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24 results for “multi-resolution”
Multi-Resolution Basemap of Northwest Aeolis Mons, Gale Crater, Mars
<p>This multi-resolution, multi-spectral basemap comprises HiRISE, CTX, MOC-NA, and CRISM imagery over the northwest of Aeolis Mons in Gale crater, Mars, to complement the HiRISE and CTX data over Sakarya Vallis (Persaud 2021, https://doi.org/10.5281/zenodo.5808371; Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808357). All of the products have been georeferenced to the CTX ORI mosaic (Persaud et al. 2021) and/or each other using QGIS and GDAL; their IDs, resolutions, and sources prior to this alignment are listed below.</p> <p>The greyimage ORIs produced in this work were generated using the Ames Stereo Pipeline. The other HiRISE ORI were selected based on the data used by the USGS to make the MSL orthophoto mosaic (Calef III and Parker 2016, https://astrogeology.usgs.gov/search/map/Mars/MarsScienceLaboratory/Mosaics/MSL_Gale_Orthophoto_Mosaic_10m_v3).</p> <p>The four CRISM images were processed using the CRISM Analysis Tool (CAT) in ENVI (Morgan et al. 2009) following the workflow described in Campbell and Muller (2017), adapted from Seelos (2009). Four RGB products were assembled from one CRISM image over Sakarya Vallis using the spectral library from Viviano-Beck et al. (2014); the band assignments are listed below.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)</p> <p>HiRISE (0.25 m/px)</p> <ul> <li>ESP_012340_1750_RED, this work</li> <li>PSP_009149_1750_RED, this work</li> <li>ESP_018854_1755_RED, this work</li> <li>ESP_019698_1750_RED, U. Arizona</li> <li>ESP_012551_1750_RED, U. Arizona</li> <li>ESP_024300_1755_RED, U. Arizona</li> <li>PSP_001488_1750_RED, U. Arizona</li> <li>PSP_009650_1755_RED, U. Arizona</li> </ul> <p>HiRISE RGB (0.50 m/px):</p> <ul> <li>ESP_061750_1750_RGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>ESP_069031_1750_MRGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>PSP_009149_1750_MRGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>ESP_029034_1750_MRGB, U. Arizona</li> </ul> <p>MOC-NA (NASA/JPL/MSSS):</p> <ul> <li>S2200845 (2.2 m/px)</li> <li>R1100953 (6.75 m/px)</li> </ul> <p>CRISM, FRT000095ee (18 m/px):</p> <ul> <li>Hydrated mineralogy <ul> <li>Red: SINDEX2</li> <li>Green: BD2100_2</li> <li>Blue: BD1900_2</li> </ul> </li> <li>Hydrated sulphates (1) <ul> <li>Red: SINDEX2</li> <li>Green: BD1900R2</li> <li>Blue: HCPINDEX3</li> </ul> </li> <li>Mafics <ul> <li>Red: LCPINDEX3</li> <li>Green: HCPINDEX3</li> <li>Blue: OLINDEX3</li> </ul> </li> <li>Hydrated sulphates (2): SINDEX2</li> </ul> <p> </p>
A multi-resolution, multi-epoch low Radio Frequency Survey of the Kepler K2 Mission Campaign 1 Field
<p>Data abstract:</p> <p>Contained within are the MWA images used as input data for this study. The production and analysis of these images are described in the linked paper. The final catalogues and light curves are available from VizieR (http://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=J/AJ/152/82).</p> <p>Paper abstract:</p> <p>We present the first dedicated radio continuum survey of a Kepler K2 mission field, Field 1, covering the North Galactic Cap. The survey is wide field, contemporaneous, multi-epoch, and multi-resolution in nature and was conducted at low radio frequencies between 140 and 200 MHz. The multi-epoch and ultra wide field (but relatively low resolution) part of the survey was provided by 15 nights of observation using the Murchison Widefield Array (MWA) over a period of approximately a month, contemporaneous with K2 observations of the field. The multi-resolution aspect of the survey was provided by the low resolution (4‧) MWA imaging, complemented by non-contemporaneous but much higher resolution (20″) observations using the Giant Metrewave Radio Telescope (GMRT). The survey is, therefore, sensitive to the details of radio structures across a wide range of angular scales. Consistent with other recent low radio frequency surveys, no significant radio transients or variables were detected in the survey. The resulting source catalogs consist of 1085 and 1468 detections in the two MWA observation bands (centered at 154 and 185 MHz, respectively) and 7445 detections in the GMRT observation band (centered at 148 MHz), over 314 square degrees. The survey is presented as a significant resource for multi-wavelength investigations of the more than 21,000 target objects in the K2 field. We briefly examine our survey data against K2 target lists for dwarf star types (stellar types M and L) that have been known to produce radio flares.</p>
WRF Forecast Data used for Verification of multi-resolution model forecasts of heavy rainfall events of 23rd-26th August 2017 over Nigeria
<p>A deterministic Weather Research and Forecasting model version 4.2 forecast of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) within the period 23<sup>rd</sup>-26<sup>th</sup> August 2017 over Nigeria. The model was setup to perform two nested domain simulations with 18 (parent domain), 6 and 2 km (hereafter WRF18, WRF6 and WRF2) horizontal resolutions. The outer domain covers West Africa and the innermost domain, which runs at convection-permitting scale, focuses on Nigeria. When interpreting the results, it is worthy of note that the data has been regridded to 18 km, which is 3 x the grid scale for WRF6 and 9 x the grid scale for WRF2. This means that there is a fair degree of smoothing that has been applied using a bilinear regridding process to get the models onto a level playing field. Only WRF18 retains its native grid and has not benefited from any additional smoothing.</p> <p>The WRF model setup is similar to the study of Gbode et al. (2019; DOI: https://doi.org/10.1007/s00704-018-2538-x) in terms of the model physics combination used in the model simulations. The parameterization schemes used are the Goddard (GD) WRF model microphysics (MP), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) and the Bett-Miller-Janjic (BMJ) cumulus convection (CU) parameterization schemes. This combination was found to reproduce realistic rainfall and temperature relative to gridded observations over West Africa. The GD is a six-class microphysics with graupel and modifications for ice/water saturation. MYJ is a local closure scheme that predicts turbulent kinetic energy and the BMJ CU is a profile adjustment scheme that relaxes both deep and shallow profiles toward a reference profile without explicit updraft, downdraft, or cloud entrainment. However, the CU scheme was turned off in the 2 km domain to explicitly represent convection.</p>
Multi-resolution X-Ray micro-CT images of Bentheimer Sandstones
<p>This dataset consists of multi-resolution X-Ray micro-tomography images of two Bentheimer sandstone rock cores. The rock cores were first used experimentally in [1] with further modelling in [2]. This new dataset is used directly in the publication [3] - preprint available at https://arxiv.org/abs/2111.01270. </p> <p>The original dataset from [1] (of the same rock cores) is hosted on the BGS National Geoscience Data Centre, ID #130625 at dx.doi.org/10.5285/5f899de8-4085-4370-a45e-e613f27e8f1d and there is also a subvolume image dataset, for easier download available on the Digital Rocks Portal, project 229, DOI:10.17612/KT0B-SZ28 at digitalrocksportal.org/projects/229. </p> <p>The images provided herein are from two distinct Bentheimer rock cores -- core 1 and core 2. The cores have diameter, 12.35mm, lengths 73.2mm and 64.7mm, core-averaged porosities of 0.203 and 0.223 and permeabilities of 1.636D and 0.681D for core 1 and 2, respectively. Core 2 has a clear low permeability lamination occurring at 2/3 of the total core length, whereas core 1 has a general fining towards the outlet of the core creating a reduction in porosity [1].</p> <p>The images were acquired with a Zeiss Versa 510 X-Ray CT scanner. We acquired images of two sub volumes from each core, at locations 1/3rd (subvolume 1) and 2/3rds (subvolume 2) of the way along the core length, at resolutions of 2, 6 and 18 microns. We refer to the 2 micron images as high-resolution (HR), the 6 micron images as low-resolution (LR) and the 18 micron images as very-low-resolution (VLR). There are also super-resolution (SR) images created at 2 micron resolution from the LR images, using a deep-learning algorithm. There are also cubic interpolation images created from the LR image - these are labels bicubic. These have a resolution of 2 microns, and size equal to the HR and SR images. Details of the SR and LR Bicubic generation are found in [3]. The following scanning protocols were used for the direct imaging:</p> <p>2 micron images:<br> --We use a 4x microscope objective, an exposure time of 8s, 2x averaged binning, 9001 projections, a scan voltage of 80kV and a power of 7W. Each scan takes approximately 24 hours.</p> <p>6 micron images:<br> --We use a flat panel detector, an exposure time of 0.7s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 14.46 degrees and the fan angle is 22.2 degrees. Each scan takes approximately 1 hour.</p> <p>18 micron images:<br> --We use a 0.4x microscope objective, an exposure time of 1s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 12.65 degrees and the fan angle is 12.65 degrees. Each scan takes approximately 2 hours.</p> <p>We present 4 sets of the images with different levels of processing. All images are mutual registered to each other. Each image filename has a Core#_Subvol#_resolution identifier, either with the actual resolution (e.g. 6) or the short form (e.g. LR). The following name endings are used</p> <p>(1) - '_16bit_LE.raw'. These are the .raw images of little-endian format. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(2) - '_16bit_LE_normalised.raw'. These are the .raw images of little-endian format with normalised greyscale values following the procedure in [1]. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(3) - 'Core1_Subvol1_HR' etc. These are the .tiff images of (2) above, which have been converted to 8 bit. Includes bicubic interpolation images and SR images, but no 16 micron images, since these were not used in the analysis of [3]. 16 images in total. </p> <p>(4) - 'Core1_Subvol1_HR_filtered' etc. These are the .tiff images from (3) above, which have filtered using non-local means filtering. More details are found in [3]. Note there are no SR images here since they are already essentially filtered, and included in (3) above. 12 images in total.</p> <p><br> <strong>References</strong><br> <br> [1] Jackson, S.J., Lin, Q. and Krevor, S. 2020. Representative Elementary Volumes, Hysteresis, and Heterogeneity in Multiphase Flow from the Pore to Continuum Scale. Water Resources Research, 56(6), e2019WR026396</p> <p>[2] Zahasky, C., Jackson, S.J., Lin, Q., and Krevor, S. 2020. Pore network model predictions of Darcy‐scale multiphase flow heterogeneity validated by experiments. Water Resources Research, 56(6), e e2019WR026708.</p> <p>[3] Jackson, S.J, Niu, Y., Manoorkar, S., Mostaghimi, P. and Armstrong, R.T. 2021. Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling. Under review, preprint available at https://arxiv.org/abs/2111.01270 </p>
Fire Season Multi-Resolution Database
<p>In the realm of wildfire research and analysis, the need for comprehensive and adaptable datasets has never been more critical. To address this imperative, we introduce the "Fire Season Multi-Resolution Database." This meticulously curated dataset represents a significant advancement in the realm of wildfire data, offering a versatile, multi-resolution approach to understanding the intricate dynamics of fire seasons.</p> <p>The Fire Season Multi-Resolution Database is a set of geospatial earth grids, thoughtfully designed to accommodate diverse research needs by providing information at varying levels of detail. Within each grid, a wealth of data is embedded, concerning fire seasons and their comprehensive characterizations at every individual pixel.</p> <p>Within each pixel of these multi-resolution grids, a set of nine variables about their respective fire seasons can be found.<br> </p> <ul> <li> FsOrNot (int): a binary variable (0,1) that contains information whether a pixel has a fire season or not </li> <li> MainStart (int): Starting month of the fire season </li> <li> MainEnd (int) : Ending month of the fire season </li> <li> SecondStart (int): Start month of a possible secondary fire seasson (if it is bimodal) </li> <li> SecondEnd (int) : End month of the secondary fire seasson </li> <li> Length (int): Months that lasts the fire season </li> <li> C (float): Seasonal concentration of the fire seasson </li> <li> P (float): Seasonal phase or timming of the fire season </li> <li> FBA (float): Fraction of Burneable Area </li> </ul> <p> </p> <p>Github used to build these files: </p> <p>https://github.com/delatorre96/Development-of-a-multi-resolution-fire-season-database-on-a-global-scale</p> <p>Finally, the sources of information used to build this dataset is the Copernicus project, specifically the web page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-fire-burned-area?tab=overview. This dataset has been constructed with the aim of enhancing wildfire research and aiding in the development of effective wildfire management and mitigation strategies. </p>
Feature-based multi-resolution registration of immunostained serial sections - online material
<p>This is the supplementary online material, including full data, evaluation, and executables, for the paper "Feature-based multi-resolution registration of immunostained serial sections" that appeared in Medical Image Analysis, Volume 35, January 2017, Pages 288–302.</p> <p>Same material was deposited online under https://gdv-server.inf.uni-bayreuth.de/gdvcloud/index.php/s/NnSov0O65n9Gp01 </p> <p>We also include here further supplementary files deposited at the journal page (http://www.sciencedirect.com/science/article/pii/S136184151630127X) under CC-BY licence. See there for the definite version of the paper or http://www.mathematik.uni-marburg.de/~lobachev/papers/lobachev-media16-registration-preprint.pdf for the preprint.</p>
D-PLACE dataset derived from 'Global Multi-resolution Terrain Elevation Data 2010'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Global Multi-resolution Terrain Elevation Data courtesy of the U.S. Geological Survey (Downloaded 14 Jul 2014)</p> </blockquote>
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
<p>A <a href="https://www.sciencedirect.com/topics/engineering/photovoltaics">photovoltaic</a> (PV) dataset from satellite and aerial imagery. The dataset includes three groups of PV samples collected at the spatial resolution of 0.8m, 0.3m and 0.1m, namely PV08 from Gaofen-2 and Beijing-2 imagery, PV03 from aerial photography, and PV01 from UAV orthophotos. PV08 contains rooftop and ground PV samples. Ground samples in PV03 are divided into five categories according to their background land use type: shrub land, grassland, cropland, saline-alkali, and water surface. Rooftop samples in PV01 are divided into three categories according to their background roof type: flat concrete, steel tile, and brick. Data document can refer to the preprint https://essd.copernicus.org/preprints/essd-2021-270/</p>
Dataset: Multi-resolution 3D rendering for high-performance Web AR
<p>The input data are the following four models: (i) Middlebury TempleRing [1] (ii) Human of CoRBS dataset [2]; (iii) Anton Memorial of Harvest4D consortium [3], and; (iv) Meteora site by Meteora Project [4]. The 3D files are converted into the multiresolution format NXS of Nexus.js library [5], in its compressed format NXZ, in the compressed DRC format of Draco library [6] and in the binary gtTF format (GLB) with some optimizations using meshoptimizer library [7]. </p> <p> </p> <p> </p> <ol> <li>Nair, N.S.; Nair, M.S. Multi-View Stereo Using Graph Cuts-Based Depth Refinement. <em>IEEE Signal Process. Lett</em>. <strong>2022</strong>, <em>29</em>, 1903–1907.</li> <li>Wasenmüller, O.; Meyer, M.; Stricker, D. CoRBS: Comprehensive RGB-D Benchmark for SLAM Using Kinect V2. In Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), New York, USA, 7 – 10 March 2016, pp. 1–7.</li> <li>Harvest 4D. Available Online: <a href="https://harvest4d.org">https://harvest4d.org</a> (accessed on 23 June 2023)</li> <li>Ioannidis, C.; Tallis, I.; Pastos, I.; Boutsi, A.M.; Verykokou, S.; Soile, S.; Tokmakidis, P.; Tokmakidis, K. A WEB-BASED PLATFORM FOR MANAGEMENT AND VISUALIZATION OF GEOMETRIC DOCUMENTATION PRODUCTS OF CULTURAL HERITAGE SITES, <em>ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci</em> <strong>2021</strong>, <em>V–2–2021</em>, 113–120.</li> <li> <p>Ponchio, F.; Dellepiane, M. Fast Decompression for Web-Based View-Dependent 3D Rendering. In Proceedings of the 20th International Conference on 3D Web Technology - Web3D ’15, Heraklion, Greece, 18-21 June 2015; pp. 199–207.</p> </li> <li> <p>Draco 3D data compression. Available online: https://google.github.io/draco/ (accessed on 23 June 2023)</p> </li> <li> <p>Meshoptimizer. Available online: <a href="https://github.com/zeux/meshoptimizer/tree/master">https://github.com/zeux/meshoptimizer/tree/master</a> (accessed on 23 June 2023)</p> </li> </ol>
WRF data for downscaling, used in Learned multi-resolution dynamical downscaling for precipitation
<p>This study uses regional climate model (RCM) simulated precipitation at low and high spatial resolution, to develop convolution neural network (CNN) based approaches, that can emulate high resolution modeled data using low resolution modeled data with cheaper computational resource than running dynamical downscaling at the high spatial resolution. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and train each CNN type both with a conventional loss function, such as Mean Square Error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high resolution precipitation with precipitation generated from a bi-linear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique, using the original high resolution precipitation from the RCM as ground truth. We find that SR technique produces similar results to the interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than ground truth shows. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique, their predictions are still not as close as ground truth. The CNNs trained by CGAN generate more realistic and physically reasonable results. This advanced technique improves not only the data variability in time and space, and but also the extremes, such as intense and long-lasting events, based on event-feature tracking algorithm.</p>
Seminar - Dr Thomas Beaney - Simplifying complexity: generating multi-resolution clusters of chronic diseases in 10 million people in England
<p>Recording of the presentation given on Wednesday 23rd October by Dr <span>Thomas</span> <span>Beaney</span> “Simplifying compl<span>e</span>xity: g<span>e</span>n<span>e</span>rating multi-r<span>e</span>solution clust<span>e</span>rs of chronic dis<span>e</span>as<span>e</span>s in 10 million p<span>e</span>opl<span>e</span> in <span>E</span>ngland”<br><br>In this talk, Tom discusses an approach for g<span>e</span>n<span>e</span>rating clust<span>e</span>rs of chronic dis<span>e</span>as<span>e</span>s in p<span>e</span>opl<span>e</span> with Multipl<span>e</span> Long-T<span>e</span>rm Conditions (MLTC), which combin<span>e</span>s natural languag<span>e</span> proc<span>e</span>ssing and n<span>e</span>twork-bas<span>e</span>d clust<span>e</span>ring. H<span>e</span> also discusses th<span>e</span> chall<span>e</span>ng<span>e</span>s of <span>e</span>valuating and using clust<span>e</span>rs in practic<span>e</span> to influ<span>e</span>nc<span>e</span>pati<span>e</span>nt car<span>e</span>.</p> <p>You can view the publication of his work here: https://www.nature.com/articles/s43856-024-00529-4</p>
Dataset presented in the recently submitted AGU manuscript "A multi-resolution finite-element approach for global electromagnetic induction modeling with application to southeast China coastal geomagnetic observatory studies"
<p>Dataset presented in the recently submitted AGU paper "A multi-resolution finite-element approach for global electromagnetic induction modeling with application to southeast China coastal geomagnetic observatory studies"</p>
BrainSTEM: A multi-resolution fetal brain atlas to assess the fidelity of human midbrain cultures
<p>This upload contains data objects associated with our paper "BrainSTEM: A multi-resolution fetal brain atlas to assess the fidelity of human midbrain cultures".</p> <p>In particular, this upload contains the in-house single-cell dataset (toh.inhouse) and 12 single-cell datasets from published literatures, totalling 13 query datasets, as R object files.</p> <p>Detailed information about the publicly available query datasets can be found in the Supplementary Table 6 of the paper abovementioned.</p> <p> </p> <p> </p>
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
<p>This is the data set for experiments of satellite images of Oroville dam site in paper: Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics. Inside the zip file, there are 3 folders: 'HD-IMG-Database-Landsat-8', 'HD-IMG-Database-Landsat-8-Qest' and 'MODIS_250'.</p>
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation
GEO Series GSE173778. Mus musculus. 34 samples. Type: Expression profiling by high throughput sequencing.
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (Spatial scRNA-Seq)
GEO Series GSE173776. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
BrainSTEM: A multi-resolution fetal brain atlas to assess the fidelity of human midbrain cultures
GEO Series GSE281535. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scMARS-Seq)
GEO Series GSE173773. Mus musculus. 26 samples. Type: Expression profiling by high throughput sequencing.
M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scRNA-Seq)
GEO Series GSE173774. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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.