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303 results for “hyperspectral”
Underwater hyperspectral scan from India, scene 31
<p>Underwater hyperspectral scan from India, scene 31</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 18
<p>Underwater hyperspectral scan from India, scene 18</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 25
<p>Underwater hyperspectral scan from India, scene 25</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 22
<p>Underwater hyperspectral scan from India, scene 22</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 9
<p>Underwater hyperspectral scan from India, scene 9</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 12
<p>Underwater hyperspectral scan from India, scene 12</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 1
<p>Underwater hyperspectral scan from India, scene 1</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Underwater hyperspectral scan from India, scene 30
<p>Underwater hyperspectral scan from India, scene 30</p> <p>Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.</p>
Hyperspectral Image of a 6 Metre Genealogical Roll as a Video
<p>This video scrolls down the length of the 6 metre genealogical roll, whilst also passing through the wavelength range of the hyperspectral image. The image is made from multiple hyperspectral scans of a medieval genealogical roll tracing the lineage of England’s Plantagenent rulers back to Adam and Eve (<a href="https://ucldigitalpress.co.uk/Book/Article/2/9/18/">https://ucldigitalpress.co.uk/Book/Article/2/9/18/</a>).</p> <p>More info on the imaging pipeline is available here <a href="https://zenodo.org/record/1312942#.W7yAVGhKhPa">https://zenodo.org/record/1312942#.W7yAVGhKhPa</a>.</p> <p>Images courtesy of Special Collections, UCL Library Services.</p>
Processed airborne hyperspectral images and mosaics of a Paranapanema River region in Capivara reservoir, Brazil
<p>This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm): 505.37, (9.51), 515.31 (14.05), 528.55 (14.82), 539.87 (14.03), 546.99 (14.31), 554.99 (13.26), 560.22 (12.11), 570.44 (14.31), 579.58 (13.26), 592.57 (16.57), 605.73 (14.98), 620.22 (16.26), 625.92 (15.47), 655.06 (12.55), 665.72 (15.59), 670.03 (15.74), 681.33 (15.89), 695.04 (14.96), 700.46 (15.44), 707.96 (15.32), 715.19 (15.37), 725.37 (14.72), 737.29 (14.98), 749.87 (15.08) and 780.1 (14.72). </p> <p>The external orientation parameters (EOP) are acquired by dual frequency GPS and adjusted with tie points computed by bundle adjustment. We perform individual georeferencing on each individual image.</p> <p>This dataset present processing using nine approaches to mosaicking individual georeferenced images. Three of then are new proposed methods developed by the authors. Details of processing and methodology are described on the oficial paper (currently in review process of journal).</p> <p>The authors thank the Graduate Program in Cartographic Sciences (PPGCC) of the School of Science and Technology (UNESP), campus Presidente Prudente, for allowing the development of this research; the National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for financial assistance dedicated to the project. The authors extend special thanks to the São Paulo Research Foundation (FAPESP) for financial support for the hyperspectral camera (2013/50426-4).</p> <p> </p>
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
<p>Published online: <a href="https://www.mdpi.com/2072-4292/11/19/2326">https://www.mdpi.com/2072-4292/11/19/2326</a></p> <p>DOI: 10.3390/rs11192326</p> <p><strong>Abstract:</strong></p> <p>In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data</p> <p>Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".</p> <p>Code is available in a Jupyter Notebook and can be found here: <a href="https://github.com/jonathanventura/canopy">https://github.com/jonathanventura/canopy</a></p> <p>National Ecological Observatory Network. 2018. Provisional data downloaded from <a href="http://data.neonscience.org/">http://data.neonscience.org</a> on 22 June 2018. Battelle, Boulder, CO, USA</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism
<h2>Abstract</h2> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance hyperspectral signature, library, sediment, sand, sea cucumber, sea star, coral, seaweed</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.25m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Underwater hyperspectral data of Vigo survey fieldwork Sept. 2023 (Reflectance Converted)
<h2>Abstract</h2> <p>Reflectance converted data of UHI data from Vigo fieldwork Sept 2023 survey. Data were recorded at three different sea zones in Vigo.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Underwater hyperspectral data of Vigo survey fieldwork Sept 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Reflectance converted data of UHI data from Vigo fieldwork Sept 2023 survey. Data were recorded at three different sea zones in Vigo.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance estimated underwater hyperspectral, UHI, seabed, mineral, sea region</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Report</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Bio-geographical regions</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>30.11.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>30.11.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>1.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Underwater hyperspectral data of Vigo survey fieldwork Sept. 2023 (Radiance Converted)
<h2>Abstract</h2> <p>Radiance converted data of UHI data from Vigo fieldwork Sept 2023 survey. Data were recorded at three different sea zones in Vigo.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Underwater hyperspectral data of Vigo survey fieldwork Sept 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Radiance converted data of UHI data from Vigo fieldwork Sept 2023 survey. Data were recorded at three different sea zones in Vigo.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Radiance converted underwater hyperspectral image data, UHI, seabed, mineral, sea region</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Report</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Bio-geographical regions</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>30.11.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>30.11.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>1.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Hyperspectral Unmixing Dataset of UAV Gathered Blueberry Field Data
<p>Hyperspectral Unmixing dataset created from hyperspectral data gathered usign SPECIM push-broom hyperspectral camera mounted on a UAV flying over blueberry fields in Lithuania. Created dataset contains six data classes and linear mixtures from raw data. All data is given in Python Numpy array .npy files. </p> <p>To keep the annonimity of data owners only the non georectified data cubes are published.</p> <p>Dataset includes three hyperpectral data cubes of blueberry fields and Dark reference cube to show camera noise.</p> <p><strong>Data structure:</strong></p> <p>cube_1, cube_2, cube_2 and Dark - folder with hyperspectral data.</p> <p>calibration_data.npy - Data of calibration plates (with 40%, 10% and 5% reflectance values) from hyperspectral flight that were used to conver DN to reflectance.</p> <p>endmembers.npy - Spectra of siz endmembers (classes) used in the dataset.</p> <p><strong>cube_x folders include:</strong></p> <p>class_matrix.npy - Numpy matrix file of hyperspectral image classes (classification results)</p> <p>raw_data.npy - Hyperspectral cube created from raw camera data (with DN values)</p> <p>data_cube_3_0.npy and abundances_3_0.npy - Classified and mixed (using slidin window of 3x3 pixels with no overlap) hyperspectral data cube and class abundance matrix. </p> <p>endmember_errors.npy - matrix of variation for each of endmembers in the hyperspectral cube (used for evaluation mostly.)</p> <p><strong>Dark folder:</strong></p> <p>includes data folder with raw-dark_fl1_20230830_140006_radiance.dat and .hdr ENVI raster data files (library like <em>rasterio</em> for Python can used to read these files). This is the dark (0% reflectance) data cube and header file used in calibration.</p>
Exploring the potential of Near Infrared Hyperspectral Imaging and chemometrics to discriminate soil seed bank of two timber species central African : Erythrophleum suaveolens (Guill. & Perr.) Brenan, and Erythrophleum ivorense A. Chev.
<p>The data of this study are accessible by sending a request to the corresponding author at the email address: douhch382@gmail.com. <a href="https://doi.org/10.5281/zenodo.13908452" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13908452</a></p>
Images for a publication "Lossless Hyperspectral Image Compression in Comet Interceptor and Hera Missions with Restricted Bandwith" by Skog et al.
<p>This archive contains following simulated hyperspectral datacubes for ASPECT (Asteroid Spectral Imager) instrument on ESA Hera mission used in the manuscript:</p> <ul> <li>ASPECT_simulated_data_Vis.zip - ASPECT visible channel datacubes noisless and with simulated instrument noise for three exposure times indicated in the file name.</li> <li>ASPECT_simulated_data_NIR.zip - ASPECT near-infrared channels datacubes noisless and with simulated instrument noise for three exposure times indicated in the file name.</li> <li>ASPECT_simulated_data_Vis_NIR_filtered.zip - Same ASPECT visible and near-infrared channels datacubes filtered with noise filters indicated in the file name. The visible datacubes have designation Vis in file name. The remaining datacubes without channel designation are near-infrared ones.</li> </ul> <p>The recorded scene and imaging distance is indicated in file name. D1 - Didymos asteroid, D2 - Dimorphos asteroid. All datacubes are in Matlab (.mat) format and with single wavelength .png preview included. For details please check associated manuscript.</p>
Four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018)
<p>These data were four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018). They were also the output results of the findings of Ma et al. (2021).</p>
WHU-OHS: A benchmark dataset for large-scale Hyperspectral Image classification
<p>The WHU-OHS dataset is made up of 42 OHS satellite images acquired from more than 40 different locations in China. The imagery has a spatial resolution of 10 m (nadir) and a swath width of 60 km (nadir). There are 32 spectral channels ranging from the visible to near-infrared range, with an average spectral resolution of 15 nm. We cropped each image into 512 × 512 pixels with a stride of 32. There are 4822, 513, and 2460 sub-images in the training, validation, and test sets, respectively.</p> <p>For transferability test, we choose eight pairs of OHS images, and each pair contains one source image (S) and one target image (T):</p> <p>S1: Changchun</p> <p>T1: Jilin</p> <p>S2: Wuxi</p> <p>T2: Shanghai</p> <p>S3: Guangzhou</p> <p>T3: Zhongshan</p> <p>S4: Xining</p> <p>T4: Lanzhou</p> <p>S5: Hetian</p> <p>T5: Kelamayi</p> <p>S6: Anyi</p> <p>T6: Nanchang</p> <p>S7: Changde</p> <p>T7: Changsha</p> <p>S8: Tianjin</p> <p>T8: Tangshan</p> <p>The 26 OHS images except for the eight pairs:</p> <p>O1: Baoding</p> <p>O2: Chongqing</p> <p>O3: Fujin</p> <p>O4: Huainan</p> <p>O5: Huhehaote</p> <p>O6: Jinzhong</p> <p>O7: Luliang</p> <p>O8: Manasi_1</p> <p>O9: Manasi_2</p> <p>O10: Nanmulin</p> <p>O11: Neimenggu</p> <p>O12: Qingdao</p> <p>O13: Qinghuangdao</p> <p>O14: Shawan</p> <p>O15: Shenyang</p> <p>O16: Shuozhou</p> <p>O17: Songpan</p> <p>O18: Taian</p> <p>O19: Tongjiang_1</p> <p>O20: Tongjiang_2</p> <p>O21: Wuzhong</p> <p>O22: Xundian</p> <p>O23: Xuzhou</p> <p>O24: Yidu</p> <p>O25: Zangzu</p> <p>O26: Zhongshan</p> <p>The image patches have been normalized and scaled by 10000 to reduce storage cost. Divide the pixel values by 10000 and then the image patches can be used directly.</p>
ScienceDex guides
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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.