Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,987
datasets available to search
ShareScore release 0.9.0
Dataset results
1,987 results for “mode”
A mixed mode cohesive model for FRP laminates incorporating large scale bridging behaviour - Datasets
<p>This data upload includes the experimental results from delaminating FRP-laminates. The experiment consists of DCB specimens where the beam ends are loaded with bending moments. A set-up of LVDTs and a clip-on extensometer are used to calculate the normal and tangential opening displacements at the crack-end.</p> <ul> <li>The test specimens are described in the file "CHO test matrix 130405B.xlsx"</li> <li>The load-displacement data for all specimens are given in the folder "DCB UBM - Experimental results.zip"</li> <li>Acoustic emission recording from the tests are given in the folder "DCB UBM - Acoustic Emission.zip"</li> <li>A set of images for each specimen during testing is given in the folder "DCB Images.zip"</li> </ul> <p>This test series is examined and described in the following peer reviewed papers:</p> <p>R.K. Joki, F. Grytten, B. Hayman, B.F. Sørensen, <em>A mixed mode cohesive model for FRP laminates incorporating large scale bridging behaviour</em>, Engineering Fracture Mechanics, 239, November 2020, <a href="https://doi.org/10.1016/j.engfracmech.2020.107274">https://doi.org/10.1016/j.engfracmech.2020.107274</a></p> <p>R.K. Joki, F. Grytten, B. Hayman, B.F. Sørensen, <em>Determination of a cohesive law for delamination modelling – Accounting for variation in crack opening and stress state across the test specimen width</em>, Composites Science and Technology, 128, 18 May 2016, <a href="https://doi.org/10.1016/j.compscitech.2016.01.026">https://doi.org/10.1016/j.compscitech.2016.01.026</a></p>
Convergence plot Crack Release Energy for Mode-I opening
<p>Convergence plot for the mode-I opening of the infinite body with planar crack. The exact analytical solution is compared with analysis from crack propagation module. </p>
Data from: Foraging mode constrains the evolution of cephalic horns in lizards and snakes
<p>A phylogenetically diverse minority of snake and lizard species exhibit rostral and ocular appendages that substantially modify the shape of their heads. These cephalic horns have evolved multiple times in diverse squamate lineages, enabling comparative tests of hypotheses on the benefits and costs of these distinctive traits. Here, we demonstrate correlated evolution between the occurrence of horns and foraging mode. We argue that although horns may be beneficial for various functions (e.g., camouflage, defence) in animals that move infrequently, they make active foragers more conspicuous to prey and predators, and hence are maladaptive. We therefore expected horns to be more common in species that ambush prey (entailing low movement rates) rather than in actively searching (frequently moving) species. Consistent with that hypothesis, our phylogenetic comparative analysis of published data on 1,939 species reveals that cephalic horns occur almost exclusively in sit-and-wait predators. This finding underlines how foraging mode constrains the morphology of squamates and provides a compelling starting point for similar studies in other animal groups.</p>
Figure Data_Edge modes in 1D MW PC
<p>(a,b) The 2D transmission spectra of the finite periodic microstrip calculated for different values of the bulk<br>parameter: l/d. The solid black lines mark the edges of the bands for the infinite microstrip, corresponding to kz = 0 or kz = π/d. Two ratios l/d = 0.25 and 0.625 are indicated by vertical dashed lines. The symmetry of the Bloch function at the edges of the band is indicated by the letters S and A, respectively. We considered the system (a) composed of five centrosymmetric cells and (b) its modification, where we added cells of modified sizes at the beginning and end of the microstrip. The sizes of the edge cells and all other parameters are the same as those given in the System section. (c,d) The cross section of the 2D spectra (a,b) at l/d = 0.625 (solid black curves) is supplemented by the measured (red curve) transmission spectra for fabricated structures. For the microstrip with additional cells of modified sizes, we can identify the transmission peaks in the second frequency gap (gray area in (d)). This double peak is attributed to edge modes that decay exponentially in space. It is noteworthy that the edge modes do not exist in the second gap for smaller values of l/d. This is related to the qualitative change in the spectrum, where the order of the edges of the gap and their symmetry are swapped: from symmetric (antisymmetric), for small l/d, to antisymmetric (symmetric), for large l/d, at the lower (upper) edge of the gap. The frequency of the edge modes can be tuned by modifying the edge cells: d0 = 12.5 mm, l0 = 0.5 mm, w0 = 11 mm – see black dotted line. The induction of the edge modes is obtained at the expense of attenuation of the third band due to the strong impedance mismatch in this frequency range.</p>
Data for: Hunting mode and habitat selection mediate the success of human hunters
<p class="MsoNormal"><span>As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems<span>. </span>Yet compared to non-human predators<span>, little is known about the </span>drivers and consequences of<span> </span>human<span> hunt</span>ing behavior<span>.<strong> </strong>Here, we characterized the hunting modes, habi</span>tat selection,<span> and harvest success of 483 rifle hunters in California</span> using<span> high-resolu</span>tion <span>GPS </span>data<span>. We used Hidden Markov Models to characterize fine-scale behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state. Hunters exhibited three distinct and successful hunting modes ("coursing", "stalking", and "sit-and-wait"), with stalking as the most successful strategy. Across hunting modes, there was variation in patterns of selection for roads, topography, and habitat cover, with </span>important<span> differences in habitat use of successful and unsuccessful hunters across modes. Our study indicates that hunters can successfully employ a diversity of harvest strategies, and </span>that<span> hunting success </span>is <span>mediated by the </span>interacting effects of<span> hunting mod</span>e and <span>landscape features. Such results high</span>light the breadth of human hunting modes, even within a single hunting technique, and lend insight into the varied ways that humans exert predation pressure on wildlife.</span></p>
Dataset for: Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio
<p>Repository of data plotted in figures for the journal publication "Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio" (doi: 10.1364/OE.500077 ).</p> <p>Please see metadata file for details on individual data files.</p>
Fig. 5 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 5. Covariation between dorsal projections of the sternum and pelvis estimated with the standard PLS analysis. Phylogenetic relationships are projected onto the PLS1 morphospace. Grey wireframe represents mean shape. Species designation as in fig. 4.
Fig. 6 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 6. Boxplot of the ratio of sternum and pelvis centroid sizes. Box represents interquartile range with solid line inside as median, whiskers are minimal and maximal values. Locomotor categories: FP — foot-propelled divers, WP — wing-propelled divers, SSW — surface swimmers.
Fig. 1 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 1. The sternum and pelvis in Common guillemot (Uria aalge) (wing-propelled diver) and Imperial cormorant (Phalacrocorax atriceps) (foot-propelled diver).
Fig. 4 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 4. Covariation between lateral projections of the sternum and pelvis estimated with the standard PLS analysis. Phylogenetic relationships are projected onto the PLS1 morphospace. Grey wireframe represents mean shape. Species points are marked by locomotor categories (FP — foot-propelled divers, WP — wingpropelled divers, SSW — surface swimmers) and numbers: 1 — Cygnus cygnus, 2 — Cygnus olor, 3 — Cygnus melancoryphus, 4 — Chen caerulescens, 5 — Anser erythropus, 6 — Branta ruficollis, 7 — Branta bernicla, 8 — Alopochen aegyptiaca, 9 — Tadorna ferruginea, 10 — Aix sponsa, 11 — Aix galericulata, 12 — Mergellus albellus, 13 — Mergus merganser, 14 — Bucephala clangula, 15 — Phalacrocorax carbo, 16 — Phalacrocorax pelagicus, 17 — Phalacrocorax atriceps, 18 — Pelecanus crispus, 19 — Eudyptes chrysolophus, 20 — Porphyrio porphyrio, 21 — Fulica atra, 22 — Gallinula chloropus, 23 — Podiceps cristatus, 24 — Aethia psittacula, 25 — Uria aalge, 26 — Alca torda.
Fig. 3 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 3. Scheme of landmarks and semilandmarks location on lateral (a) and ventral (b) projections of sternum and lateral (c) and dorsal (d) projections of pelvis in Aix galericulata. Descriptions of the landmarks and semilandmarks are given in tables 1, 2. Measurements: Lst — length of sternum, Lsyns — length of synsacrum.
Fig. 2 in Covariation In Shapes Between The Sternum And Pelvis In Aquatic Birds With Different Locomotor Modes
Fig. 2. Phylogenetic relationships among the studied birds based on Jetz et al. (2012), with their locomotor modes marked as: FP for foot-propelled divers, WP for wing-propelled divers, and SSW for surface swimmers.
Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"
<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong> datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: </p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. </li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. </li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. </li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). </li> </ul> <p> </p> <p>The <strong>NMR Data</strong> of NMA in these datasets includes: </p> <ul> <li>NMR ensembles </li> <li>Individual NMR models extracted from each ensemble </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. </p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-I
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data_mode_I folder is composed of:</p> <p>1- The DEM folder which contains the scripts to generate the DEM samples used in the simulations (Mode_I and Mode_II)</p> <p>2- The Mode_I folder which is composed of:</p> <ul> <li> <p>mode_I: Contains the scripts and data of the section "Mode I crack propagation" presented in the paper</p> </li> <li> <p>post_processing_mode_I: Contains the files to conduct the post processing relative to the section "Mode I crack propagation"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Mode I crack propagation". </p> <h2>Mode_II complementary dataset</h2> <p>The Mode-II part of the study can be found at https://doi.org/10.5281/zenodo.14264611</p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-II
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data folder contains the Mode_II folder which is composed of:</p> <ul> <li> <p>mode_II: Contains the scripts and data of the section "Surface wear during relative sliding" presented in the paper. Only data for the largest case is not provided.</p> </li> <li> <p>post_processing_mode_II: Contains the files to conduct the post processing relative to the section "Surface wear during relative sliding"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Surface wear during relative sliding". </p>
Stretching the limits of refractometric sensing in water by Whispering-gallery-modes resonators
<p>Data suppoting the information and figures presented in the paper "Stretching the limits of refractometric sensing in water by Whispering-gallery-modes resonators"</p>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Kerr quasinormal mode frequencies and excitation factors
<p>Kerr quasinormal mode frequencies and excitation factors for gravitational ($s=-2$), electromagnetic ($s=-1$), and scalar fields ($s=0$). Normalization is $2M=1$ except for the spin parameter $a/M$.</p> <h2>Data Format</h2> <p>$a/M$, $\mathrm{Re}(2M\omega)$, $\mathrm{Im}(2M\omega)$, $\mathrm{Re}(A)$, $\mathrm{Im}(A)$, $\mathrm{Re}(\nu)$, $\mathrm{Im}(\nu)$, $\mathrm{Re}(B)$, $\mathrm{Im}(B)$</p> <p>$M$: Mass<br>$a$: Spin parameter<br>$\omega$: Quasinormal mode frequency<br>$A$: Separation constant<br>$\nu$: Renormalized angular momentum<br>$B$: Excitation factor</p> <h2>Version History</h2> <p>Version 0.2.0: Expanded dataset with additional modes: <br> $s=-2$, $\ell=4–6$, $n=0–7$ <br> $s=-1$, $\ell=2–6$, $n=0–7$<br> $s=0$, $\ell=1–6$, $n=0–7$<br>Version 0.1.1: Added GIF animation (no changes to dataset)<br>Version 0.1.0: Initial release with $s=-2$, $\ell=2–3$, $n=0–7$</p>
Light intensity in reflection mode of PAAO (AJ-5-04-27 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 380 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-5-04-27.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s.</p>
Light intensity in reflection mode of PAAO (AJ-3-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 380 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-3-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s.</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.