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.
694
datasets available to search
ShareScore release 0.9.0
Dataset results
694 results for “Stiffness”
Survival-associated cellular response maintained in pancreatic ductal adenocarcinoma (PDAC) switched between soft and stiff 3D microgel culture
Open the record for dataset details and reuse information.
Shape, motion and optical cues to stiffness of elastic objects
<p>Dataset relative to the following publication:</p> <p>Paulun, V.C., Schmidt, F., van Assen, J.J.R., & Fleming, R. W. (2017). Shape, motion and optical cues to stiffness of elastic objects. <em>Journal of Vision, 17(1):20, </em>1-20, doi:10.1167/17.1.20</p> <p>Each folder contains the data and stimulus material relative to one experiment and a text file with comments.</p>
Inferring the stiffness of unfamiliar objects from optical, shape and motion cues
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., Paulun, V. C., van Assen, J. J. R., & Fleming, R. W. (2017). Inferring the stiffness of unfamiliar objects from optical, shape, and motion cues. <em>Journal of Vision, 17(3):18</em>, 1-17. <a href="http://dx.doi.org/10.1167/17.3.18">http://dx.doi.org/10.1167/17.3.18 </a></p> <p>Each folder contains the stimuli and data relative to one experiment and a text file with comments.<br> The folder of Experiment 1 (Material) also contains the Maxwell (NextLimit Technologies, Madrid, Spain) material files that were used to render the stimuli in all experiments.</p> <p> </p>
Estimates for the stiffness, strength and drift capacity of stone masonry walls based on 123 quasi-static cyclic tests reported in the literature
<p>Database of 123 shear and compression tests on stone masonry walls reported in the literature. Test references, geometrical and typological data, loading and boundary conditions, mechanical characterisation data, and synthetic test results are collected. Such test results include failure mode, force and displacement capacities for different limit states and estimates of the elastic and effective stiffness. Hysteretic force-displacement curves, digitalised from the sources, and the derived envelopes are provided, when available, as .csv files.</p>
Data rom: Stiffness anisotropy coordinates supracellular contractility driving long-range myotube-ECM alignment
<p>The ability of cells to organize into tissues with proper structure and function requires the effective coordination of proliferation, migration, polarization, and differentiation across length scales. Skeletal muscle is innately anisotropic; however, few biomaterials can emulate mechanical anisotropy to determine its influence on tissue patterning without introducing confounding topography. Here, we demonstrate that substrate stiffness anisotropy coordinates contractility-driven collective cellular dynamics resulting in C2C12 myotube alignment over millimeter-scale distances. When cultured on mechanically anisotropic liquid crystalline polymer networks (LCNs) lacking topography, C2C12 myoblasts collectively polarize in the stiffest direction. Cellular coordination is amplified through reciprocal cell-ECM dynamics that emerge during fusion, driving global myotube-ECM ordering. Conversely, myotube alignment was restricted to small local domains with no directional preference on mechanically isotropic LCNs of the same chemical formulation. These findings provide valuable insights for designing biomaterials that mimic anisotropic microenvironments and underscore the significance of stiffness anisotropy in orchestrating tissue morphogenesis.</p>
Evaluation of comparative efficacy of Trayodashang Guggul versus Asitaka Churna Vati in the management of Katigraha (Stiffness in Lumbar Region): A randomized controlled trial protocol
Open the record for dataset details and reuse information.
Spin wave stiffness and damping in a frustrated chiral helimagnet Co8Zn8Mn4 as measured by small-angle neutron scattering
<p>The repository contains the data presented in the figures in the manuscript entitled <br> "Spin wave stiffness and damping in a frustrated chiral helimagnet Co8Zn8Mn4 as measured by small-angle neutron scattering".</p> <p>Requests for further information can be directed to the corresponding authors Victor Ukleev (victor.ukleev 'at' psi.ch).</p>
Morphing of Stiffness-Heterogeneous Liquid Crystal Elastomers Via Mechanical Training and Locally Controlled Photopolymerization
<p>This dataset contains experimental and simulation data that support the findings of the study.</p> <p>Experimental data: stress-strain tests of highly crosslinked and lightly crosslinked liquid crystal elastomers.</p> <p>Simulation data: Codes (user element subroutine, .inp files) used for the finite element modeling of liquid crystal elastomer structures using the commercial software ABAQUS/Implicit.</p>
Data and models to accompany "Turgor pressure affects transverse stiffness and resonant frequencies of buzz-pollinated poricidal anthers"
<p>Data sets and models to accompany the paper "Turgor pressure affects transverse stiffness and resonant frequencies of buzz-pollinated poricidal anthers". </p>
At matched loads, aging does not alter ankle, muscle, or tendon stiffness
<p>Older adults have difficulty maintaining balance when faced with postural disturbances, a task that is influenced by the stiffness of the triceps surae and Achilles tendon. Age-related changes in Achilles tendon stiffness have been reported at matched levels of effort, but measures typically have not been made at matched loads, which is important due to age-dependent changes in strength. Moreover, there has been limited investigation into age-dependent changes in muscle stiffness. Here, we investigate how age alters muscle and tendon stiffness and their influence on ankle stiffness. We hypothesized that age-related changes in muscle and tendon contribute to reduced ankle stiffness in older adults and evaluated this hypothesis when either load or effort were matched. We used B-mode ultrasound with joint-level perturbations to quantify ankle, muscle, and tendon stiffness across a range of loads and efforts in seventeen healthy younger and older adults. At matched loads relevant to standing and the stance phase of walking, there was no significant difference in ankle, muscle, or tendon stiffness between groups (all p > 0.13). However, at matched effort, older adults exhibited asignificant decrease in ankle (27%; p = 0.008), muscle (37%; p = 0.02), and tendon stiffness (22%; p = 0.03) at 30% of maximum effort. This is consistent with our finding that older adults were 36% weaker than younger adults in plantarflexion (p = 0.004). Together, these results indicate that, at the loads tested in this study, there are no age-dependent changes in the mechanical properties of muscle or tendon, only differences in strength that result in altered ankle, muscle, and tendon stiffness at matched levels of effort.</p>
Matrix stiffness influences response to chemo and targeted therapy in brain metastatic breast cancer cells
Open the record for dataset details and reuse information.
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, vol. 271, p. 108171, 2022.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Measurement and identification of the joint stiffness on a serial articulated industrial robot
<p>This document exemplifies elastostatic compliance calibration on an articulated industrial robot, which has been calibrated at KTH Royal Institute of Technology in 2019 using procedure outlined in the CWA-17384. All data processing is done in Matlab 2018b® using the Peter Corke’s as well as Computer Vision System toolbox for robotics. In case of questions do not hesitate to send an e-mail to <a href="mailto:theissen@kth.se">theissen@kth.se</a> to obtain data and algorithms in c++ or other formats.</p>
Space-Fractional Truss Element - shape functions and stiffness matrix
<p>The following research data zipped to a .zip file is included.</p> <p>Data refers to formulas for shape functions (\(N_{i}\)) and stiffness matrix components (\(K_{ij}\)) for a non-local truss element formulated in terms of fractional calculus (space-Fractional Truss Element) and the finite element method.</p> <p>The data are described as follows:</p> <ul> <li>The .zip file contains information in the name about the number of nodes of the truss element (m).</li> <li>Formulas for shape functions written in different formats: <ul> <li>Display_Ni.svg shows the formula in a conveniently viewable form;</li> <li>LaTeX_Ni.txt contains a formula written in LaTeX syntax;</li> <li>Sym_Ni.txt contains a formula written in symbolic form, allowing loading into a calculation program (e.g., by loading a file and using the sympify() function);</li> <li>Plot_Ni.svg contains plotted shape functions.</li> </ul> </li> <li>Formulas for the elements of the stiffness matrix written in different formats:<br> <ul> <li>The formula for the elements of the stiffness matrix is as follows, <br>\(K_{ij}=EA\frac{L}{2}\int_{-1}^{1} B_{i}B_{j} d\xi. \)</li> <li>In the files, the formulas for \(B_{i} \) and the product of \(B_{i}B_{j} \) are written - in different formats, with naming analogous to that described for the shape functions.</li> </ul> </li> </ul>
CAABA/MECCA model output for evaluating optimized step size control in Rosenbrock solvers for stiff ODEs
<p>This dataset comprehend the simulation output obtained with the CAABA/MECCA model for the optimization of the step size control in the Rosenbrock integrators available from the Kinetic PreProcessor (KPP) version 2.2.3_rs4. This dataset is made avalaible for the peer-review of the manuscript describing the model and the different options of step size control that was implemented. </p>
A Proportional Control Strategy for Stiffness Tuning of Parallel Manipulators
<p>MBDyn models for the paper "A Proportional Control Strategy for Stiffness Tuning of Parallel Manipulators"</p>
Stiff stabilisation and position control of suspended loads with aerodynamic actuators
<p>The video presents the work done in the project 'Position control of suspended loads via aerodynamic thrust for sling load rescue operations', financed by the Spark SNF programme.</p>
Equilibrated Kremer-Grest polymer melts of M=1000 linear chains with Z=200 entanglements for varying chain stiffness
<p>Kremer-Grest model polymer melts of highly entangled linear chains. Each melt has approximately 1000 chains of Z=200 entanglements each. Systems have been generated for integer and half-integer stiffness kappa=-2.0 to 6.0. System sizes range from 25M to 2M beads.</p> <p>For details regarding the equilibration process and the Kremer-Grest polymer model see C. Svaneborg & R. Everaers ""Multiscale equilibration of highly entangled isotropic model polymer melts" J. Chem. Phys. 158, 054903 (2023) <a href="https://doi.org/10.1063/5.0123431">https://doi.org/10.1063/5.0123431</a></p> <p>Filenames denote the kappa<value> used when equilibrating the melt as well as the number of entanglements Z<number> and the number of molecules M<number>. The files are in ASCII format in the format of a LAMMPS data file. (https://lammps.sandia.gov) The semantics is self-explanatory, sections contains id, molecule, unwrapped coordinates of all beads, as well as bond and angular interactions between all beads.</p> <p>We acknowledge that part of the results of this research was obtained using the PRACE Research Infrastructure resource Joliot-Curie SKL based in France at GENCI@CEA. Computing facilities were provided by the eScience Center at the University of Southern Denmark and financed by the Faculty of Science.</p> <p>Please cite as:</p> <p>@article{MultiscaleEquilibrationHighlyEntangledIsotropicModelPolymerMelts,<br> author = {Svaneborg,Carsten and Everaers,Ralf },<br> title = {Multiscale equilibration of highly entangled isotropic model polymer melts},<br> journal = {J. Chem. Phys.},<br> volume = {158},<br> number = {5},<br> pages = {054903},<br> year = {2023},<br> doi = {10.1063/5.0123431},</p> <p> URL = {https://doi.org/10.1063/5.0123431}</p> <p>}</p> <pre>@misc{EquilibratedKGMeltsZ200, author = {Svaneborg,Carsten and Everaers,Ralf}, title = {Equilibrated Kremer-Grest polymer melts of M=1000 linear chains with Z=200 entanglements for varying chain stiffness.}, month = feb, year = 2023, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.7034881}, url = {https://doi.org/10.5281/zenodo.7034881} }</pre>
Effect of ACE-inhibitors on Aortic Stiffness in Elderly Patients With Chronic Kidney Disease
ClinicalTrials.gov study NCT00874432. IPD Sharing: Not stated. Countries: 1. Publications: 34.
Normal Liver Stiffness by MR Elastography
ClinicalTrials.gov study NCT03235414. IPD Sharing: NO. Countries: 1. Publications: 1.
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.