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438
datasets available to search
ShareScore release 0.9.0
Dataset results
438 results for “3D imaging”
Field dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging
<p><a href="https://zenodo.org/api/files/edf611c5-3daa-479a-8470-3406cf4df3b5/example_data.zip?versionId=43aab935-acd9-4b81-9a0d-298eaae73fd1">example data</a></p>
Images used in 3D-engineered scaffolds to study patient-derived glioma cells
<p>Images used in the analysis of the research work " 3D-engineered scaffolds to study microtubes and localization of epidermal growth factor receptor in patient-derived glioma cells".</p> <p>Images can be available upon request. Please contact: a.accardo@tudelft or p.french@erasmusmc.nl</p>
Monitoring soil surface change under simulated rainfall using image-based 3D reconstruction.
<p>This dataset contains three-dimensional (3D) soil surface information from rainfall simulation experiments in laboratory and field conditions. </p> <ul> <li>3D data are provided as .img rasters contained in the zip file</li> <li>Information pertaining to experimental conditions and treatments are provided as tables in the csv files</li> <li>Table columns are defined in the 3D_Data_Description.pdf file</li> </ul> <p>Notes: Treatment abbreviations in the laboratory experiment are Seep for seepage condition and Drain for drainage condition (See <a href="https://doi.org/10.2136/sssaj2012.0013">doi.org/10.2136/sssaj2012.0013</a>).</p>
3D models and images for: Estimating body size in the large carpenter bees (Xylocopa)
Open the record for dataset details and reuse information.
Carcassonne 3D en 1955, images IGN
Voici une tentive de modélisation de la ville de Carcassonne à partir des images IGN. Le relief est restitué et certains batiments. L'objectif suivant serait d'avoir une échelle de prise de vue plus petite avec plus de recouvrements. Source: Objaverse 1.0 / Sketchfab
Research and System Development on Functional 3D Optical Tomography for Skin Cell Imaging
ClinicalTrials.gov study NCT01557205. IPD Sharing: Not stated. Countries: 0. Publications: 0.
3D Structural Imaging of Human Breast Tissues
ClinicalTrials.gov study NCT06454214. IPD Sharing: NO. Countries: 0. Publications: 0.
Machine Learning and 3D Image-based Modeling for Body Weight Estimation.
ClinicalTrials.gov study NCT06281938. IPD Sharing: YES. Countries: 0. Publications: 0.
L-Gastrectomy With the Intelligent Navigation 4K UHD 3D Endoscopic Imaging System
ClinicalTrials.gov study NCT04526483. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Advanced Adenoma Detection With 3D Imaging Device During Colonoscopy
ClinicalTrials.gov study NCT06924489. IPD Sharing: NO. Countries: 0. Publications: 0.
3D-T1 image of Cushing disease
Open the record for dataset details and reuse information.
2D&3D images - profile scans dataset
<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>
Self-Supervised Learning Cell Image Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the self-supervised learning cell image dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". We gather images from datasets such as the LIVECell dataset, the EVICAN dataset, as well as datasets available on Image Data Resource (https://idr.openmicroscopy.org/) and the Broad Bioimage Benchmark Collection (https://bbbc.broadinstitute.org/). Only images with sizes larger than 512x512 are collected. For datasets containing more than 1000 images, we randomly select 1000 images. Otherwise, we retain all images in the dataset. </p> <p> </p> <p>After download, please put all compressed folders of subdatasets in the "image" folder under the root directory.</p>
Single-molecule detection and super-resolution imaging with a portable and adaptable 3D-printed microscopy platform (Brick-MIC)
<p>Supplementary Videos, CAD 3D-printing templates and assembly instructions</p>
Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.
<p>Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.</p> <p> </p> <p>This is the abstract:</p> <p>Congenital bicuspid aortic valve (BAV) consists of two fused cusps and represents a major risk factor for calcific valvular stenosis. Herein, a fully coupled fluid-structure interaction (FSI) BAV model was developed from patient-specific magnetic resonance imaging (MRI) and compared against in vivo 4-dimensional flow MRI (4D Flow). FSI simulation compared well with 4D Flow, confirming direction and magnitude of the flow jet impinging onto the aortic wall as well as location and extension of secondary flows and vortices developing at systole: the systolic flow jet originating from an elliptical 1.6 cm<sup>2</sup> orifice reached a peak velocity of 252.2 cm/s, 0.6% lower than 4D Flow, progressively impinging on the ascending aorta convexity. The FSI model predicted a peak flow rate of 22.4 L/min, 6.7% higher than 4D Flow, and provided BAV leaflets mechanical and flow-induced shear stresses, not directly attainable from MRI. At systole, the ventricular side of the non-fused leaflet revealed the highest wall shear stress (WSS) average magnitude, up to 14.6 Pa along the free margin, with WSS progressively decreasing towards the belly. During diastole, the aortic side of the fused leaflet exhibited the highest diastolic maximum principal stress, up to 322 kPa within the attachment region. Systematic comparison with ground-truth non-invasive MRI can improve the computational model ability to reproduce native BAV hemodynamics and biomechanical response more realistically, and shed light on their role in BAV patients' risk for developing complications; this approach may further contribute to the validation of advanced FSI simulations designed to assess BAV biomechanics.</p> <p> </p>
International Virtual Permeability Benchmark 3D Image Dataset of the Fiber Tow Microscopic Sample
<p>This microscopic 3D sample representing a part of the fiber tow of the woven composite reinforcement was used in the first phase of the international Virtual permeability benchmark. Its results are reported in [1-3]. While the permeability had been investigated before for various porous media, this benchmark exercise was the first contribution in the field of real fibrous microstructures.</p> <p>The 3D image was extracted from a 3D X-ray microscope scan of a composite sample prepared at Leibniz-Institut für Verbundwerkstoffe GmbH (IVW, Kaiserslautern, Germany). The segmentation of the 3D image was performed at the Research Institute in Civil Engineering and Mechanics (GeM) of Ecole Centrale de Nantes (Nantes, France).<br> <br> The <em><strong>segmented</strong></em> binary volume image data (.raw file) has the following characteristics:</p> <ul> <li>dimensions of 1003 x 124 x 973 voxels;</li> <li>0.521<sup>3</sup> µm<sup>3</sup> voxel size;</li> <li>8-bit;</li> <li>little-endian byte order.</li> </ul> <p>The provided corresponding <em><strong>non-segmented</strong></em> volume image has the following characteristics:</p> <ul> <li>dimensions of 1003 x 124 x 973 voxels;</li> <li>0.521<sup>3</sup> µm<sup>3</sup> voxel size;</li> <li>16-bit;</li> <li>little-endian byte order.</li> </ul> <p>The microscopic volume contains about 400 fibers of a single tow and covers the entire tow through-thickness dimension. Its average fiber volume fraction is 56.5%, though it varies locally along the fiber direction. The structure of the tow represents 3 yarns twisted together. The tow originates from the glass twill-weave fabric HexForce 01102 by HEXCEL.</p> <p> </p> <p>[1] <em>Elena Syerko, Tim Schmidt, David May, Christophe Binetruy, Suresh G. Advani et al. Benchmark Exercise on Image-Based Permeability Determination of Engineering Textiles: Microscale Predictions // Composites Part A: Applied Science and Manufacturing – 2023: 107397.</em></p> <p>[2] <em>Elena Syerko, Christophe Binetruy, Tim Schmidt, David May. </em> <em>Micro-Scale Results from the Benchmark Exercise on the Image-Based Permeability Prediction of Composite Reinforcements // Proceedings of </em> <em>IBSim-4i – Image-Based Simulation for Industry 2022</em><em>, London, UK – 2022.</em></p> <p>[3] <em> David May, Elena Syerko, Tim Schmidt, Christophe Binetruy, Luisa Rocha da Silva, Stepan Lomov, Suresh Advani. Benchmarking Virtual Permeability Predictions of Real Fibrous Microstructure // Proceedings of the American Society for Composites – 36th Technical Conference, Texas, USA – 2021.</em></p> <p> </p>
Data for Matlab package FISH-sim to simulate realistic 3d smFISH images
<p>Different data-sets needed by the Matlab package locFISH. locFISH allows the simulation and analysis of realistic single molecule FISH (smFISH) images.</p> <p><strong>data_simulation.zip</strong><br> Contains all necessary data to simulated smFISH images. Specifically, the zip archive contains a library of 3D cell shapes, realistic imaging background, and a simulated PSF (Point Spread Function). </p> <p><strong>GAPDH.zip</strong><br> Contains the smFISH data of GAPDH and the corresponding analysis results, which were used to create the library of cell shapes provided in data_simulation.zip </p> <p> </p> <p>For more details on these data and how do to use them, please consult the detailed user-manual provided with <strong>locFISH</strong>, available at</p> <p>https://bitbucket.org/muellerflorian/locfish</p> <p> </p>
Dataset related to article "Frozen Section Analysis and Real-Time Magnetic Resonance Imaging of Surgical Specimen Oriented on 3D Printed Tongue Model to Assess Surgical Margins in Oral Tongue Carcinoma: Preliminary Results"
<p>This record contains raw data related to article “Frozen Section Analysis and Real-Time Magnetic Resonance Imaging of Surgical Specimen Oriented on 3D Printed Tongue Model to Assess Surgical Margins in Oral Tongue Carcinoma: Preliminary Results"</p> <p>Abstract</p> <p><strong>Background: </strong> A surgical margin is the apparently healthy tissue around a tumor which has been removed. In oral cavity carcinoma, a negative margin is considered ≥ 5 mm, a close margin between 1 and 5 mm, and a positive margin ≤ 1 mm. Currently, the intraoperative surgical margin status is based on the visual inspection and tissue palpation by the surgeon and intraoperative histopathological assessment of the resection margins by frozen section analysis (FSA). FSA technique is limited and susceptible to sampling errors. Definitive information on the deep resection margins requires postoperative histopathological analysis.</p> <p><strong>Methods: </strong> We described a novel approach for the assessment of intraoperative surgical margins by examining a surgical specimen oriented through a 3D-printed specific patient tongue with real-time Magnetic Resonance Imaging (MRI). We reported the preliminary results of a case series of 10 patients, prospectively enrolled, with oral tongue carcinoma who underwent surgery between February 2020 and April 2021. Two radiologists with 5 and 10 years of experience, respectively, in Head and Neck radiology in consensus evaluated specimen MRI and measured the distance between the tumor and the specimen surface. We performed intraoperative bedside FSA. To compare the performance of bedside FSA and MRI in predicting definitive margin status we computed the weighted sensitivity (SE), specificity (SP), accuracy (ACC), area under the ROC curve (AUC), F1-score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). To express the concordance between FSA and <em>ex-vivo</em> MRI we reported the jaccard index.</p> <p><strong>Results: </strong> Intraoperative bedside FSA showed SE of 90%, SP of 100%, F1 of 95%, ACC of 0.9%, PPV of 100%, NPV (not a number), and jaccard of 90%, and <em>ex-vivo</em> MRI showed SE of 100%, SP of 100%, F1 of 100%, ACC of 100%, PPV of 100%, NPV of 100%, and jaccard of 100%. These results needed to be validated in a larger sample size of 21- 44 patients.</p> <p><strong>Conclusion: </strong> The presented method allows a more accurate evaluation of surgical margin status, and the first clinical experiences underline the high potential of integrating FSA with <em>ex-vivo</em> MRI of the fresh surgical specimen.</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.