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451 results for “Elasticity”
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 3)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_50_to_99.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 50 to 99 with 1 time step resolution</p> <p> </p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 2)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_1_to_49.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 1 to 49 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 4)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_100_to_170.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 100 to 170 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX3D)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>0Synthesis.zip --- Microstructure synthesis<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds and MicrostructureDiagnostics.uds meta data for the grains<br> -parameters.xml parameterization of microstructure synthesis<br> -Scripts and additional input referred to by parameters.xml</p> <p>1Coarsening.zip --- Simulation<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds meta data for the grains<br> -VoxelizedParameters.xml parameterization of coarsening simulation<br> -NrGrains&EnergyStatistics.dat descriptive stats simulation (Time step, Real time, number of grains, ---, grid size)<br> -Network.zip implicit 3d x,y,z unsigned int ID container detailing the microstructure</p> <p>Texture_Faces_1_to_1261.zip<br> Grain boundary network geometry and grain meta data tracking data</p> <p>Network.zip<br> Rendering images for evolution of the grain boundary network and volume properties</p> <p>2DataAnalysis --- Data analyses<br> -All other archive Matlab analysis scripts and TopologyTracer results<br> </p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 1)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>0Synthesis.zip --- Microstructure synthesis<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds and MicrostructureDiagnostics.uds meta data for the grains<br> -parameters.xml parameterization of microstructure synthesis<br> -Scripts and additional input referred to by parameters.xml</p> <p>1Coarsening.zip --- Simulation<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds meta data for the grains<br> -VoxelizedParameters.xml parameterization of coarsening simulation<br> -NrGrains&EnergyStatistics.dat descriptive stats simulation (Time step, Real time, number of grains, ---, grid size)<br> -Network.zip implicit 3d x,y,z unsigned int ID container detailing the microstructure</p> <p>1CoarseningTrackCurvature.zip<br> -Files of the re-run to the simulation along which capillary data were tracked on the fly in each time step</p> <p>GBContourPoints_100_to_5296.zip<br> -Grain boundary face network for time steps 100 to 5296 at 100 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 8)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>GBCurvApprx_1_to_174.zip<br> Single-grain-boundary-contour-point-resolved curvature estimation tracking data for time step 1 to 174 with 1 time step resolution and all grains</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 10)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>NetworkEvolutionPNG.zip<br> Rendered images of the evolving microstructure at very high resolution and reduced size FullHD</p> <p>PRX2DNetwork.gif and PRX2DNetwork.gifx<br> Video of the evolving microstructure and corresponding gif-x video generation file</p> <p>TrackingParallelRXEVO_Resolution_1.zip<br> TopologyTracer analysis of recrystallized volume fraction over time</p> <p>TrackingParallelSEE1002000.zip<br> TopologyTracer analysis of single-grain-resolved stored elastic energy driving forces segment length averaged</p> <p>TrackingParallelTrackingBK_Resolution_1.zip,<br> TrackingParallelTrackingBK_Resolution_100.zip<br> TopologyTracer analysis tracking backwards (BK) in time the evolution of the successful grains with different temporal resolution</p> <p>TrackingParallelTrackingFW_FID100.zip<br> TopologyTracer analysis tracking forward (FW) in time the metadata for all grains at time step 100</p> <p>TrackingParallelTrackingFW_Resolution_1.zip,<br> TrackingParallelTrackingFW_Resolution_20.zip,<br> TrackingParallelTrackingFW_Resolution_100.zip<br> TopologyTracer analysis tracking forward (FW) in time the evolution of all grains with different temporal resolution</p> <p>MPIEDevBranchPRX2D.zip<br> TopologyTracer analysis rendering of images and analysis scripts</p> <p>MPIEDevBranchPRX2DCapTrack.zip,<br> MPIEDevBranchPRX2DMajorRevPCA.zip,<br> MPIEDevBranchTrackSuccessful02.zip<br> TopologyTracer analysis of capillary tracking data in correlation with size and topology evolution and PCA</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 6)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_260_to_449.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 260 to 449 with 1 time step resolution</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: 2D segmentations
<p>This dataset contains 2D segmentations of images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation algorithm, 3D visualization and geometric analysis are presented in the corresponding manuscript. Files are uploaded in .jpg format and are named: lamella_slicenumber, with slicenumber varying from 1 to 8100. There is also a Matlab file, UndulationData_Zenodo.mat, in which all the relevant variables post analysis are stored. This file contains a variable called "myFiles", which contains the link between the slicenumbers used here and the original dataset that is published in Zenodo (.tif synchrotron images).</p>
Visualization of guided elastic waves generated by SHPFP transducers
<p>DualSH-PFP_measurement.avi: velocity magnitude generated by the Dual SHPFP and measured by Laser Doppler vibrometry</p> <p>SH-PFP_measurement.avi: velocity magnitude generated by the original SHPFP and measured by Laser Doppler vibrometry</p> <p>DualSHPFP_simulation.avi: circumferential component of the velocity generated by the Dual SHPFP and determined by simulation</p> <p>SHPFP_simulation.avi: circumferential component of the velocity generated by the original SHPFP and determined by simulation</p> <p>All simulations and measurements are performed at a center frequency of 80 kHz.</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Open Scanning Paths)
<p><strong>Elastic Wave Simulations - Open Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">(Reardon et al., 2023)</a>. If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Line Paths</strong> - The acoustic source scanned along a linear trajectory at speeds ranging from 2 m/s to 12 m/s (scanning speed is indicated in the filename).</p> <p><strong>Zigzag Paths</strong> - The acoustic source scanned along a zigzag path on the surface of the simulated medium with x-axis scanning speed <em>v<sub>x</sub></em> = 3, 4, 5, 6 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>y</sub></em>, of +-2.5 m/s yielding a zigzag path (2 cm path width). The x-axis scanning speed is designated in the filename.</p> <p><strong>Letter Paths</strong> - The acoustic source scanned the a trajectory in the shape of the letter "Z." Scanning speeds ranged from 2 m/s to 12 m/s (scanning speed is designated in the filename).</p> <p><strong>Focus Control Rate</strong> - The acoustic source scanned along a linear trajectory at 7 m/s but at different focus control sample rates <em>f<sub>c</sub></em>. These paths amount to a courser sampling of the linear trajectory. In lieu of updating the location of the acoustic source at each timepoint in the simulation, we specified a rate at which the location of the acoustic source would be updated. We set <em>f<sub>c</sub></em> to approximately 0.7, 1.4, and 4.2 kHz (designated at the end of the filename as VeryCoarse, Coarse, and Fine, respectively). (Compare with Line_07, which has the finest path sampling and an *f<sub>c</sub>* of approximately 200 kHz.)</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN.</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints.</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep Q</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Closed Scanning Paths)
<p><strong>Elastic Wave Simulations - Closed Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Circle Paths</strong> - The acoustic source was scanned at a constant linear speed along a circular trajectories with two different diameters - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The linear scanning speed ranged from 2 to 20 m/s and is designated in the filename.</p> <p><strong>Square Paths</strong> - The acoustic source was scanned at a constant speed along square trajectories with two different edge lengths - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The scan speed ranged from 2 m/s to 10 m/s and is designated in the filename.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep</p> <p><strong>nCycles</strong> - Number of pattern repetitions</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Venus visco-elastic Love numbers
<p>Supporting software and data for the manuscript <strong>Constraining the Venus interior structure with future VERITAS measurements of the gravitational atmospheric loading </strong></p> <p>https://doi.org/10.3847/PSJ/acc73c</p> <p> </p> <p>See README.pdf for additional details. </p>
Battery and water heater energy elasticity performance optimisation
<p>This dataset provides actual data demonstrating the INVADE European Union initiative (https://h2020invade.eu/) from the Bulgarian pilot situated in Albena resort, Bulgaria (https://albena.bg/). It represents results from two different approaches to energy elasticity - using a 200kWh industrial sized battery with a combination of a PV, as well as using water heaters with a combination of thermal solar collectors. For both approaches, the system takes into account the energy prices as listed in the Independent Bulgarian Energy Exchange (http://www.ibex.bg/en), as well as weather forecast for the expected energy production from the solar panels.</p> <p><strong>Battery.xlsx</strong> (16 days worth of data for the battery as follows):</p> <ul> <li>Timestamp: the time stamp of the entered data point</li> <li>IBEX SpotPrice (EUR/MWh): the energy price for the current data point</li> <li>Consumption (kWh): the current energy consumption from the grid as taken from the energy meter into the facility</li> <li>ChargingPowerRegulation (kW): control signal received from the system to charge the battery</li> <li>DischargingPowerRegulation (kW): control signal received from the system to discharge the battery</li> <li>EnergyLevel (kWh): the energy level of the battery</li> <li>PV Production (kWh): the produced energy by the PV installation</li> <li>ActualSolarIrradiation (W/m^2): the current solar irradiance</li> <li>ActualTemperature (℃): the current temperature</li> </ul> <p><strong>WaterHeater.xlsx</strong> (1 month worth of data for the water heater as follows):</p> <ul> <li>Timestamp: the time stamp of the entered data point</li> <li>IBEX SpotPrice (EUR/MWh): the energy price for the current data point</li> <li>Consumption (kWh): the current energy consumption from the grid as taken from the energy meter into the facility</li> <li>EnergyLevelHeat (kWh): the current thermal energy level in the water boilers</li> <li>EnergyHeatCapacity (kWh): the current thermal energy capacity of the water boilers</li> <li>HeatProduction (kWh): the current thermal energy production by the solar thermal collectors</li> <li>ActualSolarIrradiation (W/m^2): the current solar irradiance</li> <li>ActualTemperature (℃): the current temperature</li> </ul> <p>Please, make all Creative Commons license attributions for usage of this dataset to "Albena AD (https://albena.bg/)"</p>
Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil
<p>Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics).</p> <p>The excel file includes Aphanomyces root rot disease visual scores (<em>Root_Rating</em>), unique identifier for each lentil accession/line (<em>Lentil_ID</em>), unique identifier for each experiment (<em>Experiment</em>), and unique identifier for each image (<em>Lab_ID</em>).</p>
Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements (Supplemental Material)
<p>Supplemental material to <a href="https://openaccess.leidenuniv.nl/handle/1887/40902"><em>Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements</em>; hdl:1887/40902</a>. The supplemental material consists of 9 videos:</p> <p><strong>S1, S2</strong><br /> Two examples of foam under shear, φ = 0.85 (S1) and φ = 1.25 (S2). The foam is sheared from s<sub>CD</sub> = − 0.2 to s<sub>CD</sub> = + 0.2 at γ̇=3 × 10<sup>−5</sup> /s. Time and a scale bar are indicated in the top right; the video is sped up 250× .<br /> <br /> <strong>S3,S4</strong><br /> Difference imaging for direct (top) and affine-corrected (bottom) images, for the same systems as in S1 and S2. Both the real space (left) and difference images (right) are shown. The direct difference images are dominated by the affine deformation, while the affine-corrected difference images highlight the nonaffine motion in the system.</p> <p><strong>S5,S6</strong><br /> Tracked particle trajectories for the same systems as in S1 and S2. Particle trajectories are indicated using white curves. Left: direct tracking data, right: affine-corrected tracking data.</p> <p><strong>S7</strong><br /> Compression of a foam from φ = 0.77to φ = 1.41 under ε̇ = − 3 × 10<sup>−5</sup> /s. (left) Real space image; (right) from top to bottom: flame graph and log<sub>10</sub> A and β from the power law fit Eq. (4.21). Time is indicated on the top left, φ is indicated at the bottom left. With increasing confinement, we observe a transition from fully smooth to fully intermittent behavior.</p> <p><br /> <strong>S8,S9</strong><br /> Two examples of foam under shear, φ = 0.9 (S8) and φ = 1.5 (S9). The foam is sheared from s<sub>CD</sub> = −0.2 to s<sub>CD</sub> = +0.2 at γ̇=3 × 10<sup>−5</sup> /s. At low density, A ≈ 10<sup>−6</sup> and β ≈ 1.6 are fairly constant, while we can clearly distinguish the quiet and active periods for the high density foam.</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Dataset related to the article titled "Prediction of elastic modulus of basaltic rocks using machine learning methods."
<p>Dataset related to the article titled "Prediction of elastic modulus of basaltic rocks using machine learning methods."</p>
Linear rheology of liquid foam - data from "Delayed elastic contributions to the viscoelastic response of foams"
<p>Linear rheological data of Gillette shaving cream "normal skin" made by Gillette UK LTD, acquired at the Department of Physics, University of Fribourg, Switzerland, between 2019 and 2022. Dataset contains elastic modulus, creep-recovery, stress relaxation, and frequency sweep experimental data, together with model data, as described in "Delayed elastic contributions to the viscoelastic response of foams", The Journal of Chemical Physics, 2022, published in the special issue <em>Slow Dynamics</em> [DOI: 10.1063/5.0085773].</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.