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63 results for “3D methods”
Simulation dataset to benchmark 3D force inference methods
<p>Dataset of 47 artificial images (.tif) and corresponding segmentation masks (.tif), generated from simulations of foam-like cell structures (early embryos) of various cell numbers (2 to 11), cell sizes and interfacial tensions.<br> The ground truth simulation tensions and pressures to be inferred are provided as Numpy arrays (.npy).</p> <p>This dataset was used to benchmark a method to infer cellular forces in 3D from microscopy images of multicellular contours, that is available on <a href="https://github.com/VirtualEmbryo/foambryo">https://github.com/VirtualEmbryo/foambryo</a>.<br> Non-manifold multimaterial meshes corresponding to artificial microscopy images are also provided as binary files (.rec) and may be opened with our delaunay-watershed Python code, available on <a href="https://github.com/VirtualEmbryo/delaunay-watershed">https://github.com/VirtualEmbryo/delaunay-watershed</a>.</p> <p><strong>Credits, contact, citations</strong><br> If you use this dataset, please cite the published version of the following preprint: <br> <em>Ichbiah, S., Delbary, F., McDougall, A., Dumollard, R., & Turlier, H. (2023). Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy. bioRxiv, 2023-04. </em><a href="https://doi.org/10.1101/2023.04.12.536641">https://doi.org/10.1101/2023.04.12.536641</a><br> <br> We hope that this dataset may be useful to benchmark future 3D force inference methods.<br> If you have any question on this dataset, please contact <a href="mailto:herve.turlier@college-de-france.fr?subject=%5BZenodo%5D%203D%20tension%20inference%20benchmark%20dataset">Hervé Turlier</a>.</p> <p><strong>License</strong><br> Copyright (c) 2023 Turlier Lab - <a href="https://www.turlierlab.com/">https://www.turlierlab.com/</a><br> This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 mm Macro lens and processed with Agisoft Photoscan. The visual comparison of the mollusc shows a similar level of detail between photogrammetry and MechScan for the external surfaces, with still a bit more detail for the MechScan. The HDI Advance has a much lower resolution.
Sample 3D image data from RIMS method for image analysis code demo
<p>Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.</p>
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).
A method to determine local aerodynamic force coefficients from fiber-resolved 3D flow simulations around a staple fiber yarn: simulation data
<p>This data set contains all set-up files and necessary scripts to run the simulations performed in the publication <a href="https://doi.org/10.1007/s11044-024-09992-2" target="_blank" rel="noopener">"A method to determine local aerodynamic force coefficients from fiber-resolved 3D flow simulations around a staple fiber yarn"</a>, published in Multibody System Dynamics.</p>
Combination of 1D, 2D, and 3D molecular descriptors for all the dopant-free HTMs without considering the variability introduced by fabrication methods on PSC efficiency
<p>1D, 2D, and 3D molecular descriptors for all the dopant-free HTMs <strong><span>without considering the variability introduced by fabrication methods on PSC efficiency </span></strong>along with their photovoltaic properties (PCE, JSC, and VOC)</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
Outputs from new methods for 3D+time cell image segmentation and tracking
<p>Segmentation and tracking of 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin.</p> <p>The file named 7_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named 7_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for seven cells (colored black) moving in time, and the file _tracking_of_7_cell_in_70_frames.mp4 has the tracking of these seven cells. </p> <p>Additionally, the file named group_of_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named group_of_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for the group of cells (colored black) moving in time and the file _tracking_of_group_of_cell_in_70_frames.gif has the tracking of this group of cells. </p>
A Mixed-Flux-Based Nodal Discontinuous Galerkin Method for 3D Dynamic Rupture Modeling
<p>This repository contains data produced by a mixed-flux-based discontinuous Galerkin method for 3D dynamic rupture modeling, using the software DRDG3D (<a href="https://github.com/wqseis/drdg3d">https://github.com/wqseis/drdg3d</a>). Input scripts for the SCEC/USGS dynamic rupture benchmark validation problems (<a href="https://strike.scec.org/cvws">https://strike.scec.org/cvws</a>) and other cases are hosted on DRDG3D's GitHub page. The preprint is published at ESS Open Archive (DOI: <a href="http://doi.org/10.1002/essoar.10512657.1">10.1002/essoar.10512657.1</a>).</p>
Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods
<p>Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.</p> <p> </p> <p><strong>Data from two studies</strong></p> <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol> <p> </p> <p><strong>Folders and Files</strong></p> <p><strong>valve-2D.zip </strong>is the folder with data from the idealized models of vertical and curved 2D valves:</p> <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>inflow-outflow-3D.zip </strong>is the folder with data from the inflow-outflow model in MPS:</p> <ul> <li>Fluid physical properties of water <ul> <li><span>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</span></li> </ul> </li> <li>Pipes of length <span>\(L=0.15m\)</span>: <ul> <li>circular section of diameter <span>\(D=0.1m\)</span>.</li> <li>square section of sides <span>\(S=0.1m\)</span>.</li> </ul> </li> <li>Constante pressure variation (<span>\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)</span>) between inflow and outflow: <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (<span>\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)</span>) between inflow and outflow <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>References</strong></p> <p><a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a></p> <p><a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a></p> <p><a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a></p> <p><a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a></p> <p><a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a></p>
A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method
Open the record for dataset details and reuse information.
A fast, precise, in-vivo method for micron-level 3D models of corals using dental scanners
<p>1. Several sampling and measurement strategies have been developed to assess biological forms in three dimensions (3D), including corals. However, the effectiveness (in speed and precision) of current 3D methods in scanning and model construction are challenging at small scales (μm – mm).</p> <p>2. In this paper, a practical 3D scanning and model construction tool using an intra-oral dental scanner was assessed to measure the surface area and volume of coral juveniles across multiple species. Intra-oral scanners using confocal imaging are fast, precise to the μm scale, and safe to use with live tissue, thereby eliminating the need to harm or kill the animals. The trial was conducted at the National Sea Simulator at the Australian Institute of Marine Science.</p> <p>3. High-quality 3D scans of individual coral juveniles were successfully generated and integrated automatically into high-resolution (μm) mesh from point clouds. The attained average scanning efficiency was < 2 min./individual, without a significant difference in speed given coral complexity or between live colonies or dead coral skeleton.</p> <p>4. Overall, this fast and precise system could become a promising tool for marine environmental surveys and restoration initiatives. This tool also removes the need to sacrifice animals for measurement analysis, thereby increasing conservation and animal welfare.</p>
Taxonomic classification of seabird long bones using 3D shape: A method with wider potential in zooarchaeology
<p>Dataset of manually-placed landmark (.pts) files. </p> <p>Fixed landmarks and semilandmarks were placed on 3D digitised (.ply) models and exported in .pts file format from Landmark Editor 3.0 (Institute of Data Analysis and Visualization IDAV, University California Davis, USA) (Wiley et al., 2005). The .pts files can be read into R (R Core Team., 2021) using the Morpho::read.pts function (Schlager, 2017). </p> <p> </p> <p>R Core Team. (2021). R: A language and environment for statistical computing. (Version 4.0.2). R Foundation for Statistical Computing, Vienna, Austria. <a href="https://www.r-project.org/">https://www.r-project.org/.</a></p> <p>Schlager, S. (2017). Morpho and Rvcg–Shape Analysis in R: R-Packages for geometric morphometrics, shape analysis and surface manipulations. In <em>Statistical shape and deformation analysis</em> (pp. 217-256). Elsevier.</p> <p>Wiley, D. F., Amenta, N., Alcantara, D. A., Ghosh, D., Kil, Y. J., Delson, E., Harcourt-Smith, W., Rohlf, F. J., St. John, K., & Hamann, B. (2005). Evolutionary morphing. <em>IEEE Visualization 2005 - (VIS'05)</em>, pp.55.</p>
Novel stereological method for estimation of cell counts in 3D collagen scaffolds
<p>Dataset provides all images used for cell number evaluation. The used macros are the part of Supplementary of the article.</p>
Raw coordinates of 3D landmarks related to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing Ovis aries morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al.
<p>Raw coordinates from 3D landmarks of <em>Ovis aries </em>astragali. These bones originate from Final Bronze Age archaeological contexts from central and southeastern Kazakhstan. These relate to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing <em>Ovis aries</em> morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al. </p>
A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry (Data)
<p>This is the underlying data for the publication "A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry" by Daniel D Conley and Erin N. R. Hollander published in 2021.</p>
Research Compendium for Himes et al. (2023): "Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods"
<p>This archive is the Reproducible Research Compendium for</p> <p>Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods</p> <p>by Himes, Harrington, and Baydin (2023), published in The Planetary Science Journal.</p> <p>The compendium includes all the software, documentation, configuration files, plots, and data published in the paper. The compendium is under the Reproducible Research Software License; see LICENSE file. The README provides additional information and describes the contents of each compressed .tar.gz file.</p>
3D Evaluation of Maxillary Expansion Methods
ClinicalTrials.gov study NCT07262892. IPD Sharing: YES. Countries: 1. Publications: 2.
ScienceDex guides
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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.