of seafloor displacements and uncertainty estimates, with no spatial averaging (“0R” in the file name) or with one-ring spatial averaging (“1R”). These models are shown in Figures 5 and 6 of <em>Jiang and Simons</em> (2016). The data files “posterior_mean_{0,1}R.txt” have three columns for (1) vertical seafloor displacement (m), (2) one-sigma standard deviation of displacement (m), and (3) corresponding resolution length (km). The model values (558 rows) correspond to nodes in files “*Pt{1,2}-SM2.PointCoord.txt” concatenated in sequential order.</li> <li>Tsunami arrival times (in sec) are calculated from the posterior mean values of propagation speeds, with zero sec at the earthquake epicenter. The coordinates (508 nodes) are included in geometry file “*Total-SM2.PointCoord.txt.”</li> </ul> <p><strong>Directory “kin_ensemble”</strong> includes the entire posterior model ensemble (98304 samples) in HDF5 format. Using a Linux command <em>h5dump</em> will show the following information about the contained datasets, with their names and dimensions. The main datasets are: (1) Covariance (1008×1008); (2) Data Log-likelihood (98304×1); (3) Posterior Log-likelihood (98304×1); and (4) Sample Set (98304×1008). Each model has 1008 parameters (504 for displacement and 504 for propagation speeds). The source coordinates (504 nodes) are included in geometry file "*Total-SM2-Parameter.PointCoord.txt."</p> <p><strong>Note:</strong> three different geometries files above are used for (1) posterior mean displacements (558 nodes), (2) arrival time calculation (508 nodes), and (3) source inversion models (504 nodes). </p>
Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort.</p> <p>The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p><strong>Note this data should be used only for non-commercial purposes.</strong></p>
Data associated to: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors
<p>Data associated to the manuscript entitled: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
OpenFOAM cases of the paper "CFD modeling of pressure drop through an OCP server for data center applications"
<p>This dataset contains the<em> underling data</em> for the paper "<em>CFD modeling of pressure drop through an OCP server for data center applications</em>” in Energies Journal.</p> <p>Numerical simulations are performed using open-source CFD code OpenFOAM. Features of the numerical model described in the paper can be summarized as:</p> <ul> <li> A hexahedra (hex) and split-hex mesh was created using <em>snappyHexMesh</em> utility from the STL (Stereolithography) model of the server</li> <li><em>Allrun</em> script runs steady-state simulations for different inlet flow rates given in the <em>flowrates</em> file.</li> <li><em>k-omegaSST</em> turbulence closure model is used</li> <li>Convective and diffusive fluxes are computed using second-order accurate numerical schemes.</li> <li>Resultant matrices are solved using <em>GAMG</em> and <em>smoothSolver</em> methods.</li> </ul> <p>New libraries are developed for the detection of whether the solution reaches state-state and for the calculation of average pressures at the inlet and outlet of the server. These libraries need to be download via the following link and compiled before the cases run: </p> <p><a href="https://github.com/DSTECHNO/OCPFoam">https://github.com/DSTECHNO/OCPFoam</a></p> <p><strong>active_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow flow over Leopard V3.1 model of OCP server neglecting fan blades.</p> <p><strong>inactive_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow over Leopard V3.1 model of OCP server considering fan blades.</p> <p><strong>Results_active_Server.tar.gz:</strong> Simulation results for active server.</p> <p><strong>Results_inactiveServer.tar.gz: </strong>Simulation results for inactive server.</p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
CMIP6 model vertically-integrated net primary production data
<p>Vertically-integrated net primary production (NPP) data from 12 models that participated in phase six of the Coupled Model Intercomparison Project (CMIP6). All data pulled from the Earth System Grid Federation.</p> <p>All model output was regridded onto a common, regular horizontal grid of 1x1 degrees (360 x 180) in longitude by latitude.</p> <p>Units are mol C per metre squared per second.</p> <p>Models are:</p> <ol> <li>ACCESS-ESM1-5</li> <li>CanESM5</li> <li>CESM2</li> <li>CNRM-ESM2-1</li> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> <li>IPSL-CM6A-LR</li> <li>MIROC-ES2L</li> <li>MPI-ESM1-2-HR</li> <li>MRI-ESM2-0</li> <li>NorESM2</li> <li>UKESM1-0-LL</li> </ol>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
Dynamical mean field theory data for single band Hubbard model
<p>Dynamical mean field theory (DMFT) data for the half filled repulsive single band Hubbard model on four lattices: cubic, diamond, hypercubic in <span class="math-tex">\(d=\infty\)</span>, and hyperdiamond in <span class="math-tex">\(d=\infty\)</span>.</p> <p>It is allowed for long range antiferromagnetic ordering, i.e., the self-consistency condition as seen in Eq. 97 of Georges et al., Rev. Mod. Phys. 68, 13 is used.</p> <p>The simulations are done for different temperatures between <span class="math-tex">\(\beta t = 3\)</span> and <span class="math-tex">\(\beta t = 40\)</span>.</p> <p>The interaction <span class="math-tex">\(U\)</span> is chosen in steps of 0.1 centered around the respective Mott transitions.</p> <p>Available data are</p> <ul> <li>interacting Greens function on Matsubara frequencies</li> <li>self energy on Matsubara frequencies</li> <li>double occupation</li> <li>spin up and spin down occupation</li> </ul> <p>Data is generated using triqs 1.4 and the continous time quantum Monte Carlo application 1.4, compare homepage at https://triqs.ipht.cnrs.fr</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to calculate derivatives of the double occupancy w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p> </p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square and honeycomb lattice.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>Data for the honeycomb lattice calculated for</p> <ul> <li>lattice sizes 6x6, 9x9, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>All energies are in units of the hopping parameters which is set to t=1. All simulations are done for half filling.</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to do an extrapolation of finite size and finite trotter errors and finally calculate derivatives of charge correlation functions w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>This version (v2) includes the number of bins used in each simulation and a slighlty changed python script to read the data.</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.