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882 results for “3d model”
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 16:56 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 16:56 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_814-817 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 21 August 2018 at 21:31 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 21:31 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_767-773 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 Low Poly/VR/AR Model Viking Shield
3D Low Poly/VR/AR Model Viking Shield ready for games. Source: Objaverse 1.0 / Sketchfab
Turgut Alp Axe 3d model
from Diriliş: Ertuğrul Source: Objaverse 1.0 / Sketchfab
3D Treasure Chest Model
A 3D Model of Treasure Chest. Source: Objaverse 1.0 / Sketchfab
Antique Axe Head 3D Scanned Model Vintage Tool
Antique Single Bit Axe head we scanned in the LITEStudio, our multi function photo studio and 3D scanner. Looks like a Kentucky style Axe head, could be a Georgia or Virginia style as well. Source: Objaverse 1.0 / Sketchfab
Revealing nanoscale plasticity of metallic nanosponges with correlative and scale-bridging 3D microscopy and modelling
<p>Data for manuscript "Revealing nanoscale plasticity of metallic nanosponges with correlative and scale-bridging 3D microscopy and modelling":</p> <p>SI Movies 01-12. Movies from Supporting Information. Description in file name.</p>
Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
<p>Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively.</p> <p>Filenames denote the structure of the SS profile: </p> <p>???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}<br> </p> <p>LT = elastic lithosphere thickness (km)</p> <p>UMV = upper mantle viscosity (1E21 Pa s)</p> <p>LMV = lower mantle viscosity (1E21 Pa s)</p> <p>i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile</p> <p>96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity</p> <p> </p> <p>The columns of the input files are as follows:</p> <p>LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0)</p> <p>units for the above are as follows:</p> <p>km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m, mm/year</p> <p>where 'days since 2000' assumes exactly 365.25 days per year.</p>
Dataset of CCFs and 3D Vs model beneath the Tangshan Fault zone
<p>This project contains the datasets needed to produce the 3D detailed shallow shear wave velocity model beneath the Tangshan Fault zone.</p><p> </p><p>The zip file has the following files:</p><p>Z-Z_CCFs_SAC: Rayleigh wave cross-correlation functions (CCFs)</p><p>3DVelocityModel: 3D Vs model</p><p>Readme: Readme</p><p>station.txt: Station coordinates</p><p> </p><p>Each of the Z-Z_CCFs_SAC folders contains the final stacked CCFs in SAC format between all available station pairs, e.g., </p><p>COR_TS001_TS002.SAC</p><p>COR_TS004_TS017.SAC</p><p>COR_TS009_TS080.SAC</p><p> </p><p>The CCF file name show the names of station pairs (e.g., TS001, TS004, TS009).</p><p> </p><p>The 3DVelocityModel folders contains the 3D detailed shallow shear wave velocity model with the format of "longitude (deg), latitude (deg), depth (km), Vs (km/s)".</p><p> </p><p>The station.txt has a format of "sta_name, longitude (deg), latitude (deg)".</p>
Integration of 3D-printed middle ear models and middle ear prostheses in otosurgical training
<p>Additional files for article: Integration of 3D-printed middle ear models and middle ear prostheses in otosurgical training</p><p>Additional file 1: Simulation Video of drilling, scooping and placing a 3D-printed prosthesis (PORP) in the 3D-printed middle ear</p><p>Additional file 2: Responses of otosurgeons and ORL-HNS residents to the simulation questionnaire</p>
Superalloys fracture process inference based on overlap analysis of 3D models
<h2>Datasets and code utilized in the paper "Superalloys fracture process inference based on overlap analysis of 3D models"</h2> <h2>Code and data description</h2> <h3>Data for 3D reconstruction</h3> <ul> <li>Original SEM images of Fracture A - Fracture D obtained through the collection method in the paper.</li> </ul> <h3>Data for scale calibration</h3> <ul> <li>Original SEM images sequences of marked points 'dot1' and 'dot2' of Fracture A - Fracture D.</li> </ul> <h3>Sharpness score calculation</h3> <ul> <li>'shapeness.m' : Calculating image sharpness using normalized variance equations.</li> <li>'focus_A_data' - 'focus_D_data' : Sharpness scores for all images in the image sequences and their corresponding sample stage coordinates.</li> </ul> <h3>3D fracture models</h3> <ul> <li>Scale calibrated 3D models of Fracture A - Fracture D.</li> </ul> <h3>Description of internal cracks</h3> <ul> <li>Original images and EDS results for an illustration of the regions of internal crack generation in Fracture A</li> </ul> <p> </p> <p> </p>
3D models
An important thing for any viking Source: Objaverse 1.0 / Sketchfab
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
3D Models thumbnails
<p>3D Models image thumbnails</p>
Philoxenite 3D models: North-western part of area N1 including sectors 1–4
<p>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</p>
Philoxenite 3D models: North-western part of area N1 including sectors 1, 4 and 6
<p>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</p>
Philoxenite 3D models: North-western part of area N1 sector 6
<p>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</p>
Philoxenite 3D models: Northern part of area N1, sector 7
<p>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</p>
Philoxenite 3D models: North-western part of area N1 sector 8
<p>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</p>
3D Crustal Velocity Model of the Irpinia Region (Italy) obtained through Local Earthquake Tomography
<p>Supplementary materials for: "The 3D crustal structure in the epicentral region of the 1980, Mw 6.9, Southern Apennines earthquake (southern Italy): new constraints from the integration of seismic exploration data, deep wells and local earthquake tomography" by Feriozzi F., Improta L., Maesano F.E., De Gori, P., Basili R., Tectonics 2024</p>
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.