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979 results for “Image Dataset”
(10)-Strobl2022A-DS0003 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0003 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0002 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0002 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0007 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0007 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0009 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0009 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0006 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0006 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0005 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Dataset for tumor infiltrating lymphocyte classification (304,097 image patches from TCGA)
<p>This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original images are from Abousamra et al. (2022) and Saltz et al. (2018), and the original whole slide images are from TCGA. This dataset is a subset of the data presented in Abousamra et al. (2022) (with new data partitions).</p> <p>If you use this dataset, please cite the following papers, as well as this Zenodo page.</p> <p>Abousamra, S., Gupta, M. D., Hou, L., Batiste, R., Zhao, T., Shankar, A., Rao, A., Chen, C., Samaras, D., Kurc, T., & Saltz, J. (2022). Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. <em>Frontiers in Oncology</em>, 5971. https://doi.org/10.3389/fonc.2021.806603</p> <p>Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., & Danilova, L. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. <em>Cell Reports</em>, <em>23</em>(1), 181-193.</p> <p> </p> <p>The acknowledgements from the <em>Frontiers in Oncology</em> and <em>Cell Reports</em> papers are included below:</p> <blockquote> <p>This work was supported by the National Institutes of Health (NIH) and National Cancer Institute (NCI) grants UH3-CA22502103, U24-CA21510904, 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 as well as generous private support from Bob Beals and Betsy Barton. AR and AS were partially supported by NCI grant R37-CA214955 (to AR), the University of Michigan (U-M) institutional research funds and also supported by ACS grant RSG-16-005-01 (to AR). AS was supported by the Biomedical Informatics & Data Science Training Grant (T32GM141746). This work was enabled by computational resources supported by National Science Foundation grant number ACI-1548562, providing access to the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center, and also a DOE INCITE award joint with the MENNDL team at the Oak Ridge National Laboratory, providing access to Summit high performance computing system. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.</p> </blockquote> <p> </p> <blockquote> <p>We are grateful to all the patients and families who contributed to this study. Funding from the Cancer Research Institute is gratefully acknowledged, as is support from National Cancer Institute (NCI) through U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845,U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672, U24CA180924, U24CA210950, U24CA215109, NCI Contract HHSN261201400007C, and Leidos Biomedical Contract 14X138. A.U.K.R. and P.S were supported by CCSG Bioinformatics Shared Resource P30 CA01667, ITCR U24 Supplement 1U24CA199461-01, a gift from Agilent technologies, CPRIT RP150578, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation XSEDE Science Gateways program under grant ACI-1548562 allocation TG-ASC130023. The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure and the Institute for Advanced Computational Science at Stony Brook University for access to the high-performance LIred and SeaWulf computing systems, the latter of which was supported by National Science Foundation grant (#1531492).</p> </blockquote> <p>------------------------------------</p> <p>This dataset includes 304,097 image patches. All images are 100 x 100 pixels at 0.5 micrometers per pixel. An image is TIL-positive if there are at least two TILs present.</p> <p>Refer to `images-tcga-tils-metadata.csv` for information about each image. That spreadsheet has the following columns:</p> <pre><code>partition,study,barcode,label,path,md5</code></pre> <p>Partition specifies which partition the image is part of (train, val, test). Study is the TCGA study the image is part of (e.g., acc for TCGA-ACC). Barcode is the TCGA participant barcode. This is used during partitioning, to ensure that images from the same participant are not present in different data partitions. Label is either til-negative or til-positive. An image is til-positive if there are at least two TILs in the image. Path is the path to the PNG image. All images are stored as PNG. Md5 is the md5 hash of the image. This can be used to ensure there are no duplicate images and to verify the integrity of images.</p> <p>There are study-specific directories in the directory `images-tcga-tils`, and there is a directory named `pancancer` that includes images from all the included TCGA studies. That directory uses symlinks to avoid storing duplicate data.</p> <p> </p>
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
<p>Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC\footnote{Available~at: \url{https://crisisnlp.qcri.org/medic/index.html}}, which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on \textit{multi-task learning}, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. <br> </p>
Chess piece dataset for image classification
<p>Chess piece dataset for image classification. Contains 4 different chess sets, 3 used for training and the remainder for validation purposes. Each chess piece from each set has been photograph by a static bird's eyes camera from each of the 64 squares that form a chess board. This way, each piece is seen from all its different angles. </p>
CoFly-WeedDB: A UAV image dataset for weed detection and species identification
<p>The CoFly-WeedDB contains 201 RGB images (~436MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5m and 3m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.</p>
Dataset of very-high-resolution satellite RGB images to train deep learning models to detect and segment high-mountain juniper shrubs in Sierra Nevada (Spain)
<p>This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.</p>
Dataset of very-high-resolution satellite RGB images to train deep learning models to recognize high-mountain juniper shrubs from Sierra Nevada (Spain)
<p>This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to recognize Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 2000 images (.jpg) of size 512x512 pixels partitioned into two classes: Shrubs and NoShrubs. We also provide partitioning of the data into Train (1800 images), Test (100 images), and Validation (100 images) subsets.</p>
Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider: calo-image datasets for the standard model
<p>An example calo-image dataset used in the <a href="https://arxiv.org/abs/2205.09876">paper</a>: standard model.</p> <p>Each shard is named `calo-image_sm_${DATASET}_${INDEX}.tar.gz`. Each contains one data file. DATASET is in {train,test,private_test} to label the three independent splits for training, validation, and testing respectively. INDEX labels separate batches which should be trivially combined.</p> <p>Each data file is in <a href="https://www.h5py.org/">h5</a> format. Its data are under the key "entries" as an array with shape (n, 32, 32) corresponding to (event_index, eta, phi) for the n calorimeter images in an unrolled eta--phi surface.</p> <p> </p>
Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider: calo-image datasets for lambdaPV=1
<p>An example calo-image dataset used in the <a href="https://arxiv.org/abs/2205.09876">paper</a>: PV-mSME with lambdaPV=1.</p> <p>Each shard is named `calo-image_pv_msme_1_${DATASET}_${INDEX}.tar.gz`. Each contains one data file. DATASET is in {train,test,private_test} to label the three independent splits for training, validation, and testing respectively. INDEX labels separate batches which should be trivially combined.</p> <p>Each data file is in <a href="https://www.h5py.org/">h5</a> format. Its data are under the key "entries" as an array with shape (n, 32, 32) corresponding to (event_index, eta, phi) for the n calorimeter images in an unrolled eta--phi surface.</p> <p> </p>
A Dataset of Synthetic Images of Outdoor Scenes Taken from Sidewalks, for Temporal Semantic Segmentation Applications
<p>This dataset has been generated using the CARLA simulator (release 0.9.11), an open-source 3D simulator for experiments in autonomous vehicle, based on the Unreal Engine game engine. It comes with pre-made city environment maps. CARLA is distributed with several integrated maps as well as parameters to increase the variety in the dataset. In the release that we have used, there are 13 semantic segmentation classes: None, Building, Fence, Other, Pedestrian, Pole, Lane-marking, Road, Sidewalk, Vegetation, Vehicle, Wall, and Traffic sign. The "None" category corresponds to textures that are not part of an object, such as lawns which are not part of "Vegetation", or sky. In the “Other” category are found objects that are not included in the other classes like plant and flower pots. For smart mobility applications, the “Sidewalks” and “Road” classes are of particular importance to find the way forward, as well as “Buildings” and “Poles” for obstacle avoidance. Sequences are made of 4 images. The dataset is composed of 46436 frames (11609 sequences) partitioned in 41024 frames (10256 sequences) for train, 2696 frames (674 sequences) for validation, and 2716 for test (679 sequences). The size of the images is 800 x 600 (resp. width x height).</p> <p>Additionaly, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames for train/validation/test is respectively 7288 (1822 sequences) partitioned in 6344 (1687 sequences) for train, 416 frames (104 sequences) for validation, and 424 for test (106 sequences). This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road and test on images with a viewpoint located on the sidewalk, and vice versa.</p>
Dataset for Crack Detection in Images of Bricks and Masonry Using CNNs
<p><strong>Dataset for training CNN built from aerial drone images of buildings in Hamburg</strong></p> <p>This dataset contains images extracted from aerial surveillance photos of the <a href="https://www.bing.com/ck/a?!&&p=b45d6a7b67c7b3c6JmltdHM9MTY1ODMzOTc0MCZpZ3VpZD04NTU2MjdiYS1kYjljLTQyOTMtOTFlOC0xYmM0NmE1ZWViOGMmaW5zaWQ9NTIyMQ&ptn=3&hsh=3&fclid=2a810abc-0855-11ed-8af8-d88619cb2403&u=a1aHR0cHM6Ly9kZS53aWtpcGVkaWEub3JnL3dpa2kvU3BlaWNoZXJzdGFkdA&ntb=1">Speicherstadt</a> and <a href="https://www.bing.com/ck/a?!&&p=750d82f6afc564d9JmltdHM9MTY1ODMzOTg0OSZpZ3VpZD1mOWMwYzE5OC01NjU3LTQ1NzMtOGE0YS1mNzYxN2VlOTlmMmEmaW5zaWQ9NTIxNA&ptn=3&hsh=3&fclid=6b854f27-0855-11ed-9794-60e2f5efc96c&u=a1aHR0cHM6Ly93d3cuaGFmZW5jaXR5LmNvbS9pbmZvY2VudGVyL2tlc3NlbGhhdXM&ntb=1">Kesselhaus</a> buildings in Hamburg, provided by the City of Hamburg. Original 834 high resolution images (5472 x 3648 pixels) have been separated into smaller images (227 x 227 pixels) of the size that could be processed using SqueezeNet, a deep Convolutional Neural Network (CNN). This resulted in more than 350 thousand images that had to be subsequently processed automatically to retain images containing solely bricks and mortar and concrete. The final stage contained tedious manual/visual verification of images and their separation into positive (containing cracks) and negative (clear bricks and mortars) sets of images. The final set contains nearly 40 thousand images.</p> <p>Since images extracted from Hamburg buildings contained only specific type of bricks and our intention was to extend the CNN to be able to deal with wider range of brick types as well as concrete surfaces, we added to our training set also images from the following Open Access databases (note that such images required resizing to 227 x 227 pixel size before use):</p> <ul> <li><a href="https://data.mendeley.com/datasets/5y9wdsg2zt/1">Concrete Crack Images for Classification (Mendeley Data)</a></li> <li><a href="https://zenodo.org/record/5108846#.YthGSLbP0bB">Dataset for Crack Detection in Images of Masonry Using CNNs</a></li> </ul> <p>Such a combined data set resulted in over 80 thousand of images.</p> <p><strong>Matlab WebApp Server application based on trained SqueezeNet CNN </strong></p> <p>The integrated database of images has been used to train the SqueezeNet CNN using a method proposed by <a href="https://www.linkedin.com/in/kenta-itakura-b88129202/">Kenta Itakura</a> in his article published on Matlab Central: <a href="https://www.mathworks.com/matlabcentral/fileexchange/75418-classify-crack-image-using-deep-learning-and-explain-why?s_tid=srchtitle">Classify crack image using deep learning and explain "WHY"</a>, which in turn is based on the work of <a href="https://ieeexplore.ieee.org/author/37280177000">Lei Zhang</a> reported in his IEEE article: <a href="https://ieeexplore.ieee.org/abstract/document/7533052">Road crack detection using deep convolutional neural network</a> published at <a href="https://ieeexplore.ieee.org/xpl/conhome/7527113/proceeding">2016 IEEE International Conference on Image Processing (ICIP)</a>.</p> <p>The "Matlab" subfolder contains the complete software to allow building the application to run under Matlab WebApps Server. The provided version of the "<em>netTransfer.mat</em>" file has been compiled for Matlab revision 2020b, but it should also work when compiled for other revisions from 2019a onwards. BTW, the original location of the files was "D:\Cracks (2-class)\". For instructions how to use the provided Matlab files, refer to Matlab instructions at <a href="https://www.mathworks.com/products/matlab-web-app-server.html">MATLAB Web App Server</a> and <a href="https://www.mathworks.com/help/webappserver/getting-started-with-matlab-web-app-server.html?s_tid=CRUX_lftnav">Get Started with MATLAB Web App Server</a>.</p> <p>After producing and uploading the application to the Matlab WebApps Server, the application can be found at http://localhost:9988/webapps/home/ if deployed locally. It can be also deployed on a WEB server, subject to installation of the compliant Matlab Runtime package on the custom server, whcih can be found at <a href="https://www.mathworks.com/products/compiler/matlab-runtime.html">MATLAB Runtimes (mathworks.com)</a>.</p> <p>The important function included in the package is "unscramble.m", which <strong>corrects the error that exists in all known revisions of Matlab</strong> in uploading images selected by open file function in the Matlab App Designer. The effect is that image is "scrambled beyond recognition" after uploading to the Matlab WebApps Server. Our function de-scrambles such images, converting them into their original form.</p>
EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping
<p>EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping </p> <p>The dataset can be unzipped and includes an image dataset and a vector dataset, which includes nine land use/land cover categories (i.e., cropland, orchard, forestland, grassland, construction land, transportation land, water body, bare land, terrace) for object-oriented remote sensing image mapping using deep learning.</p>
Cone-Beam Computed Tomography Dataset of a Chicken Bone Imaged at 4 Different Dose Levels
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a chicken leg bone imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is a chicken bone obtained from a cooked chicken. The bone was boiled to remove soft tissues, after which it was left to dry in room temperature for several months to remove extra moisture. For the scan the sample was placed directly into the rotation stage and secured with a screw.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings</em></p> <p>The dataset consists of four different scans of the same sample. For each scan 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were:</p> <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> <p>The scans were made in sequence, proceeding from the lowest dose to the highest dose.</p> <p><em>Data Post-Processing</em></p> <p>Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading
<p>Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files.</p> <p>Examples:</p> <ul> <li>S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel <ul> <li>Z-Stack <strong>was not</strong> activated inside acquisition experiment</li> </ul> </li> <li>S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels <ul> <li>Z-Stack <strong>was </strong>activated inside acquisition experiment</li> </ul> </li> </ul> <p>The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.</p> <p> </p> <p> </p> <p> </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.