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339 results for “Raw images”
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
Raw planetary images and boulder labels data (as shapefiles) collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 64 large images of craters on the lunar and martian surfaces and 3 images of boulder fields on Earth (see manuscript <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a> for more information on those terrestrial locations). The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024.</p> <p>For each image, the boulder outlines within specific tiles within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). </p> <p>For each location, you will find a raster with a .tif format, and three shapefiles:</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a tiles-completely-mapped file, which depicts the patches/tiles/windows on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches/tiles/windows (pick the term you are the most familiar with) within a raster.</p> </li> </ul> <p>In addition you will find .pkl (which stands for pickle), which contains some information about the patches/tiles/windows if you would need to clip those windows out from the original raster. You can find more information in the way we process this raw data into a format which can be ingested in a deep learning model (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>) in the two following github repositories (<a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth</a> and <a href="https://github.com/astroNils/MLtools/tree/main" target="_blank" rel="noopener">https://github.com/astroNils/MLtools</a>). If you don't plan in adding more training data, you can directly used the pre-processed database (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>).</p> <p>There are multiple locations/images per planetary body. Cold spots are located on the Moon, but they are saved in a folder of their own. </p> <p>Note that the cold spots boulder mapping shapefiles are partially manually mapped, and partially originating from predictions made from a deep learning model (which explains the outline of boulders are predicted within one pixel).</p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── raw_data/ ├── coldspots/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</code></pre>
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Multiplexed fluorescence imaging based on cycles, raw and processed data.
<p>This dataset was created from a larger acquisition in order to provide an example of reasonnable size, as a companion data set to the F1000Research paper preprint DOIXXX.</p> <ul> <li>The original raw data including metadata files are included in <strong>Microscope_Output.zip.</strong></li> <li><strong>Experiment.json</strong> and<strong> channelnames.txt </strong>are the ones generated by the acquisition software. They are the only files needed when starting from one of the processed data set below.</li> <li>The deconvolution obtained with the commercial software Microvolution is also provided in <strong>bu_deconvolution.zip.</strong> To start from Step 1(Extended Depth of Field) instead of Step 0 (deconvolution), unzip this file in your output directory and rename the folder bu_deconvolution to out.</li> <li>The extended field of view 2D images created from step 0 to step 2, provided for convenince in <strong>edfonly.zip</strong></li> <li>The final files generated by trhe Multiplex processor, including the segmentation mask , are provided in<strong> finaloutput.zip</strong>. These files can be used in a specific analysis software.</li> </ul> <p> </p>
Raw diffraction images of pyruvate phosphate dikinase (PPDK), PDB 5JVN
<p>Raw diffration images and processing input files/logs of pyruvate phosphate dikinase (PPDK) from the C<sub>3</sub> plant <em>Flaveria pringlei</em>. The data was used for PDB entry <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/5jvn">5JVN</a> (<a href="https://www.doi.org/10.1038/srep45389">Minges et al. 2017</a>). Data was collected in two helical scans from the same crystal, each consisting of 3600 images (360°/scan, 0.1°/image). Data were cut according to accumulated radiation damage at 3445 and 3400 images respectively. All data was collected from loop-harvested crystals at beamline ID29 at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using a wavelength of 0.976252 Å and a Pilatus 6M (Dectris, Baden, Switzerland) detector.</p> <p>The crystal belonged to the spacegroup P622 with unit cell constants a, b ~ 250 Å, c ~ 84 Å, α, β, γ ~ 90°.</p> <p>.</p>
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset
<p><strong>Overview:</strong></p> <p>This repository contains the <strong>raw images</strong> for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>.</p> <p>This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p><strong>Usage:</strong></p> <p>Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal:</p> <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <p><strong>File Numbering/Naming Scheme:</strong></p> <p>The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image.</p> <p>To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong><a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a></strong> (see the scripts/alignment/ directory).</p> <p>For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>. The CSV format is as follows:</p> <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre> <p> </p>
Example imaging mass cytometry raw data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the <strong>I</strong>ntegrated i<strong>MMU</strong>noprofiling of large adaptive <strong>CAN</strong>cer patient cohorts (IMMUcan) project (<a href="https://immucan.eu">immucan.eu</a>) using the Hyperion imaging system (<a href="https://www.fluidigm.com/products-services/instruments/hyperion">www.fluidigm.com/products-services/instruments/hyperion</a>). To get an overview on the technology and available analysis strategies, please visit <a href="https://bodenmillergroup.github.io/IMCWorkflow/">bodenmillergroup.github.io/IMCWorkflow</a>. The individual data files are described below:</p><ul><li><strong>Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip</strong>: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.</li><li><strong>compensation.zip</strong>: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: <a href="https://doi.org/10.1016/j.cels.2018.02.010">Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry</a></li><li><strong>panel.csv</strong>: This file contains metadata for each antibody/channel used in the experiment. The <i>full</i> column indicates which channel should be analysed. The <i>ilastik</i> column specifies which channels were used for ilastik pixel classification and the <i>deepcell</i> column indicates the channels used for deepcell segmentation.</li><li><strong>sample_metadata.csv</strong>: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).</li></ul>
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE) - Raw images for the analysis of stomatal density and length 2021-2022
Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the raw images underlying the data reported in a separate data package on stomatal density and length: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=372 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284
<p>A set of raw gel images used in the article: Koralewska <em>et al</em>. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production <em>in vitro</em> and <em>in vivo, </em>NAR 2024, DOI 10.1093/nar/gkae284.</p>
Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"
<p>These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing".</p> <p>For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: </p> <ul> <li><strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li><strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> <p>The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.</p>
Raw images from: Detecting life by behavior, the overlooked sensitivity of behavioral assays
<p>Raw images of the manuscript entitle: "Detecting life by behavior, the overlooked sensitivity of behavioral assays"</p> <p>Description: Using a magnetotactic bacterial species,<em> Magnetospirillum magneticum</em>, we conduct a lab sensitivity experiment comparing PCR with the hanging drop behavioral assay, using a dilution series.</p> <p>Data:</p> <p>1.-Gel image resulted from the <em>Magnetospirillum magneticum </em>PCR assays. </p> <p>2.-Microphotographs of <em>Magnetospirillum magneticum </em>obtained using the hanging drop technique and serial dilution. </p> <p>3.-Videos 1 to 4.Environmental samples were taken from Agmon Hula lake, (33° 10′ N 35° 60′ E). We used the HDT (see main MS) to morphologically identify magnetotactic bacterial species.</p>
Metzger and Ozpolat 2024 - raw imaging files for 40S group
<p>Original imaging files for the publication by Metzger and Özpolat 2024. See the GitHub page for details and a link to the paper.</p> <p>https://github.com/BDuyguOzpolat/Platynereis-GermRegen-Metzger-et-al-2024 </p> <p>Publication: https://www.sciencedirect.com/science/article/pii/S0012160624001246</p>
Metzger and Ozpolat 2024 - raw imaging files for 60S group
<p><span>Original imaging files for the publication by Metzger and Özpolat 2024. See the GitHub page for details and a link to the paper. </span></p> <p><span>https://github.com/BDuyguOzpolat/Platynereis-GermRegen-Metzger-et-al-2024 </span></p>
Comprehensive Mini-Database of the Northern Hemisphere's Winter Sky: 100 Raw Images from Ensenada, Mexico
<p>We carried out several test sessions for data collection to adjust the settings of our optical system. From October 2022 to June 2023, we executed numerous sessions to assemble our primary catalog, capturing an extensive array of sky views. A total of 100 sky observations were recorded from various directions without restrictions. These sessions were held at the peak of a hill where CICESE, our research institute, is situated at coordinates 31°52′21.5′′ N 116°40′11.8′′ W in Ensenada, Baja California, Mexico. This location was chosen because it is relatively free from urban light pollution and noise, despite its proximity to the city outskirts. This position minimizes city light interference on one side, slightly reducing light pollution, although image quality was occasionally compromised by the light pollution and facility lighting.</p> <p>Using the ASI Studio software, we captured high-resolution images of 5496 × 3672 pixels without employing pixel binning to achieve the highest possible resolution. The camera's settings were adjusted to an exposure time of 0.5 seconds and standard gain, with the lens focused at infinity and an aperture set at f/4. This setup enabled us to detect significant background noise and numerous areas that could potentially contain stars.</p> <p>More information about the article is in the:</p> <p><a href="https://doi.org/10.3390/aerospace10090748">https://doi.org/10.3390/aerospace10090748</a></p>
Raw images (photographs) of urban text scenes for camera-based Thai text recognition
<p>Raw image collection of city scenes in Thailand with text content.<br> Text is photographed from diffeent angles. Also morning and evening<br> photographs were taken in order to capture different lighting<br> conditions. The material, 309 images, was photographed in 2013 by<br> Bowornrat Sriman and volunteers.</p> <p>Example EXIF:<br> JPEG image data, Exif standard: [TIFF image data, little-endian,<br> direntries=13, height=2448, manufacturer=SAMSUNG, model=GT-I9300,<br> orientation=upper-right, xresolution=220, yresolution=228,<br> resolutionunit=2, software=I9300XXEMA2, datetime=2013:03:14 18:17:49,<br> GPS-Data, width=3264], baseline, precision 8, 3264x2448, frames 3</p> <p>The images are not labeled. The orientation (landscape/portrait) is<br> not corrected yet. This material was used in preparation of the publication:</p> <p>Sriman, B. & Schomaker, L. (2015).<br> Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT,<br> Proceedings of the International Conference on Pattern Recognition Applications and<br> Methods: ICPRAM 2015. De Marsico, M., Figueiredo, M. & Fred, A.<br> (Eds.). Lisbon, Portugal: SciTePress, Vol. 1, p. 304-311 8 p.</p> <p>Please cite this publication when using these data.</p>
RAW SEM images mosaics dataset for the HRDIC strain localization study in shot peened Ni superalloy
<p>We used a FEI Magellan HR 400L FE-SEM with a theoretical resolution of ≤ 0.9 nm at <1kV and ≤ 0.8 nm at ≥ 5kV to take backscattered electron images of the fine, homogeneous distributed gold speckle pattern obtained by remodelling of a thin gold layer previously deposited on the polished sample surface. The images were obtained at a working distance of 3.5 mm, 5 kV and 0.8 nA beam current. Mosaics of 30x15 images were used to cover 950x420 µm<sup>2</sup>. Each image contains 2048 x 1768 pixels and has a horizontal field of view of 43 µm. The images were overlapped by 20% to enable easy stitching prior to the digital image correlation. We obtained 7 mosaics, one before tensile testing and 6 after each deformation step.</p> <p>This set of images at different strain steps is coupled with the EBSD data set in https://doi.org/10.5281/zenodo.4730184, the HRDIC strain maps in http://doi.org/10.5281/zenodo.4728016 and data visualisation scripts in http://doi.org/10.5281/zenodo.4727939</p> <p>0_def corresponds to the undeformed sample, while 1_def to 6_def were obtained after each deformation step.</p>
Raw Data: Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging
<p>Two sets of raw data from gold coated ZnO microstructure (rod) investigated by Bragg coherent X-ray diffraction imaging used in publication: "Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications" (<a href="https://doi.org/10.1002/aelm.202100546">https://doi.org/10.1002/aelm.202100546</a>)</p> <p>Included is data from two different spatial positions along the c-axis of the ZnO rod. Futher on called position 1 (P1) and position 2 (P2). For each position there is a .nxs file of a rocking scan around the {10-10} Bragg reflection, collected by a 2D detector and other recorded values, e.g. motor positions, counter values. </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.