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654 results for “deformance”
Data for patient-specific solution of the electrocorticography forward problem in deforming brain
<p>This dataset contains magnetic resonance (MR) and computed tomography (CT) images of a patient undergoing intracranial electrical monitoring using electrocorticography grid electrodes, together with patient-specific geometry and computational grids created from these images applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain”. The images were acquired at Boston Children’s Hospital and provided to The University of Western Australia’s Intelligent Systems for Medicine Laboratory for analysis. The analysis was conducted using our open-source SlicerCBM software extension for the 3D Slicer medical imaging platform. The analysis steps include image processing to obtain the patient-specific brain geometry, construction of computational grids (tetrahedral grid for meshless solution of biomechanical model and regular hexahedral grid for finite element solution of the electrocorticography forward problem), biomechanics-based image warping to predict the postoperative images corresponding to the brain configuration deformed by placement of subdural electrodes, and patient-specific solution of the electrocorticography forward problem to compute the electric potential distribution within the patient’s head. We use well-established open-source data file formats including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry and Visualization Toolkit (VTK) files for computational grids. This facilitates the re-use of this dataset in a range of studies that rely on medical image analysis, and computational biomechanics and electrostatics to solve the electrocorticography forward problem for electrical source imaging.</p>
Paleomagnetic Evidence of the Deformation of the Pontides during the closure of the Intra-Pontide Ocean in the Early Cretaceous
<p>Several models exist concerning the deformation history of the Pontides in North Anatolia during the Cretaceous period, which vary depending on the positions of the Istanbul and Sakarya zones, the consumption of the northern branches of the Neotethys ocean and the rifting of several sub-basins. Notably, the early Cretaceous tectonic history of the Pontides involved the closure of the northern Neotethys ocean (Intra-Pontide ocean), and the collision between the Istanbul and Sakarya zones, producing thrust structures along the collisional front. The lack of paleomagnetic data providing evidence for this deformation pattern demonstrates that further investigation is required, particularly focusing on the Lower Cretaceous strata in the Pontides. Thus, the present study aimed to examine samples from a total of 78 sites from the Lower-Upper Cretaceous sedimentary rocks, and Middle Eocene to Middle Miocene sedimentary and volcanic rocks. Results of the present study indicated large counter-clockwise rotations up to R±DR=<strong>-</strong>73.9°±9.1°, and small clockwise rotations of R±DR= 14.2°±12.2° in the Istanbul and Sakarya zones, during the Early Cretaceous and Late Cretaceous periods. These rotation patterns are accompanied by the closure of the Intra-Pontide ocean, and the collision between the Istanbul and Sakarya zones during the Early and Late Cretaceous periods. On the other hand, in the Middle Eocene, small counter-clockwise rotations of R±DR=-6.4°±13.9° and R±DR=4.6°±12.9° along the western coastline of the Pontides indicated that the northern margin of the Pontides was stable during this period.</p>
Coseismic ground deformation map for the Mw5.7 20 March, 2019 Acipayam earthquake (Turkey)
<p>This repository contains an interferogram from Sentinel-1 satellite acquired over the Mw5.7 20 March, 2019 Acipayam earthquake (Turkey). This interferogram was obtained by processing Sentinel-1 SAR data with the JPL-developed InSAR Scientific Computing Environment (ISCE) open-source software package.</p> <p>Sentinel-1 descending track 138:</p> <ul> <li>Master image: 11 Mar 2019, identifier: S1A_IW_SLC_1SDV_20190311T040709_20190311T040736_026285_02F00C_6A73</li> <li>Slave image: 23 Mar 2019, identifier: S1A_IW_SLC_1SDV_20190323T040709_20190323T040736_026460_02F67D_EC68</li> </ul> <p>This interferogram was generated for figures in: Jiang, Y., and González, P. J. (2020). "Bayesian inversion of wrapped satellite interferometric phase to estimate fault and volcano surface ground deformation models. " JGR: Solid Earth.</p>
Research data supporting for Stress-induced amorphization triggers deformation in the lithospheric mantle
<p>Original TEM micrographs used to prepare the figures of the article</p>
Using coupled micropillar compression and micro-Laue diffraction to investigate deformation mechanisms in a complex metallic alloy Al13Co4
<p>In this investigation, we have used <em>in-situ</em> micro-Laue diffraction combined with micropillar compression of focused ion beam milled Al<sub>13</sub>Co<sub>4</sub> complex metallic alloy to study the evolution of deformation in Al<sub>13</sub>Co<sub>4</sub>. Streaking of the Laue spots showed that the onset of plastic flow occured at stresses as low as 0.8 GPa, although macroscopic yield only becomes apparent at 2 GPa. The measured misorientations, obtained from peak splitting, enabled the geometrically necessary dislocation density to be estimated as 1.1 x 10<sup>13</sup> m<sup>-2</sup>.</p>
Haptically perceived softness of deformable stimuli can be manipulated by applying external forces during the exploration
<p>The perception of softness is the result of the integration of information provided by multiple cutaneous and kinesthetic signals. The relative contributions of these signals to the combined percept of softness was not yet addressed directly. We transmitted subtle external vertical forces to the exploring human finger during the exploration of deformable silicone rubber stimuli to dissociate the force estimates provided by the kinesthetic signals and the efference copy from cutaneous force estimates. This manipulation introduced a conflict between the cutaneous and the kinesthetic/efference copy information on softness. We measured Points of Subjective Equality (PSE) of manipulated references to stimuli which were explored without external forces. PSEs shifted as a linear function of external force in predicted directions - to higher compliances with pushing and to lower compliances with pulling force. We found relative contribution of kinesthetic/efference copy information to perceived softness being 23% for rather hard and 29% for rather soft stimuli. Our results suggest that an integration of the kinesthetic/efference copy information and cutaneous information with constant weights underlies softness perception. The kinesthetic/efference copy information seems to be slightly more important for the perception of rather soft stimuli.</p> <p>Metzger, A., & Drewing, K. (2015). Haptically perceived softness of deformable stimuli can be manipulated by applying external forces during the exploration. In World Haptics Conference (WHC), 2015 IEEE (pp. 75-81). IEEE.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite: The Post-Proc service in Geohazard Exploitation Platform applied to Sentinel-1 dataset
<p>In the framework of the ESA funded project “MEMpHIS - Multi Scale and Multi Hazard Mapping Space based Solutions ”, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) together with TRE-ALTAMIRA, generated the EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite. The two maps of ground velocity were derived thanks to the adoption of a specific tool named "Post-Proc", implemented in MEMPHIS and with the support of Terradue, that is able to automatically re-project on the east-west and vertical directions the ascending and descending InSAR time series. In particular, the output refers to the deformation maps calculated by processing with SqueeSAR (TM) method, a large dataset acquired by the ESA Sentinel-1 mission.</p> <p>The Post-Proc tool also calculates the mean accelerations associated to each persistent scatterer in the scenes. Some additional features are also available from this tool:</p> <p>- Change the coherence threshold for selecting a subset of persistent scatterers</p> <p>- Activate a geometrical distortion filter to take into account the layover and foreshortening effects</p> <p>- Change the reference point (position and coherence)</p> <p>- Choose between two types of accelerations: 2<sup>nd</sup> order model or velocity derivative</p> <p>- Choose among three different projection: east-west, vertical, and downslope.</p> <p>The present dataset is composed of the EAST-WEST and VERTICAL acceleration maps. The data are in shapefile format: for each record (PS) the topography, velocity, acceleration, and InSAR coherence is reported.</p>
LOTOS files for Local earthquake tomography of the Aegean crust: Implications for active deformation, large earthquakes, and arc volcanism
<p>This archive contains LOTOS codes and model files/folders associated with the publication "Local earthquake tomography of the Aegean crust: Implications for active deformation, large earthquakes, and arc volcanism" (inside Aeg_tomo.zip)</p> <p> </p> <p>New in version 2:</p> <p>3D model files as well as lateral sections for Vp, Vs, and Vp/Vs (inside nc_3d_model.zip)</p>
3D cell tracking dataset of bacterial biofilm deformation and recovery under shear flow
<p>This MAT file includes dataset in the scientific article "<i>In vivo</i> microrheology reveals elastic and plastic responses inside three-dimensional bacterial biofilms" by the following authors: Takuya Ohmura, Dominic Skinner, Konstantin Neuhaus, Gary Choi, Jörn Dunkel, Knut Drescher. This MAT file can be conveniently opened with Matlab. </p><p>When you open this file with Matlab, you will find 4 variables stored in the file "Data_v3_loop2_newRxy_bidx1_274.mat"</p><p><strong>Variable 1: name_parameter</strong></p><p>Names of 31 parameters for columns in 3 variables: 'deformation_all', 'recovery_all' , 'plasticity_all'. The parameters have cell displacements, orientations, coordinates, biofilm indexes and experimental conditions. When the parameters have units, they are shown in the names. </p><ul><li>'x_Frame1[um]'</li><li>'y_Frame1[um]'</li><li>'z_Frame1[um]'</li><li>'Normalized_x_Frame1'</li><li>'Normalized_y_Frame1'</li><li>'Normalized_z_Frame1'</li><li>'LocalDensity_Frame1(VolumeFractionAround30px)'</li><li>'LocalCellNumberDensity_Frame1(VolumeFractionAround30px)'</li><li>'NematicOrderParameter_Frame1'</li><li>'AlignmentFlow_Frame1[rad]'</li><li>'AlignmentRadial_Frame1[rad]'</li><li>'AlignmentZaxis_Frame1[rad]'</li><li>'d_x[um]'</li><li>'d_y[um]'</li><li>'d_z[um]'</li><li>'Normalized_d_x'</li><li>'Normalized_d_y'</li><li>'Normalized_d_z'</li><li>'d_LocalDensity'</li><li>'d_LocalNumberDensity[um^-3]'</li><li>'d_NematicOrderParameter'</li><li>'d_AlignmentFlow[rad]'</li><li>'d_AlignmentRadial[rad]'</li><li>'d_AlignmentZaxis[rad]'</li><li>'CrossProduct[um^2]'</li><li>'BiofilmIndexNumber'</li><li>'Biofilm_width[um]'</li><li>'Biofilm_height[um]'</li><li>'Biofilm_volume[um^3]'</li><li>'FlowRate[ul/min]'</li><li>'Duration[min]'</li></ul><p><strong>Variable 2: deformation_all</strong></p><p>The rows indicate 704198 single-cell trackings in deformations of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p><strong>Variable 3: recovery_all</strong></p><p>The rows indicate 685991 single-cell trackings in recoveries of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p><strong>Variable 4: plasticity_all</strong></p><p>The rows indicate 665749 single-cell trackings in plasticities of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p> </p><p>To plot the cell tracked data in the figures of the article, use our MATLAB code uploaded in our GitHub (https://github.com/knutdrescher/biofilm-rheology).</p>
JIP HaSPro Loose Rock deformation data
<p>Data associated with manuscript "Predicting loose rock scour protection deformation around monopiles using the relative mobility number and the Keulegan-Carpenter number" accepted for publication in Ocean Engineering (https://doi.org/10.1016/j.oceaneng.2024.117475).</p>
Geodetic anomaly detection and analysis in the Campi Flegrei caldera (Italy) deformation pattern of the 2021-2023 escalating unrest phase
<p>Data used within the manuscript: "<strong><span>First evidence of a geodetic anomaly in the Campi Flegrei caldera (Italy) ground deformation pattern revealed by DInSAR and GNSS measurements during the 2021-2023 escalating unrest phase</span>"</strong></p> <p> </p> <p>Archive content:</p> <ul> <li><code>DTSLOS_CNRIREA_20150325_20231021_FB9K</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from ascending orbits (Track 44) over Campi Flegrei caldera in the 20150325 - 20231021 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>DTSLOS_CNRIREA_20150324_20231020_UJBI</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from descending orbits (Track 22) over Campi Flegrei caldera in the 20150324 - 20231020 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>Campi_Flegrei_GNSS_Weekly_Timeseries</code>: Weekly displacement time series of Campi Flegrei caldera GNSS network from 2016 to 2023.</li> </ul>
Effect of Nd on high temperatures deformation and corrosion behavior of AZE Mg alloy
<p>An extruded AZE Mg alloy (Mg-3Al-1Zn-0.6Nd) has been developed and prepared to examine the effect of the precipitated phase on corrosion and hot deformation behavior. High-temperature compression experiments show that dynamic recrystallization (DRX) occurs at various experimental parameters (473 to 673 K and 1×10<sup>-4</sup> s<sup>-1</sup> to 1×10<sup>-3</sup> s<sup>-1</sup>). The deformation mechanism is dominated by dislocation slip and/or twinning. Al<sub>11</sub>Nd<sub>3</sub> precipitates with two different sizes could impede nucleation and the growth of DRXed grains. Serrated flow occurred owing to the interactions between dislocations and precipitates. The corrosion current density decreases with the addition of Nd elements. Moreover, the continuous precipitation of the Al<sub>11</sub>Nd<sub>3</sub> phase at grain boundaries reduces the corrosion rate in NaCl solution, thereby enhancing the corrosion resistance of AZE alloys.</p>
Numerical simulation of friction extrusion: Process characteristics and material deformation due to friction
<p>This study employs a finite element thermo-mechanical model, using a Lagrangian incremental setting to investigate friction extrusion (FE) under varying process conditions. The incorporation of rotation in FE generates substantial frictional heat, leading to significantly reduced process forces in comparison to conventional extrusion (CE). The model reveals the interplay between temperature, strain, and strain rate across different microstructural zones of the resulting wire. Specifically, the sticking friction condition in FE enhances initial shear deformation, aligning with a homogeneous spatial strain distribution and predicting complete grain refinement in the extruded wire, as per Zener-Hollomon calculations. On the other hand, under the sliding friction condition in FE, the shear deformation is reduced which results in an inhomogeneous microstructure in the extruded wire. The analysis of material flow in the workpiece reveals distinct transitions from the base material to the thermo-mechanically affected zones. The simulated process force, thermal history, and microstructure during sliding friction conditions align well with the findings from performed friction extrusion experiments.</p>
Coseismic ground deformation map for the 2020 M>7.5 Shumagin, Alaska, earthquake doublet
<p>This repository contains six interferograms from ESA Sentinel-1 satellite acquired over the 2020 M>7.5 Shumagin, Alaska, earthquake doublet. These interferograms were obtained by processing Sentinel-1 SAR data with the JPL-developed InSAR Scientific Computing Environment (ISCE) open-source software package. The details of six Sentinel-1 interferograms for the 2020 Shumagin earthquake doublet, M7.8 and M7.6 events, are listed in the table below.</p> <table> <tbody> <tr> <td> <p>Earthquake date and magnitude</p> </td> <td> <p>Track</p> <p>no.</p> </td> <td> <p>Direction</p> <p>(asc/des)</p> </td> <td> <p>Incidence</p> <p>(degree)</p> </td> <td> <p>Primary image</p> <p>(yyyy/mm/dd hh:mm:ss)</p> </td> <td> <p>Secondary image</p> <p>(yyyy/mm/dd hh:mm:ss)</p> </td> </tr> <tr> <td>2020/07/22 06:12:44 M7.8</td> <td>73</td> <td>des</td> <td>30-33</td> <td>2020/07/10 17:03:59</td> <td>2020/07/22 17:04:00</td> </tr> <tr> <td> </td> <td>102</td> <td>des</td> <td>43-46</td> <td>2020/07/12 16:47:32</td> <td>2020/07/24 16:47:33</td> </tr> <tr> <td> </td> <td>153</td> <td>asc</td> <td>36-41</td> <td>2020/07/22 04:23:36</td> <td>2020/09/08 04:23:39</td> </tr> <tr> <td>2020/10/19 20:54:38 M7.6</td> <td>73</td> <td>des</td> <td>30-35</td> <td>2020/10/14 17:04:04</td> <td>2020/10/26 17:04:04</td> </tr> <tr> <td> </td> <td>102</td> <td>des</td> <td>43-46</td> <td>2020/10/16 16:47:36</td> <td>2020/10/28 16:47:36</td> </tr> <tr> <td> </td> <td>153</td> <td>asc</td> <td>34-41</td> <td>2020/10/14 04:23:40</td> <td>2020/11/07 04:23:40</td> </tr> </tbody> </table> <p> </p> <p>These interferograms were generated for figures in: Jiang, Y., González, P. J., and Bürgmann, R. "Subduction earthquakes controlled by incoming plate geometry: The 2020 M>7.5 Shumagin, Alaska, earthquake doublet. " Earth and Planetary Science Letters.</p>
GPS time series of solid Earth deformation for the southern Antarctic Peninsula
<p>This dataset contains vertical GPS time series observed from selected sites in the southern Antarctic Peninsula. The time series were processed using GAMIT-GLOBK software (Herring et al., 2016) in combination with a globally distributed network that includes all available data from the International GNSS Service (IGS), US POLENET-ANET and UKANET networks. The provided archive contains raw and corrected time series for the effect of elastic deformation-induced from the RACMO surface mass balance (SMB) model with a 5.5 km horizontal resolution (van Wessem et al. 2016). This data is used in the manuscript "GPS-observed elastic deformation due to surface mass balance variability in the Southern Antarctic Peninsula" to study how modelled elastic deformation due to SMB variation can explain vertical land motion observed derived from GPS signals.</p>
Research data supporting for "Periclase deforms slower than bridgmanite under mantle conditions"
<p>This deposits contains the files related to the Dislocation Dynamics calculations of creep in MgO presented in the paper</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
High Temperature Compression Studies of a 6082.50 Aluminium Alloy using Deformation Dilatometer
<p>Data recorded in uniaxial compression for a 6082.50 aluminium alloy deformed at temperatures of 490C, 520C and 560C, at strain rates of 10, 1, and 0.1 s-1, to 50% height reduction, using TA Instruments DIL 805 A/D/T Quenching and Deformation Dilatometer. The cylindrical samples measured 5 mm diameter and 10 mm height. The 6082.50 aluminium specimens were machined from an as-cast billet. Two homogenisation have been performed on this material, one at 590C for 8h and one at 520C for 2h. These are identified as homogenisation 1 and homogenisation 2 in the data. Al2O3 platens were used for all tests, with Mo discs superglued at both ends of the sample to maximise thermal contact. Tests were conducted in an inert He gas atmosphere under a vacuum of 1e-5 mbar. The temperature was controlled using an K-Type thermocouple spot-welded to the centre of the samples with another K-Type thermocouple welded halfway between the centre and end to measure the thermal gradient. Not all of these second thermocouples provided data due to breakages. </p> <p>Data recorded at high acquisition frequency during deformation is stored in the 'data_deformation' folder and saved with the format: ' test number (002 to 038)_Compression_Daniel_Al_(homogenisation treatment - see above)_SHT_540C_Deform_(Deformation temperature)_(deformation strain rate)_QuenchHeGas__1. Here SHT_540C refers to the solutionising treatment and QuenchHeGas__1 refers to the method of cooling after deformation. This dataset does not include readings from the off-centre thermocouple. Data in the 'data_basic' folder is recorded at a lower acquisition frequency but includes a recording of the entire thermomechanical cycle, including both heating and cooling stages, as well as deformation. The 'method' folder includes contains the temperature and deformation profiles used. 'pictures' includes images taken of the samples and set up during the experiment. </p>
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
<p>Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain.</p> <p>Our project page can be found at <a href="http://ai.bu.edu/zerowaste/">http://ai.bu.edu/zerowaste/</a></p> <p>Please use the following password to extract the zip files: UP#1VuX409z4</p>
Teller 47 solifluction: Sentinel-1 deformation estimates
<p>Sentinel-1 deformation estimates from orbit 15/377</p> <p>Specifications:</p> <ul> <li>Location: Seward, Peninsula, AK</li> <li>Spatial resolution: ~100 m</li> <li>Unit: m</li> <li>Sign convention: positive: increasing distance to satellite</li> </ul> <p>Files:</p> <ul> <li>avg_disp171819.tif: average thaw-season displacement for years 2017, 2018, 2019; normalized to a 90-day period</li> <li>subseasonal.txt: subseasonal displacement time series for four points J1-J4 in years 2017-2019. Coordinates and measurement times are included.</li> </ul>
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.