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1,670 results for “forcing”
Regional HYCOM output: absolute and relative wind forcing experiments
<p>Hybrid Coordinate Ocean Model (HYCOM) output from 2 forced regional simulations of the Agulhas Current. The first experiment is forced by absolute winds, the second experiment is forced by relative winds (the wind speed relative to the current speed). Data uploaded here are the sea surface height and surface u and v velocities, for both experiments. This is weekly output from January 1993- December 2013 at 1/10°. Also uploaded is a vertical section of the HYCOM output along the ACT transect in the Agulhas Current (~33.4°S at the coast and extending 300km offshore) for both experiments from 2010- 2013.</p> <p>For more information on the data please refer to "L. Braby, Backeberg, B., Krug M. and Reason C. (in prep), Quantifying the impact of wind-current feedback on mesoscale variability in forced simulation experiments of the Agulhas Current using an eddy tracking algorithm."</p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
CHARMM36M Force Field Parameters for the Thioester Bond between Cysteine and Glycine
<p>CHARMM36M force field parameters for the thioester bond connecting the side chain of a cysteine to the carbonyl of a C-terminal glycine.</p> <p>The dataset includes parameter and auxiliary files in GROMACS format. In addition, a short tutorial shows how to patch the CHARMM36M force field and use the parameters.</p> <p>These parameters have been developed for the parameterization and simulation of the Ubc6 ubiquitin complex, but can be used with any conparable system.</p> <p>Details about the parameterization are provided in:</p> <p><br><a href="https://www.embopress.org/doi/full/10.1038/s44318-024-00301-3">Swarnkar, Anuruti, Florian Leidner, Ashok K. Rout, Sofia Ainatzi, Claudia C. Schmidt, Stefan Becker, Henning Urlaub, Christian Griesinger, Helmut Grubmüller, and Alexander Stein. "Determinants of chemoselectivity in ubiquitination by the J2 family of ubiquitin-conjugating enzymes." <em>The EMBO Journal</em> (2024): 1-35.</a></p> <p> </p> <p> </p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
Maintenance of Convectively Coupled Kelvin waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing (Code and Data)
<p>This is the dataset and code for generating all figures for the journal article named "Maintenance of Convectively Coupled Kelvin Waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing," The article was written by Mu-Ting Chien and Daehyun Kim and submitted to Geophysical Research Letters in 2024.</p>
Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants
<p>Data from: Mallon, JM, KL Bildstein, and WF Fagan. 2021. Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants. Movement Ecology</p> <p>These data include only migrations and are annotated with stopovers. See metadata for full information.</p> <p>Data modified from Bildstein KL and Barber D. 2021. Movebank Data Repository. https://doi.org/10.5441/001/1.f3qt46r2.</p> <p> </p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
Triazole-Extended Anthracenes as Optical Force Probes
<p><sup>1</sup>H- and <sup>13</sup>C-NMR FID of compounds <strong>2</strong> (Figure S1-S2), <strong>3 </strong>(Figure S4-S5), and <strong>6 </strong>(Figure S7-S8) of the Supporting Information.</p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors
<p>This repository contains the data used in the corresponding study:</p> <p>Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors</p> <p><strong>Abstract</strong></p> <p>Piezoresponse force microscopy (PFM) is routinely used to probe the nanoscale electromechanical response of ferroelectric and piezoelectric materials. However, many challenges remain in the interpretation of the recovered signal. Specifically, many non-ferroelectric contributions affect the measured response, ranging from electrostatics, to charge injection and trapping, and topographic cross-talk. Recently, machine learning (ML) has been utilized to identify multiple contributors within complex data systems, such as PFM response. A substantial advancement in ML approaches for PFM techniques is offered by dimensional stacking, enabling encoding of physical and/or chemical correlations within the materials’ response across different data dimensions spanning varying ranges. However, dimensional stacking requires appropriate scaling for each dimension (before ML analysis) to minimize undesired information loss. Here, the impact of clustering globally and locally scaled parameters in polarization switching experiments via resonant PFM (RPFM) are discussed. Specifically, dimensional stacking of scaled parameters can mask or enhance ferroelectric and non-ferroelectric behaviors, and aid identification of various physical phenomena contributing to the measured RPFM response. This study highlights the importance of data curation for ML, and its role in identifying signal contributors to scanning probe microscopy (SPM)-based techniques with multidimensional data, such as resonant and/or spectroscopic SPM.</p>
Land Cover Fraction Mapping with FORCE - Supplemental Data
<p> </p> <p>This upload contains data required to replicate a <a href="https://github.com/franzschug/force/blob/develop/docs/source/howto/lcf.rst">tutorial </a>that applies regression-based unmixing of spectral-temporal metrics for sub-pixel land cover mapping with synthetically created training data. The tutorial uses the <a href="https://github.com/davidfrantz/force">Framework for Operational Radiometric Correction for Environmental monitoring</a>.</p> <p>This dataset contains intermediate and final results of the workflow described in that tutorial as well as auxiliary data such as parameter files.</p> <p>Please refer to the above mentioned tutorial for more information.</p> <p> </p>
Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean
<p>This dataset contains processed model data used in</p> <p>Tesdal, J.-E., A. MacGilchrist, G., Beadling, R. L., Griffies, S. M., Krasting, J. P., & Durack, P. J. (2023). Revisiting interior water mass responses to surface forcing changes and the subsequent effects on overturning in the Southern Ocean. Journal of Geophysical Research: Oceans, 128, e2022JC019105. <a href="https://doi.org/10.1029/2022JC019105">https://doi.org/10.1029/2022JC019105</a>.</p> <p>The above publication uses two coupled climate models (AOGCMs), GFDL-CM4 and GFDL-ESM4, to assess the impact of perturbations in wind stress and Antarctic ice sheet melting on the Southern Ocean meridional overturning circulation (SO MOC) and associated water mass transformations (WMT).</p> <p>The attached archive includes netCDF files to recreate all figures and tables in <a href="https://doi.org/10.1029/2022JC019105">Tesdal et al. (2023)</a>, including overturning streamfunction (moc), volume storage change (dVdt), surface water mass transformation (swmt), meridional volume transports (mvt) zonal mean potential density referenced to 2000 dbar (sigma2) and mixed layer depth (mld). These variables are derived from preindustrial control (piControl) and idealized perturbation runs of Antarctic melting (Antwater), wind stress (Stress), as well as the combination (Antwater-Stress) using the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) protocol.</p> <p>The FAFMIP protocol (<a href="https://doi.org/10.5194/gmd-9-3993-2016">Gregory et al., 2016</a>) involves adding perturbations to the surface fluxes that are computed within the atmosphere-ocean general circulation model (AOGCM) from the state of the system (<a href="https://doi.org/10.1029/2005JC003421">Lowe and Gregory, 2006</a>; <a href="https://doi.org/10.1088/1748-9326/9/3/034004">Bouttes and Gregory, 2014</a>). The perturbations in this dataset were technically added as a flux adjustment similar to that formerly used in AOGCMs (<a href="https://doi.org/10.1007/BF01053472">Sausen et al., 1988</a>).</p> <p>The data files contain processed model output and do not include any raw model output. Model data from the piControl runs of CM4 and ESM4 are available at the Earth System Grid Federation archive (<a href="https://esgf-node.llnl.gov/projects/cmip6">https://esgf-node.llnl.gov/projects/cmip6</a>). The forcing fields (perturbations) used in the perturbation experiments can be found at <a href="https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans">https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans</a>. Python scripts and Jupyter notebooks to reproduce the tables and figures can be accessed at <a href="https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans">https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans</a>.</p> <p><strong>Contents</strong>:</p> <ul> <li>Overturning streamfunction (moc)</li> <li>Volume storage change (dVdt)</li> <li>Surface water mass transformation (swmt)</li> <li>Meridional volume transports (mvt) </li> <li>Zonal-mean potential density referenced to 2000 dbar (sigma2)</li> <li>Mixed layer depth (mld) </li> <li>Antarctic shelf mask</li> <li>Static grid files</li> </ul> <p><strong>Models</strong>:</p> <ul> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>Preindustrial control (piControl)</li> <li>Experiment with a 0.1 Sv freshwater perturbation entering at the Antarctic coast (Antwater)</li> <li>Experiment with zonal and meridional wind stress perturbations (Stress)</li> <li>Experiment with combined perturbation of both Antarctic melting and wind stress (Antwater-Stress)</li> </ul> <p><strong>NetCDF file name structure</strong>:<br> <model>_<simulation>_<member_id>_<domain>_<time_period>_<variable>.nc</p> <ul> <li>model: CM4, ESM4</li> <li>simulation: control, antwater, stress, antwaterstress</li> <li>member_id (only for antwater, stress, antwaterstress): 251, 290, 332 (CM4), 101, 151, 201 (ESM4)</li> <li>domain: global, so</li> <li>time_period: yyyy-yyyy (first year to last year)</li> <li>variable: e.g., moc_rho2_online_lores, dVdt_rho2_online_lores, swmt_sigma2_005, sigma2_jmd95_zmean</li> </ul>
Potential of Mean Force (PMFs) for Zerovalent Iron (Fe (100-110-111)) NanoParticles
<p>The data is potential of mean force for three fcc configurations of zero valent iron, (100), (110) and (111) nano particles interacting with side chain analugos of 22 different amino acids that are the building blocks of the proteins. We used adaptive Well-Tempered Metadynamics method to calculate the adsorption free energies that has previously been described for measuring the adsorption of the biomolecules for TiO2 and Ag. GROMACS and plumed softwares were used to carry out the simulations and the CHARMM-GUI/Nanomaterial Modeler was used create the iron slabs. </p> <p>The system was solvated using the original form of TIP3 water model . It was then neutralized by NaCl regarding the charge of the whole system. In the MD calculation the energy of the system has been minimized by Verlet particle-based cut-off scheme using charge groups and steepest gradient method for 1000 steps. The system was equilibrated under constant pressure, particles and temperature (300 K) condition (NPT) using Berendsen weak coupling or 1.0 ns. The component of the pressure tensor were set to 1.0 bar. The thermostat and the barostat for the relaxation time was 1.0 ps. The system underwent another more unbiased equilibrations for 10 ns in the NVT ensemble conditions. The Nose-Hoover thermostat’s relaxation time constant for the NVT ensemble was 5 ps. The cut-off distance was set to 1.0 nm for the VdW short range interactions. In the metadynamics biased simulation, the Surface Separation Distance (SSD) describes the reaction coordinates. This SSD measures the minimum distance of the center-of-mass (COMs) of the SCA Rmol and the surface atoms ri along the z coordinate.</p>
Summary of Input from Stakeholders and other institutions involved in dynamic force applications
<p>Survey data used to create Deliverable 1 of ComTraForce project "Roadmap detailing the future requirements for improved force transfer standards and associated calibration methods for force testing machines taking into account realistic uncertainties".</p>
Frictional fluid instabilities shaped by viscous forces
<p>Original images constituting the data set used for the phase diagrams in the paper </p> <p>Frictional fluid instabilities shaped by viscous forces</p> <p>Zhang D, Campbell JM, Eriksen J, Flekkoy EG, Maloy KJ, MacMinn CW and Sandnes B.</p> <p>Accepted for publication in Nature Communications</p> <p> </p> <p>The experiments involved injection of a viscous mixture (water/glycerol) into dry hydrophobic grains in a Hele-Shaw cell. The cell gap was 0.9 mm, and the outer radius shown in the images is 13.4 cm. The experimental variables were: granular material filling fraction (phi), injection rate (volumetric) and viscosity of the injected fluid. The images correspond to Fig. 2, Fig. 8 and Fig. 9 in the paper, with simulation output corresponding to the experiments included in Fig. 2 and 8.</p> <p> </p> <p>The File names include the experimental/simulation variables. For example:</p> <p>Fig2_exp_phi042_rate1_visc1.jpg</p> <p>- Belongs to Fig 2 in the paper</p> <p>- Is an experimental image</p> <p>- The filling fraction was phi = 0.42</p> <p>- The injection rate was 1 mL/min</p> <p>The viscosity of the injected fluid was 1 mPs s (i.e. water)</p> <p> </p> <p>The experiments and simulations are described in detail in the Zhang et al. paper.</p> <p> </p>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
TUK-FFDat - Data scheme and data format for transferable force fields for molecular simulation
<p>Online repository to suplement the following publication:</p> <p>G. Kanagalingam, S. Schmitt, F. Fleckenstein, S. Stephan: Data scheme and data format for transferable force fields for molecular simulation, Scientific Data, accepted (2023).</p>
Data for Figures 4, S2, S7-9 and emission data in the Publication "Enhanced Light Absorption and Radiative Forcing by Black Carbon Agglomerates"
<p>This repository contains the data to produce Figure 4, S2, S7-9 and emission data for the paper:</p> <p>"Kelesidis, G. A., Neubauer, D., Fan, L.-S., Lohmann, U., & Pratsinis, S. E. (2022). Enhanced light absorption and radiative forcing by black carbon agglomerates. <em>Environmental Science and Technology</em>, 56(12), 8610– 8618. <a href="https://doi.org/10.1021/acs.est.2c00428">https://doi.org/10.1021/acs.est.2c00428</a> "</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8167401)</p>
Synth-Forc Loop Data.
<p>This is an sqlite-lite binary file containing room temperature magnetic hysteresis loops and minor loops for a set of first order reversal curves (FORC)s. The data is generated using MERRILL (https://www.rockmag.org). It can be viewed using the command line sqlite (see https://www.sqlite.org). The file contains a single table, called 'all_loops' with the following fields:</p> <p>id - a unique integer id for each row in the</p> <p>geometry - a unique name of a geometry in this case 'oblate' and 'prolate' indicating an oblate truncated tetrahedron and a prolate truncated tetrahedron.</p> <p>temperature - the temperature at which micromagnetic models were run in order to generate the data.</p> <p>aspect_ratio - the aspect ratio of the geometries.</p> <p>size - the size of each geometry - in nanometre, using a convention of equivalend spherical volume diameter.</p> <p>Br - the reversal field of a minor loop.</p> <p>B - the applied applied field.</p> <p>M - the magnetisation that results from applying B (starting from Br).</p> <p>SatMag - the saturation magnetisation value of magnetite at the given temperature.</p> <p>In order for the file to be consistent, it is recommended that each field step B is uniform across all geometries, sizes and aspect ratios.</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.