Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
34
datasets available to search
ShareScore release 0.9.0
Dataset results
34 results for “3D Volume”
3D Reconstruction of Shoulder Muscles in Hominoid Primates: Correlating Scapular Attachment Areas with Muscle Volume
<h2><strong>How To Cite:</strong></h2> <p>If you use this data or code in your research, please cite the associated open-access <strong>manuscript, </strong>which you can find here: <a href="https://doi.org/10.1111/joa.14199">https://doi.org/10.1111/joa.14199</a><br>and this <strong>zenodo repository</strong>.</p> <h2><strong>Online Visualization:</strong></h2> <p>You can access an interactive, web-based view of the notebooks and analyses <a title="Shoulder Muscle Reconstruction Code" href="https://juliavanbeesel.github.io/ShoulderMuscleReconstructions/intro.html" target="_blank" rel="noopener">here</a>.</p> <h2><strong>Repository Description:</strong></h2> <p>This repository contains two zip files related to the analysis and visualization of 3D reconstructed muscle volumes and lengths from various hominoid specimens.</p> <ol> <li> <p><strong>MeshFiles.zip:</strong></p> <ul> <li><strong>Contents:</strong> This zip file includes all <code>.obj</code> files for 3D reconstructed muscle volumes and associated anatomical structures. Specifically, it contains: <ul> <li><strong>Muscles:</strong> Supraspinatus, Infraspinatus, Subscapularis, Teres Major, Teres Minor</li> <li><strong>Bones:</strong> Scapula and Humerus</li> <li><strong>Attachment Sites</strong></li> </ul> </li> <li><strong>Organization:</strong> The files are organized into folders by specimen. There are 9 hominoid specimens from the following species: <ul> <li><em>Hylobates lar</em></li> <li><em>Symphalangus syndactylus</em></li> <li><em>Pongo pygmaeus</em></li> <li><em>Pongo abelii</em></li> <li><em>Gorilla gorilla</em></li> <li><em>Pan troglodytes</em></li> <li><em>Homo sapiens</em></li> </ul> </li> <li><strong>Surface Scans of Muscle Geometry: </strong>The specimens <em>Pongo</em> (ID 3) and <em>Symphalangus </em>(ID 122) also contain surface scans that depict the muscle geometry of the listed muscles. These surface scans can be used for training with the iterative polygonal modelling approach. The scans are stored as <code>.obj</code>, <code>.mtl</code> and <code>.png</code> files. To view textures on these meshes, keep all three files together in the same folder.</li> <li><strong>Additional Details:</strong> Muscle reconstructions were performed for different arm positions. Each folder contains multiple humerus files, with each file representing a humerus in a specific position aligned with the corresponding muscles. The humerus file names indicate the muscles the humerus is aligned with.<br><br></li> </ul> </li> <li> <p><strong>DataAndCode.zip:</strong></p> <ul> <li><strong>Contents:</strong> <ul> <li><strong>Excel File:</strong> The original data used for analysis, presented in Table 2 of the manuscript.</li> <li><strong>Jupyter Notebook Files: </strong>These notebooks provide the analyses and figures as described in the manuscript: <ul> <li><em>Accuracy_Muscle_Length_Reconstruction:</em> Analysis of muscle length measurement comparisons, detailed in Supplementary Information Section 3: <em>Accuracy of estimating Muscle Length from 3D reconstructions</em>.</li> <li><em>Accuracy_Muscle_Volume_Reconstruction:</em> Analysis of muscle volume measurement comparisons, detailed in Results Section 3.2: <em>Accuracy of Muscle Volume and Length Reconstruction</em>.</li> <li><em>Correlation_Analysis_SIS:</em> Correlation analysis of muscle origin area to volume for the supraspinatus, infraspinatus, and subscapularis muscles, detailed in Results Section 3.3:<em> Correlation Analysis</em>.</li> <li><em>Correlation_Analysis_TT:</em> Correlation analysis of muscle origin area to volume for the teres major and minor muscles, detailed in Supplementary Information Section 1: <em>Correlation results of teres major and minor</em>.</li> </ul> </li> <li><strong>Requirements.txt:</strong> A file listing the necessary packages required to run the Jupyter notebooks.</li> </ul> </li> <li><strong>Purpose:</strong> The Python files include code for performing statistical analyses and generating figures as described in the manuscript.</li> </ul> </li> </ol> <h2><strong>Usage Instructions:</strong></h2> <ul> <li>For analyzing muscle volumes and lengths, refer to the Jupyter notebooks included in the <code>DataAndCode.zip</code>. Ensure all dependencies listed in the <code>requirements.txt</code> file are installed.</li> <li>The <code>MeshFiles.zip</code> contains the 3D models necessary for visualizing muscle and bone reconstructions, organized by specimen and arm position.</li> </ul>
Multimodal Dataset of 3D point clouds and CT-volumes
<p>The multimodal dataset for evaluating algorithms for aligning CT volumes and point clouds which is presented in 'Multimodal registration across 3D point clouds and CT-volumes'. (Saiti, E., and T. Theoharis. "Multimodal registration across 3D point clouds and CT-volumes." <em>Computers & Graphics</em> 106 (2022): 259-266.) The multimodal dataset consistsof real micro-CT scans and their synthetically generated 3D models (point clouds) .</p>
Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers
<p>3D EIT dataset of ten healthy human volunteers, as described in the corresponding <a href="http://dx.doi.org/10.1371/journal.pone.0191870">journal publication at PLOS ONE</a> or the first author's <a href="http://dx.doi.org/10.5075/epfl-thesis-8343">PhD thesis at EPFL</a>. Please also read the attached ReadMe file.</p> <p>When using this data please cite the corresponding journal publication:</p> <blockquote> <p>Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers, PLOS ONE, 2018, <a href="http://dx.doi.org/10.1371/journal.pone.0191870">https://dx.doi.org/10.1371/journal.pone.0191870</a></p> </blockquote>
cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation
<p>cigKarst is a dataset created by the <a href="http://cig.ustc.edu.cn/">Computational Interpretation Group (CIG)</a> for the deep-learning-based peleokarst interpretation in 3D seismic images, <a href="http://cig.ustc.edu.cn/xinming/list.htm" target="_blank" rel="noopener">Xinming Wu</a> is the main contributor to the dataset.</p> <p>This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth:</p> <p><strong>Wu, X.</strong>, S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. <strong>JGR, Solid Earth</strong>, Vol. 125(9), 1-23, e2020JB019685. <a href="http://cig.ustc.edu.cn/_upload/tpl/05/cd/1485/template1485/papers/wu2020karst.pdf">[PDF]</a>. doi: 10.1029/2020JB019685</p> <p>Below are some brief description of the dataset:</p> <p>1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256;</p> <p> 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip</p>
Segmentations of 3D electron microscopy image volume from an albino mouse dorsal lateral geniculate nucleus
Open the record for dataset details and reuse information.
3D nuclei instance segmentation dataset of fluorescence microscopy volumes of C. elegans
<p>The dataset consists of 28 confocal microscopy volumes of C. elegans worms at the L1 stage and corresponding stacks of densely annotated nuclei instance segmentation masks.</p> <p>* 28 raw images and corresponding masks of average dimension (xyz) 1050 x 140 x 140<br> * Pixelsize (xyz): 0.116 x 0.116 x 0.122μm<br> * Microscope: Leica confocal microscopy, 63x oil objective</p> <p><br> The original raw data and preliminary annotations were part of the following publication (please cite if you use the dataset):<br> <br> <em>Long, F., Peng, H., Liu, X., Kim, S. K., & Myers, E. (2009). A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature methods, 6(9), 667-672.</em></p> <p>The nuclei annotation masks were further manually curated by Dagmar Kainmueller (MDC Berlin) for the following publication:</p> <p><em>Hirsch, P., & Kainmueller, D. (2020). An auxiliary task for learning nuclei segmentation in 3d microscopy images. In Medical Imaging with Deep Learning (pp. 304-321). PMLR.</em></p> <p>We provide the dataset already structured into the train/validation/test split as used by the above as well as the following publications: </p> <p><em>Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3666-3673).</em><br> </p> <p> </p>
VesselExpress: Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs
<p>This dataset contains raw, segmented and skeletonized 3D light-sheet microscopic image volumes of blood vessels of different organs which were processed by VesselExpress. Please find the software here: https://github.com/RUB-Bioinf/VesselExpress. For details on how to run and setup the software please watch our tutorial (https://youtu.be/a8GWVKJNh68).</p>
SYNTHETIC VOLUMES AND 3D GROUND TRUTH ANNOTATIONS OF NUCLEI IN 3D MICROSCOPY VOLUMES
<p>Manual ground truth annotations of subvolumes of eight microscopy volumes are provided.</p> <p>Synthetic subvolumes, generated with an SpCycelGAN, of the same volumes are also provided. </p> <p> </p>
3D volume data of kenaf pulvini
<p>This is the supplemental data for the article titled "Simultaneous Analysis of Shape and Internal Structure of a Curved Hibiscus cannabinus Pulvinus: X-ray Microtomography and Semi-Automated Quantification," which was published in the Journal of Plant Research. https://doi.org/10.1007/s10265-023-01498-w</p>
3D volumes of suspended particulate matter in the Belgian part of the North Sea
<p>This dataset comprises 3D volumes that display the converted mean mass concentrations of suspended particulate matter (SPMC) for different size ranges (1-500 µm, 1-3 µm, 3-20 µm, 20-200 µm, 200-500 µm). These 3D grids (2 m resolution) were made for five campaigns, which were conducted during 2020-2021 in the Belgian part of the North Sea. </p> <p>This dataset was used in the research article in Remote Sensing titled "The potential of multibeam sonars as 3D turbidity and SPM monitoring tool in the North Sea" by Praet et al. (2023).</p>
Measuring Volume Using a 3D Scanner and iPad to Grade the Swelling of Legs
ClinicalTrials.gov study NCT06944041. IPD Sharing: NO. Countries: 1. Publications: 4.
Contrast Enhanced 3D Echocardiographic Quantification of Right Ventricular Volumes in Repaired CHD
ClinicalTrials.gov study NCT05186415. IPD Sharing: NO. Countries: 1. Publications: 21.
Fetal 3D Study (Fetal Body Composition and Volumes Study)
ClinicalTrials.gov study NCT03266198. IPD Sharing: YES. Countries: 1. Publications: 1.
3D PanIN volume distribution data
<p>This data was originally published in Braxton,* Kiemen* et al "Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions" and was used in Kiemen et al "Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings."</p>
Supplementary movies and datasets of the paper: Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP
<p>Imaging data supporting the paper: </p> <p>Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP, containing raw and processed data from fluorescent and electron microscopy.</p> <p> </p>
Comparing a New 3D Technology Used to Measure Thyroid Nodule Volume to Standard Ultrasound
ClinicalTrials.gov study NCT07178080. IPD Sharing: NO. Countries: 1. Publications: 3.
Comparison of the Diagnostic Accuracy of 3D Volume Acquisition MRI With CT in Staging Colonic Cancer
ClinicalTrials.gov study NCT01187641. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Cell Volume (3D) Correlative Microscopy Facilitated by Intra-Cellular Fluorescent Nanodiamonds as Multi-Modal Probes
<p>RAW files</p>
Simulated 3D volume in MRC format of a ribosome from PDB 4v6x at 4 Å/px
Open the record for dataset details and reuse information.
Cropped 3D volume of the reconstructed dataset from i23 beamline, DLS
<p>This is a cropped volume of the tomographic dataset obtained at i23 beamline of Diamond Light Source, UK</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.