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438 results for “3D imaging”
Fig. 2.14 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.14. Picture of a quickly composed image of a mite at 20× magnification.
Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'
<p>3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423</p>
Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'
<p>Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset:</p> <p><strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): .</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Gridrec_reconstruction_downsized.zip?versionId=28d98982-f69d-4eac-9dfa-efcc89c6823c">Gridrec_reconstruction_downsized.zip</a>) and the paganin, or phase-contrast, algortithm (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Phase_contrast_reconstruction_downsized.zip?versionId=bef3260d-2865-4c9b-b5e1-e692edefb691">Phase_contrast_reconstruction_downsized.zip</a>). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension.</p> <p>A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Binary_stack_for_local_thickness.zip?versionId=165e3938-b490-4e56-9c8e-a2084cb39d49">Binary_stack_for_local_thickness.zip</a>), a map of the local thickness is created (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Local_thickness_map.zip?versionId=ce0a7dc7-5e3f-44a4-8881-cf84b6efd87c">Local_thickness_map.zip</a>). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack.</p> <p>Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Hand_labelled_slices.tif?versionId=a21a13ac-fa47-4ef8-a903-ecc433787184">Hand_labelled_slices.tif</a>).</p> <p>Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Fullstack_Prediction_Example-6_training_slices-6_testing_slices.zip?versionId=02b69e65-da85-492e-9b72-9b2b3ccd085f">Fullstack_Prediction_Example-6_training_slices-6...</a>).</p> <p>The source code for the segmentation program is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/master">here</a>, and the source code for the testing used in the paper is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/nb-slices-eval">here</a>.</p>
Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin
<p>We prepared these datasets associated with the paper “Genome-scale imaging of the 3D organization and transcriptional activity of chromatin” published in Cell: <a href="https://doi.org/10.1016/j.cell.2020.07.032">https://doi.org/10.1016/j.cell.2020.07.032</a>.</p> <p>Please find detailed descriptions of individual data files in the README_August 2020.txt.</p> <p>We provide example codes to load and analyze these datasets in: <a href="https://github.com/ZhuangLab/Chromatin_Analysis_2020_cell">https://github.com/ZhuangLab/Chromatin_Analysis_2020_cell</a>.</p> <p>If you use these datasets, please cite our Cell paper.</p>
Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition
<p>This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.<br> </p> <p><strong>Equipment</strong><br> The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand.</p> <p><em>Hyperspectral data</em></p> <p><br> Hyperspectral data acquisition was performed with Surface Optics SOC710 camera. This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range.</p> <p>The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel. </p> <p><em>3D point clouds</em></p> <p><br> The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights </p> <p><em>HD Images</em></p> <p><br> The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources. <br> </p> <p><strong>Data</strong></p> <p>The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles.</p> <p>Scene 1 (denoted <em>SceneEagle</em>) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand.<br> Scene 2 (<em>SceneFruit</em>) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image.<br> Scene 3 (<em>SceneFruit2</em>) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.</p> <p> </p> <p>The data are formatted as follows:<br> - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame.<br> - Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files.<br> - Matched Hyperspectral clouds are also provided as \text{*.obj} files<br> - HD photo files are provided in \text{*.jpg} files.<br> </p> <p><br> <strong>Acknowledgements</strong></p> <p>This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.</p> <p> </p>
Data for: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences
<p>Microscale processes in three-phase suspensions (mixtures of gas, liquids, and solids) can affect the macroscale behavior of the whole suspension. To visualize these small-scale processes at high speed and in 3D, we use a recently developed imaging system: Swept Confocally-Aligned Planar Excitation (SCAPE) microscopy. This dataset contains 3D videos taken with SCAPE microscopy of experiments where different phases interact with each other. Each zipped folder contains raw data and processed data for a single experiment. "Case 1" experiments show CO2 bubbles growing on PMMA (acrylic) particles in sparkling water. The "Case 2" experiment shows water droplets suspended in canola oil and flowing through a porous medium made of packed PMMA particles. "Case 3" experiments show growth of injected air bubbles in particle suspensions (either glass beads in immersion oil, or PMMA particles in a refractive index matched liquid).</p> <p>All scaling parameters are provided in Table 1. "info.txt" files contain metadata for the processed hyperstacks.</p> <p>The experiments provided here are discussed in the following publication:<br> Oppenheimer, J.*, Patel, K.*, Lindoo, A., Hillman, E. M. C., and Lev, E.: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences. <em>Geochemistry, Geophysics, Geosystems.</em> (In press, 12/2020)</p> <p><br> </p>
NASA 3D images catalog
<p>El archivo CSV nasa_3d_models.csv contiene información detallada sobre modelos 3D recopilada del sitio web de la NASA. Los campos incluyen:</p><p>Titles: Título del modelo 3D.</p><p>Descriptions: Descripción del modelo 3D.</p><p>Imagen3D Links: URL de la imagen 3D del modelo.</p><p>Authors: Autores u origen del modelo.</p><p>Missions: Misión relevante del modelo.</p><p>Dates: Fecha de agregado del modelo.</p><p>Keywords: Palabras clave asociadas al modelo.</p><p>Repositories: Enlace al repositorio de GitHub del modelo.</p><p>zips: URL del archivo ZIP asociado al modelo.</p>
TINKER_WP3_2D&3D images - profile scans dataset_221123
<p>2D and 3D images of PCBs showing the gap 2D and 3D information. Moreover, profile measurements are also included in x and y axes.</p>
Test Dataset for 3D semantic image segmentation of the various organs from CT and MR scans
<p>These test cases are for the <a href="https://github.com/MIC-DKFZ/nnUNet/releases/tag/v1.7.1">nnUnet v1</a> models trained on the following datasets:<br><br></p> <table> <tbody> <tr> <td>Dataset </td> <td>Task</td> <td>Model Details on Zenodo</td> </tr> <tr> <td> <a href="../record/6802614">TotalSegmentator</a> and <a href="../record/5903672">FLARE21</a> datasets</td> <td>Segment Liver from CT scans</td> <td>https://zenodo.org/record/8274976</td> </tr> <tr> <td><a href="https://kits-challenge.org/kits23/">KiTS23</a> datasets and a subset of the<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=5800386#5800386566e265abf95408aa64c4917f0cbe5d9"> TCGA-KIRC </a>dataset</td> <td>Segment Kidney, Cyst, and Tumors from CT Scans</td> <td>https://zenodo.org/records/8277846</td> </tr> <tr> <td><a href="http://ji%20yuanfeng.%20(2022).%20amos%20a%20large-scale%20abdominal%20multi-organ%20benchmark%20for%20versatile%20medical%20image%20segmentation%20[data%20set].%20zenodo.%20https">AMOS</a> and <a href="http://macdonald,%20jacob%20a.,%20zhu,%20zhe,%20konkel,%20brandon,%20mazurowski,%20maciej,%20wiggins,%20walter,%20&%20bashir,%20mustafa.%20(2020).%20duke%20liver%20dataset%20(mri)%20v2%20(2.0.0)%20[data%20set].%20zenodo.%20https//doi.org/10.5281/zenodo.7774566">DUKE Liver</a> datasets</td> <td>Segment Liver from the MR scans</td> <td>https://zenodo.org/record/8290124</td> </tr> <tr> <td>Data from m <a href="../record/6624726">pi-cai</a></td> <td>Segment Prostate region from MR scans</td> <td>https://zenodo.org/record/8290093</td> </tr> </tbody> </table>
Data from: facial growth and development trajectories based on 3D images: geometric morphometrics with a deformation perspective
<p>Developmental changes of facial shape are commonly investigated through geometric morphometrics. A limitation with this approach is the inability to investigate patterns of morphological changes at local scale. This could be addressed through quantifying the deformation required to deform one shape to another. This study aimed to investigate changes in mean, rate, and variance of facial shape at local scale using geometric morphometrics through deformation perspective. 2112 Europeans 3 to 40 years-old from the 3D Facial Norms project were included. Shape and rate trajectories from partial least-squares regressions revealed that the developmentally protrusive nasal bridge was due to local expansion in surrounding tissues as opposed to shape changes in nasal bridge per-ser. Local expansion of the supraorbital region, in particular the medial part in males, resulted in the sloping forehead and deep-situated eyes with development. Facial shape variation increased non-linearly with age (p < 0.05), with features having larger rate of change becoming more developmentally diversified. In summary, our deformation perspective facilitates unravelling morphogenetic processes underlying shape changes. Our extended analytical scope inspires novel measures worthy of consideration while establishing facial growth charts. The analytical framework in this study is broadly applicable for analysis of shape changes in general.</p>
BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
<p>The use of patient-derived material and immune cell co-cultures in modeling immune-oncology has gained significant interest for understanding and manipulating immune cell tumor targeting in a patient-specific context. However, current protocols have limitations in visualizing and analyzing the dynamic cellular features of these living culture systems. We recently developped a workflow names BEHAV3D, that combines multi-color live 3D imaging and computational tools to analyze cell death dynamics, classify T cell behavior, and generate data-informed 3D images and videos. Here we provide some example pre-processed dataset of videos of two co-culture set ups: breast cancer Patient Derived Organoids with αβ T cells engineered to express a γδ TCR (TEGs) and Acute Lymphoblastic Leukemia cells with CD19 CART cells.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
Perceptual maps of Heliconiini butterflies: images, 3D spaces, 2D maps, and mimicry ring listings
<h3>Summary</h3> <p>This repository contains<strong> images</strong>, <strong>3D animated spaces</strong>, <strong>2D perceptual maps with GMM</strong>, and <strong>mimicry ring lists</strong> for heliconiine butterflies complementing the analyses presented in this research paper:<em><strong> </strong>"Doré et al., 2025 - Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep."</em>.</p> <h3>Abstract</h3> <p> In 1879, Fritz Müller formulated the first mathematical evolutionary model to explain mutualistic mimicry between coexisting defended prey. Yet, the degree to which local mimicry drives the structure of prey aposematic signals at continental scale remains unclear, because the perception of pattern similarity has never been assessed at large spatial scale. Here, we implement a Citizen Science survey to quantify and analyze the structure of perceived variation in the wing patterns of heliconiine butterflies (Nymphalidae: Heliconiini) throughout the entire Neotropics. Despite a continuum of perceived wing patterns at the continental scale, we show that the convergence of sympatric species into discrete mimicry rings is ubiquitous among communities. These results expand Müller’s historical predictions by supporting the rampant convergence of prey signals across an entire continent. </p> <h3>Contents</h3> <p>This repository contains three folders:</p> <ul> <li><em>"3D_maps"</em> contains the <strong>animated 3D perceptual spaces</strong> of heliconiine wing patterns for the Citizen Science dataset (N = 432) and the Local reference for the five local communities highlighted in the article.</li> <li><em>"Clustering"</em> contains the <strong>2D perceptual maps</strong> and associated <strong>lists of mimicry rings</strong> built for each of the five local communities, for different level of clustering from GMM (K from 5 to 10).</li> <li><em>"Images"</em> contains the 432 <strong>images of dorsal wing patterns</strong> of heliconiine butterflies used in the online survey (<a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>) designed for this study.</li> </ul> <h3>How to cite</h3> <p>Please cite this research article as:</p> <blockquote> <p>Doré, M., Pérochon, E., Aubier, T.G., Le Poul, Y., Joron, M., Elias, M., 2025. Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep. <a href="https://doi.org/TBA">https://doi.org/TBA</a></p> </blockquote> <h3>Associated ressources</h3> <p>The source codes for the analyses carried out in the study are available on <a href="https://github.com/MaelDore/Perceptual_map_Heliconiini">GitHub</a>.<br> <br>The occurrences data and distribution maps used in this study are publicly available from Zenodo: Occurrences data at <a href="https://doi.org/10.5281/zenodo.10906853">https://doi.org/10.5281/zenodo.10906853</a>; Distribution maps at <a href="https://doi.org/10.5281/zenodo.10903661">https://doi.org/10.5281/zenodo.10903661</a>.</p> <p>The online Citizen Science survey on the perception of mimicry in wing color patterns of heliconiine butterflies is temporary available at <a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>.<br>Source code for the online Citizen Science survey are accessible on <a href="https://github.com/MaelDore/Memometic_website">GitHub</a>.</p>
Data for preprint: "Non-Telecentric 2P microscopy for 3D random access mesoscale imaging "
<p>Numerical data used in latest version of preprint: "Non-Telecentric 2P microscopy for 3D random access mesoscale imaging ", https://www.researchsquare.com/article/rs-121292/v1</p>
Source data for "Non-Telecentric two-photon microscopy for 3D random access mesoscale 2 imaging"
<p>Source data used in a manuscript "Non-Telecentric two-photon microscopy for 3D random access mesoscale 2 imaging"</p> <p> </p> <p> </p> <p> </p>
VesselExpress: Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs
<p>This dataset contains raw, segmented and skeletonized 3D light-sheet microscopic image volumes of blood vessels of different organs which were processed by VesselExpress. Please find the software here: https://github.com/RUB-Bioinf/VesselExpress. For details on how to run and setup the software please watch our tutorial (https://youtu.be/a8GWVKJNh68).</p>
Data and code for "High-speed 3D imaging flow cytometry with optofluidic spatial transformation"
<p>Data and codes used in Ugawa & Ota, "High-speed 3D imaging flow cytometry with optofluidic spatial transformation".</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.