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1,028 results for “modelling & simulation”
Source code for dynamic models and simulations of mate sampling behavior
<p>Theory predicts that the strength of sexual selection (i.e., how well a trait predicts mating or fertilization success) should increase with population density, yet empirical support remains mixed. We explore how this discrepancy might reflect a disconnect between current theory and our understanding of the strategies individuals use to choose mates. We demonstrate that the density-dependence of sexual selection predicted by previous theory arises from the assumption that individuals automatically sample more potential mates at higher densities. We provide an updated theoretical framework for the density-dependence of sexual selection by (1) developing models that clarify the mechanisms through which density-dependent mate sampling strategies might be favored by selection and (2) using simulations to determine how sexual selection changes with population density when individuals use those strategies. We find that sexual selection may increase strongly with density if sampling strategies change adaptively in response to density-dependent sampling costs, whereas within-individual plasticity in sampling over time (e.g., due to adaptation to increasing sampling costs as the breeding season progresses) produces weaker density-dependent sexual selection. Our findings suggest that density-dependence of sexual selection depends on the ecological context in which mate sampling has evolved.</p>
Hadron Shower Simulation Data for Generative Models in Fundamental Physics
<p>Data set containing pion calorimeter showers used to train and evaluate our generative models for our Hadrons, Better, Faster, Stronger publication. Complete dataset consist of three hdf5 files:</p> <ul> <li>pion_train_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to train the models.</li> <li>pion_eval_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to evaluate the models.</li> <li>pion_eval_steps20to90.hdf5 contains showers originating form pions with discrete energies ranging form 20 GeV to 90 GeV in steps of 10 GeV. This set was used to evaluate the models.</li> </ul> <p>Each file contains a group called 'hcal_only'. This group has two dataset, 'energy' which contains the energy of the pions in GeV and 'layers' which contains the shower images, projected onto a 48x48x48 grid. For our model training this was reduced to 48x25x25 via slicing. The entries correspond to energy depositions in MeV.</p>
A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control
<p>An exoskeleton, a wearable device, was designed based on the user's physical and cognitive interactions. The control of the exoskeleton used biomedical signals reflecting user intention as input and its algorithm calculated an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between subject and exoskeleton. Here we propose an EMG-based single-joint exoskeleton system, merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models by rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported effective and accurate performance of an exoskeleton controlled by EMG signals.</p>
Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
<p>Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies -- individual animals observed at specific locations and times -- can provide a rich source of information about how these processes relate to demographic rates. We developed SCR models that integrate complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using Bayesian analysis methods. We evaluated their performance through a simulation study, where we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T=25 to T=15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but movement models developed here could be extended to open SCR models. The movement process models could also be extended to accommodate additional "building blocks'' of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.</p>
Models and simulation results of "A relatively dry mantle transition zone revealed by geomagnetic diurnal variations"
<p>Data and models used in Zhang et al. 2022: A relatively dry mantle transition zone revealed by geomagnetic diurnal variations. See ReadMe for more detailed description of file contents. Files includes:</p> <p>1. 3D Conductivity models (ascii) and corresponding perturbed magnetic field on the surface (Matalab Mat format):</p> <p>Global*Model, Secondary*Hs_1cpd.mat</p> <p>2. inhomogeneous surface conductance layer for thin-sheet modeling (Matlab Mat format):</p> <p>SurfaceCond1degx1deg.mat</p> <p>3. 1D inversion profiles to generate Figure 1 (Matalab Mat format)</p> <p>depth.mat, MultiMode*.mat</p> <p>4. PCA used for the data analysis (Matalab Mat format). </p> <p>Data_1-4cpd_Mode_1-5.mat</p> <p>5. Source estimates results used for the preferred conductivity model (Matalab Mat format).</p> <p>SphereCoeff_PerferedSource.mat</p> <p> </p>
GAIA model simulate data of doubled CO2 (Forces, advections, and winds)
<p>This dataset contains forces, advections, and winds output from the GAIA model, that are related to the Figures in the paper. The forces and advections are divided by the Coriolis parameter or a zonal mean absolute vorticity. </p>
CryoGridLite: Model output of pan-Arctic simulations at 1° resolution from 1700 to 2020
<p><strong>CryoGridLite</strong> is a lightweight version of the more complex and process-rich <strong>CryoGrid Community model</strong> (<a href="https://gmd.copernicus.org/preprints/gmd-2022-127/">Westermann et al. 2022</a>).</p> <p>This archive contains the following output datasets which were produced by the model.</p> <p><code>./SETUP.zip</code> The parameters varied in the ensemble simulations.<br> <code>./RESULTS.zip</code> The output variables. An overview of the output variables is provided below and in the README.md file of the archived code.</p> <p>The model code can be found here: <a href="https://zenodo.org/record/6619537">10.5281/zenodo.6619537</a></p> <p>Input data required for pan-Arctic simulations can be found here: <a href="https://zenodo.org/record/6619212">10.5281/zenodo.6619212</a></p> <table> <thead> <tr> <th>Variable</th> <th>Coordinates</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><strong>H_lat_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total latent heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_sen_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total sensible heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_tot_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_lat_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total latent heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>H_sen_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total sensible heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>H_tot_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>ThawedGround_10m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Maximum vertically integrated depth of thawed ground (T>0°C) in the uppermost 10m of the ground column [m]</td> </tr> <tr> <td><strong>T_av_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Mean annual ground temperature [°C]</td> </tr> <tr> <td><strong>T_min_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Minimum annual ground temperature [°C]</td> </tr> <tr> <td><strong>T_max_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Maximum annual ground temperature [°C]</td> </tr> <tr> <td><strong>LandArea</strong></td> <td><em>(latitude, longitude)</em></td> <td>Total area covered by land within the model grid cell [km²]</td> </tr> </tbody> </table>
Upscaling tracer-aided ecohydrological EcH2O-iso model in larger catchments: model setup and model simulations in the Selke catchment, central Germany
<p>This data repository is associated with the scientific article "Upscaling Tracer-aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization" by Yang et al. (submitted to Water Resources Research).</p> <p>This dataset includes the model setup information of the EcH2O-iso model in the Selke catchment, central Germany (./model_setup_Selke), and all model simulations and data analyses that are necessary to rebuilt the work (./model_sim_data).</p> <p>Please also refer to https://github.com/XYang-EcoHydroWQ/EcH2O-iso_largescale for corresponding model source code of the EcH2O-iso model, including modifications for larger-scale modeling.</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Abiotic radiocarbon simulated in NEMO ocean model
<p>Following the Ocean Model Intercomparison Project (Orr et al., 1999, 2017), abiotic radiocarbon is simulated using the global ocean circulation model Nucleus for European Modelling of the Ocean (NEMO) version 3.6 (Madec, 2014), which is coupled to the Los Alamos sea-ice model (CICE, Rae et al., 2015). </p> <p>Simulation one, "Hist", was forced with inter-annually varying JRA-55-do atmospheric reanalysis data (version 1.5, Tsujino et al., 2018), which is available for the period 1958 through 2020 at 3-hour intervals, which is cycled over 1850 to 2020; and with historical atmospheric CO2 and ∆14C boundary conditions.</p> <p>The other simulation (”Hist-NYF”) was forced with the JRA-NY atmospheric data in every year, which is a one year chunk (01-May-1990 to 30-April-1991) from JRA-55-do (version 1.3) data when major climate modes such as the North Atlantic Oscillation, Southern Oscillation and Southern Annular Mode were largely neutral. </p> <p>The "Fix" simulation ran with fixed atmospheric CO2 and ∆14 C boundary conditions, using 1850 values and inter-annually varying JRA-55-do atmospheric reanalysis data.</p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
ModelE simulation output used in the study "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size" in Journal of Climate (2024)
<p>The included files are the GISS ModelE output needed to replicate the figures in McGraw et al 2023, "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size"</p> <p>Most of the data herein is output from GISS ModelE2.2 simulations that did not include interactive aerosol microphysics and chemistry. Instead, aerosol extinction and effective radius were input into the model from scaled Easy Volcanic Aerosol [Toohey et al, GMD 2016] output, as described in this study's Methods section. To calculate volcanic temperature impacts and forcings at combinations of injected sulfur mass and peak effective radius (Reff) that were not simulated, we used 2D linear interpolation with the scipy function 'Rbf'.</p> <p>Separately included is output from GISS ModelE2.1 with MATRIX interactive aerosol microphysics and chemistry [Bauer et al, ACP 2008]. Note that the injections were scaled to match that a 6.5 Tg sulfur (S) injection in ModelE2.1/MATRIX best replicated the aerosol optical depth (AOD) and effective radius observations of the 1991 Pinatubo event despite this injection being most commonly considered an 9 Tg S injection. Hence, to produce the 1000 Tg S eruption, a 722 Tg S injected was simulated. Such a mismatch has been found in other GCMs (eg Mills et al, JGRA 2016) and may be due to aerosol quick-removal processes not represented in these models.</p> <p>Please note that simulated eruption masses are in this dataset listed in units of Tg S, but in the publication are in Tg SO2 (Tg S x 2).</p> <p>Data from other modeling studies included in Fig. 1 and tree ring estimates in Figs. S2 & S4 can be found within the cited studies.</p> <p>For additional information, please contact zachary.mcgraw@columbia.edu</p>
Simulation data results from SEAMANCORE model
<p>This dataset contains simulation data from the SEAMANCORE model for a region in the Indo-Pacific.</p> <p>Four different management alternatives were examined with 1100 model runs each to account for parameter uncertainty.</p> <p>This dataset was created by running the SEAMANCORE model (https://doi.org/10.5281/zenodo.7155783) with the different input parameters (input_parameters.zip). </p> <p>The results (SEAMANCORE.zip) are daily time series of proportion of benthos groups, biomass of fish functional groups, and fished biomass in the investigated area over 2280 days (~6 years) for the four different management alternatives and 1100 parameter specifications each.</p>
[3/3] Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation
<p>Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation</p>
[2/3] Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation
<p>Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation</p>
[1/3] Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation
<p>Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
Robustness assessment via simulation-based fault injection of the implementation level models of the LEON3, MC8051, and PIC microcontrollers in presence of stuck-at, bit-flip, pulse, and delay fault models
<p>This dataset package contains the results of fault injection experiments for RTL and implementation-level models 3 microprocessors (LEON3, MC8051, PIC).</p> <p><strong> 1. Package contents:</strong><br> - raw traces of fault injection experiments: *.lst files<br> - analysis results grouped by fault models: *.html files in the /REPORT subfolders<br> - summary for each targeted HDL model and fault model: index.html in each design folder</p> <p>To facilitate the navigation through the contents, it is organized as a tree of *.html pages. The root page is 'index.html' in the archive root.<br> Additionally, to observe the raw traces for each experiment in the convenient form, the *.lst files are processed on the fly by custom python-script, returning the interactive *.html page. Each observation trace is a table, where: <br> - each row represents an observation vector, comprising {simulation time stamp}, {flags}, {internal state}, {outputs}.<br> - each cell is highlighted:<br> a) green if matches with reference trace (fault-free simulation),<br> b) red if mismatches with reference trace, denoting error for internals / failure for outputs,<br> c) violet in case of vector whose timestamp was not in reference (unexpected transition).<br> These highlighting options can be customized by modifying the linked *.css files.</p> <p> <br> <strong> 2. Installation</strong><br> 2.1 Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p>2.2 Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise install the relevant 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.<br> <br> 2.2 Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the resulting line:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>2.3 Unpack the *.zip package into the folder on the Web Server. For instance 'Web-server root folder'/ExperimentalResults.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>2.4 In the web-browser navigate to the root directory of extracted package:<br> http://localhost/ExperimentalResults/index.html</p> <p> </p> <p><strong>3. How to read the contents</strong><br> The root page contains links to different analysis reports, for each HDL design under study and considered fault models. <br> The pages on the first tree level, represent the summary for each injection campaign, describing the rate of failure modes, number of experiments, latencies, and supplementary info. <br> The pages on the second tree level are the detailed analysis reports for each experiment, detailing the fault target, parameters of injected fault, detected failure mode, number of errors, etc. <br> The cells of the first column are highlighted a) in green if injection did not cause the failure, b) in red otherwise. The links in this first column navigate to the detailed traces for each experiment. <br> The latter requires that Web-server is configured to execute the python-scripts (see section 2 - Installation); otherwise the raw traces (*.lst files in ./results folders) can be observed by any text editor (notepad++, etc.).<br> </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.