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155 results for “2D images”
Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'
<p>3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423</p>
TINKER_WP3_2D&3D images - profile scans dataset_221123
<p>2D and 3D images of PCBs showing the gap 2D and 3D information. Moreover, profile measurements are also included in x and y axes.</p>
Label-free imaging of DNA interactions with 2D materials
<p>Raw images and data analysis related to the manuscript entitled "Label-free imaging of DNA interactions with 2D materials"</p>
Perceptual maps of Heliconiini butterflies: images, 3D spaces, 2D maps, and mimicry ring listings
<h3>Summary</h3> <p>This repository contains<strong> images</strong>, <strong>3D animated spaces</strong>, <strong>2D perceptual maps with GMM</strong>, and <strong>mimicry ring lists</strong> for heliconiine butterflies complementing the analyses presented in this research paper:<em><strong> </strong>"Doré et al., 2025 - Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep."</em>.</p> <h3>Abstract</h3> <p> In 1879, Fritz Müller formulated the first mathematical evolutionary model to explain mutualistic mimicry between coexisting defended prey. Yet, the degree to which local mimicry drives the structure of prey aposematic signals at continental scale remains unclear, because the perception of pattern similarity has never been assessed at large spatial scale. Here, we implement a Citizen Science survey to quantify and analyze the structure of perceived variation in the wing patterns of heliconiine butterflies (Nymphalidae: Heliconiini) throughout the entire Neotropics. Despite a continuum of perceived wing patterns at the continental scale, we show that the convergence of sympatric species into discrete mimicry rings is ubiquitous among communities. These results expand Müller’s historical predictions by supporting the rampant convergence of prey signals across an entire continent. </p> <h3>Contents</h3> <p>This repository contains three folders:</p> <ul> <li><em>"3D_maps"</em> contains the <strong>animated 3D perceptual spaces</strong> of heliconiine wing patterns for the Citizen Science dataset (N = 432) and the Local reference for the five local communities highlighted in the article.</li> <li><em>"Clustering"</em> contains the <strong>2D perceptual maps</strong> and associated <strong>lists of mimicry rings</strong> built for each of the five local communities, for different level of clustering from GMM (K from 5 to 10).</li> <li><em>"Images"</em> contains the 432 <strong>images of dorsal wing patterns</strong> of heliconiine butterflies used in the online survey (<a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>) designed for this study.</li> </ul> <h3>How to cite</h3> <p>Please cite this research article as:</p> <blockquote> <p>Doré, M., Pérochon, E., Aubier, T.G., Le Poul, Y., Joron, M., Elias, M., 2025. Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep. <a href="https://doi.org/TBA">https://doi.org/TBA</a></p> </blockquote> <h3>Associated ressources</h3> <p>The source codes for the analyses carried out in the study are available on <a href="https://github.com/MaelDore/Perceptual_map_Heliconiini">GitHub</a>.<br> <br>The occurrences data and distribution maps used in this study are publicly available from Zenodo: Occurrences data at <a href="https://doi.org/10.5281/zenodo.10906853">https://doi.org/10.5281/zenodo.10906853</a>; Distribution maps at <a href="https://doi.org/10.5281/zenodo.10903661">https://doi.org/10.5281/zenodo.10903661</a>.</p> <p>The online Citizen Science survey on the perception of mimicry in wing color patterns of heliconiine butterflies is temporary available at <a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>.<br>Source code for the online Citizen Science survey are accessible on <a href="https://github.com/MaelDore/Memometic_website">GitHub</a>.</p>
Automated measurement of fetal head circumference using 2D ultrasound images
<p>For more information about this dataset go to: <a href="https://hc18.grand-challenge.org/">https://hc18.grand-challenge.org/</a></p>
Imaging thermocline microstructure in 2D with swaths traced by wave-pumped χpods: dataset and code
<p><a href="https://doi.org/10.1029/2024JC022134">Associated paper</a> published in <em>J. Geophys. Res. Oceans</em></p> <h2>Dataset summary</h2> <p>Location: 0°N, 140°W<br>Depth: 120 m<br>Period: 16-Sep-2014 to 19-Oct-2015</p> <p>This data archive contains two types of data files:<br>data_yymmdd.mat<br>grid_yymmdd.mat<br>Each file contains 24 hours of data. There are 394 of each type.</p> <p>Arrays in data_yymmdd.mat are single precision (except the 'time' array) to keep file sizes small.</p> <h2>Contents of the data files</h2> <p>Each Matlab file contains a single struct. These structs include readmes, which are reproduced in the full PDF readme (chipod_swaths_readme.pdf).</p> <p>Files are grouped into months and zipped (yymm.zip) to ease downloading.</p> <h2>Reading the data file with Python</h2> <p>Example code to read the files into Python as dictionaries is given in the full PDF readme (chipod_swaths_readme.pdf).</p> <h2>Matlab code to produce the processed data</h2> <p>The code to read in raw chipod data and process them is primarily contained in the file 'swaths_paper_data_preparation.m'. This file calls three other files ('raw_load_chipod.m', 'deglitch.m', and 'bin.m'). All of these files are provided for completeness, but we are only archiving the processed outputs (not the raw voltage signals). Please email if more information is needed.</p> <h2>Matlab code for the convolutional neural network</h2> <p>See 'chipod_swaths_convolutional_neural_network.m'.</p>
nPSize VSL images as 2D arrays
<p>VSL.zip are 2D arrays, to be extracted in the following directory: /dataset/images/VSL to build /dataset/images/VSL/[VSL_images.h5] Note: h5 is a compressed format, to uncompress.</p>
nPSize BAM images as 2D arrays
<p>BAM1.zip BAM2.zip BAM3.zip are 2D arrays, to be extracted in the following directory: /dataset/images/BAM to build /dataset/images/BAM/[BAM_images.h5] Note: h5 is a compressed format, to uncompress.</p>
nPSize CEA images as 2D arrays
<p>CEA1.zip CEA2.zip CEA3.zip CEA4.zip CEA5.zip CEA6.zip CEA7.zip CEA8.zip CEA9.zip are 2D arrays, to be extracted in the following directory: /dataset/images/CEAto build /dataset/images/CEA[CEA_images.h5] Note: h5 is a compressed format, to uncompress.</p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
archive_image_correlation_robust_geometrical_design_of_2D_sequential_interlocking_assemblies
<p>Archive containing the X and Y deformation, obtained by image correlation, of several laser cut samples of assemblies generated using the method described in the article Robust geometrical design of 2D sequential interlocking assemblies (not published yet)</p>
Parcel2D Real - A real-world image dataset of cuboid-shaped parcels with 2D and 3D annotations
<p>Real-world dataset of ~400 images of cuboid-shaped parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>.</p> <p>Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction (.binvox files)</li> <li>3D reconstruction (.obj files)</li> </ul> <p>For details, see our <a href="https://ieeexplore.ieee.org/abstract/document/10069342">paper</a> and <a href="https://a-nau.github.io/parcel2d/">project page</a>.</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannScrapeCutPasteLearn2022, title = {Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics}, author = {Naumann, Alexander and Hertlein, Felix and Zhou, Benchun and Dörr, Laura and Furmans, Kai}, booktitle = {{{IEEE Conference}} on {{Machine Learning}} and Applications ({{ICMLA}})}, date = 2022 }</code></pre> <p> </p>
Parcel3D - A Synthetic Dataset of Damaged and Intact Parcel Images with 2D and 3D Annotations
<p>Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and for visual samples our <a href="https://a-nau.github.io/parcel3d/">project page</a>.</p> <p><br> Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>classification</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction</li> <li>3D reconstruction</li> </ul> <p> </p> <p>The dataset is for <strong>academic research use only</strong>, since it uses resources with restrictive licenses.<br> For a detailed description of how the resources are used, we refer to our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and <a href="https://a-nau.github.io/parcel3d/">project page</a>.</p> <p>Licenses of the resources in detail:</p> <ul> <li><a href="https://research.google/resources/datasets/scanned-objects-google-research/">Google Scanned Objects</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (for details on which files are used, see the respective <em>meta </em>folder)</li> <li><a href="https://zenodo.org/record/8041823">Cardboard Dataset</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></li> <li><a href="https://ieeexplore.ieee.org/abstract/document/8999123">Shipping Label Dataset</a>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></li> <li>Other Labels: See file <em>misc/source_urls.json</em></li> <li><a href="https://github.com/weberhen/learning_indoor_lighting">LDR Dataset</a>: License for Non-Commercial Use</li> <li><a href="https://data.vision.ee.ethz.ch/sagea/lld/">Large Logo Dataset (LLD)</a>: Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.</li> </ul> <p>You can use our textureless models (i.e. the <em>obj</em> files) of damaged parcels under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (note that this does not apply to the textures).</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }</code></pre>
Datesets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: 10.1002/pssa.202300148
<p>Datasets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: <a href="https://www.doi.org/10.1002/pssa.202300148">10.1002/pssa.202300148</a></p> <p>The Jupyter Notebooks for analyzing the datasets and generating all the figures are available at <a href="https://gitlab.com/JohMu/tds_hios_manuscript">https://gitlab.com/JohMu/tds_hios_manuscript</a>.</p> <p><strong>MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>192x192 scan pixels</li> <li>200x200 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 10.56 mm</li> <li>Camera pixel size: 4x5.86 µm = 23.44 µm (original dataset with 4x4 binning)</li> <li>File location: Figure 2_3_S1.zip -> 230101205338_20kV_hexz0_camz-10_posi_003_good\scan_data_bin2_centered_crop-imgNx200.h5</li> <li>The original raw dataset (23 GB, 192x192 scan pixels, 800x800 camera pixels, camera pixel size: 5.86 µm), the scan reference dataset and the Jupyter Notebook for the shift-compensation is available from the author. The dataset uploaded here is binned by a factor of 4 and shift-compensated.</li> </ul> <p><strong>C60/MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>113x113 scan pixels</li> <li>512x512 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 20.56 mm</li> <li>Camera pixel size: 5.86 µm</li> <li>File location: Figure 4.zip -> scan_data_scan113x113_gzip.h5</li> </ul> <p> </p>
ATLAS Database — 2D Images
<p>We need affect-based stimuli specifically conceived to investigate architectural spaces. The RESONANCES project crafted ATLAS, a dATabase of visuaL Atmospheric Stimuli. It collects a series of spatial patterns born from a systematic selection of generators of atmosphere. Generators of atmosphere are architectural features designed to afford atmospheric effects (such as lights, colors, materials, and proportions). ATLAS is an open-access tool that supports researchers interested in studying emotional reactions to architectural features by providing reliable, standardized, and reproducible stimuli. In this dataset, ATLAS stimuli are presented as 2D images.</p>
Dataset: Segmentation of cortical bone, trabecular bone, and medullary pores from micro-CT images using 2D and 3D deep learning models
Open the record for dataset details and reuse information.
Scripts and raw data for comparing 2D vs 3D image analysis of zebrafish embryo microscopic data
<p>Raw data and MatLab scripts used for image analysis of RNA polymerase II with serine 5 phosphorylation in the C-terminal domain (CTD) of the subunit 1 (Pol II Ser5P) in a fixed zebrafish embryo, comparing a 2D vs 3D approach to segment out the Pol II Ser5P clusters. Pol II Ser5P was labeled by immunofluorescence, microscopy images were acquired by instant-SIM microscopy and analyzed using MatLab scripts and the bioformats importer.</p>
2D bright field yeast cell images with ground truth annotations
<p>Dataset used to evaluate the method described in "Yeast cell detection and segmentation in bright field microscopy", ISBI 2014 (<strong>DOI: </strong>10.1109/ISBI.2014.6868107).</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Hierarchical Planar Correlation Clustering for Cell Segmentation", EMMCVPR 2015. (DOI: 10.1007/978-3-319-14612-6_36)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
2D phase contrast HeLa cells images with ground truth annotations
<p>Original images are from http://www.robots.ox.ac.uk/~vgg/software/cell_detection/. This software is associated with the publication "Learning to Detect Cells Using Non-overlapping Extremal Regions", MICCAI 2012. (DOI: 10.1007/978-3-642-33415-3_43)</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
2D bright field Fission yeast cell images with ground truth annotations
<p>Original images are from http://www-bcf.usc.edu/~forsburg/pombeX.html. This software is associated with the publication "PombeX: robust cell segmentation for fission yeast transillumination images", PLoS One 2013. (doi: 10.1371/journal.pone.0081434)</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
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.