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3,309 results for “mri”
TheVirtualBrain Macaque MRI
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MRI data of 40 adult participants in response to a cue induced craving task following food fasting, social isolation and baseline (within-subject design)
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T2-weighted Kidney MRI Segmentation
<p>A dataset containing 100 T<sub>2</sub>-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. </p> <p>For more information about the dataset please refer to <a href="https://doi.org/10.1002/mrm.28768">this article.</a></p> <p>For an executable that allows automated segmentation of the kidneys from this dataset please refer to <a href="https://github.com/alexdaniel654/Renal_Segmentor">this software.</a></p>
functional MRI study on the language stress perception in a foreign language
<p>fMRI dataset of 91 participants during a linguistic task about language stress perception in a foreign language.</p> <p>Participants listened to pairs of words in a foreign language (Spanish) and had to indicate if the words were the same or different. The different pairs differed either by the stress pattern, or by the final vowel.</p> <p>This dataset was divided into two groups: 51 participants with French as native language and 40 with Swiss-German as native language. None of the participants had knowledge of Spanish.</p> <p>This repository respects the BIDS standard (<a href="https://bids.neuroimaging.io/">https://bids.neuroimaging.io/</a>), including all the raw data (func, fmap, anat) and metadata in order to reproduce the processing.</p> <p>These data have been used in two papers:</p> <p>S. Schwab, M. Mouthon, L.B. Jost, J. Salvadori, I. Yakoub, E. Ferreira da Silva, N. Giroud, B. Perriard and J.M. Annoni, Neural correlates of lexical stress processing in a foreign free-stress language; Brain and Behavior (2023)</p> <p>L. Rogenmoser, M. Mouthon, F. Etter, J. Kamber, J.M. Annoni and S. Schwab; The processing of stress in a foreign language modulates functional antagonism between default mode and attention network regions, (submitted)</p>
Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
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Postnatal Affective MRI Dataset
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In vivo T1w MRI of a TDP-43 knock-in mouse model of ALS-FTD
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Effects of Phase Regression on High-Resolution Functional MRI of the Primary Visual Cortex
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Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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T1-weighted brain MRI acquired from awake and unrestrained sheep
<p>This dataset contains T1-weighted brain MRI images acquired from 6 awake sheep, 1 anesthetized sheep and the MRI acquisition parameters.</p> <p><strong>When using this data please cite: </strong>Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> </p> <p><strong>Note:</strong> A "Version v2" was created because the original "13332_anesthetized_T1.nii" file was corrupted.</p>
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Functional MRI data from medetomidine-isoflurane anesthetized marmosets
<p>This dataset contains unprocessed <strong>functional MRI (fMRI)</strong> data acquired in <strong>common marmosets</strong> (<em>Callithrix jacchus</em>), The data were obtained during a continuous infusion of the sedative <strong>medetomidine</strong>, supplemented with a low concentration of <strong>isoflurane</strong>. All experiments were carried out in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.</p> <p><strong>Related paper</strong></p> <p>This dataset supplements the following <a href="https://www.biorxiv.org/content/10.1101/2023.11.21.568138v1.abstract">manuscript</a>.</p> <p><strong>Preserving functional network structure under anesthesia in the marmoset monkey brain</strong></p> <p>M Ortiz-Rios, N Sirmpilatze, J Koenig, S Boretius - bioRxiv, 2023</p> <p><strong>doi: <a href="https://doi.org/10.1101/2023.11.21.568138">https://doi.org/10.1101/2023.11.21.568138</a> </strong></p> <p><strong>Data structure</strong></p> <p>The main data files are organized into eight zipped folders - <em><strong>sub-02.tar.gz, .... sub-09.tar.gz </strong></em> - each constituting a dataset formatted according to the <a href="https://bids.neuroimaging.io/">Brain Imaging Data Structure</a> specifications (BIDS v1.6.0).</p> <ul> <li>Each BIDS-formatted dataset contains subfolders for individual sessions (e.g. <em><strong>ses-0001</strong>, </em>etc.).Additionally, a text file, <strong><em>participants.tsv</em></strong>, with some essential information about the subjects (e.g. age, weight, sex).</li> <li>Each subject-specific folder contains subfolders named <em><strong>func</strong> </em>and <strong><em>anat</em></strong>, storing fMRI and structural MRI data respectively. The (f)MRI data are provided in <a href="https://nifti.nimh.nih.gov/">NIfTI format</a> (suffixed with <strong><em>.nii.gz</em></strong>). </li> <li>The <em><strong>func</strong></em> files are named as <strong><em>{sub-id}_{ses-id}_{run-id}_{task-id}_bold.nii.gz. </em></strong>The task id is based on runs acquired for resting-state or during visual stimulation.</li> </ul> <p><strong>BIDS-formatted dataset</strong></p> <p>The basic characteristics of the datasets are given below. More details can be found in the <a href="https://www.biorxiv.org/content/10.1101/2023.11.21.568138v1.abstract">preprint</a>.</p> <ol> <li><em><strong>Marmoset</strong></em> <ul> <li><strong>Institution:<em> </em></strong>German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany</li> <li><strong>MR system:</strong> Bruker BioSpec 9.4 T, equpped with B-GA 20S gradient</li> <li><strong>Anatomical MRI scan:</strong> Proton density-weighted (PDw) with magnetization transfer (MT) pulse, 1 per subject</li> <li><strong>fMRI scan:</strong> GE-EPI, several runs per subject (between 6 and 9 runs), duration 330 s for visual runs and 600 s for resting-state runs, all with a TR of 2 s and a resolution of 0.4 mm isotropic.</li> <li><strong>Subjects:</strong> 8 <em>Callithrix jacchus</em></li> <li><strong>Age range:</strong> 2.6 - 9.5 years</li> <li><strong>Weight range:</strong> 375 - 517 g</li> <li><strong>Sex:</strong> 5 males and 3 females</li> <li><strong>Ethics oversight:</strong> Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval numbers 33.19-42502-04-17/2496)</li> </ul> </li> </ol>
Data for: Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI
<p>Magnetic Resonance Imaging data used in the work "Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p> </p> <p>The *_ind.{cfl,hdr} files store the indices of the different spokes of the corresponding datasets:</p> <p> </p> <p><strong>data_res_S{1597,0987,0377,0233,0089,0055,0021}</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 3.2|2.04</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{16,15,13,12,10,9,7}^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_raga, data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{13}^1, 2\psi^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi^1</p> <p>Sampling: GA</p> <p><br><br> </p> <p><strong>data_invivo_ga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi^1</p> <p>Sampling: Golden-Ratio</p> <p><br> </p> <p> </p> <p><strong>data_invivo_raga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi_{13}^1</p> <p>Sampling: RAGA</p> <p><br> </p>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</p>
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</p>
MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).</p>
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p>
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 2
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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.