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908 results for “Mesh”
Marlim R3D (MR3D) - Extended Resistivity Model (100 x 100 x 20 m mesh)
<p>Marlim R3D (MR3D) is an open-source realistic geoelectric model for CSEM simulations of the post-salt turbiditic reservoirs at the Brazilian offshore margin.<br> Here we make available the Marlim MR3D model with a 100 x 100 x 20 m mesh extended to a volume of 56 x 51 x 6 km. That extended model was used by Correa and Menezes (2019) to produce the two CSEM dataset available at https://doi.org/10.5281/zenodo.1807135 (noise-free) and http://doi.org/10.5281/zenodo.1256787 (1% added noise).<br> You can follow the updates of the marlim project at:<br> https://www.researchgate.net/project/Marlim-R3D-A-realistic-model-for-mCSEM-simulations<br> Correa, J. L., and Menezes, P. T. L., 2019. Marlim R3D: A realistic model for controlled-source electromagnetic simulations — Phase 2: The controlled-source electromagnetic data set. Geophysics, 84 (5), E293–E299.</p> <p> </p> <p>Mrl3d_Rh_20metersmesh.segy - horizontal resistivity</p> <p>Mrl3d_Rv_20metersmesh.segy - vertical resistivity</p>
The Quasi-Uniform and Variable-Resolution Mesh files for GRIST
<p>This entry contains the GRIST mesh files to enable the tests in the submitted paper. The "g10.tar.gz" file contains the quasi-uniform mesh of the G10 grid, the "g8-qu.tar.gz" file contains the quasi-uniform mesh of the G8 grid, and the "g8-vr.tar.gz" file contains the variable-resolution mesh of the G8 grid.</p>
The QU and VR Meshes for GRIST
<p>This entry contains the GRIST mesh files to enable the tests in the submitted paper. The "g10.tar.gz" file contains the quasi-uniform mesh of the G10 grid, the "g8-qu.tar.gz" file contains the quasi-uniform mesh of the G8 grid, and the "g8-vr.tar.gz" file contains the variable-resolution mesh of the G8 grid.</p>
A Publicly Available Virtual Cohort of Four-chamber Heart Meshes for Cardiac Electro-mechanics Simulations
<p><strong>Motivation: </strong> Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients.</p> <p><strong>Dataset Description: </strong>We present the first database of four-chamber heart models suitable for electro-mechanical simulations. Our database consists of twenty-four four-chamber heart models generated from end-diastolic CT acquired from heart failure patients recruited for cardiac resynchronization therapy upgrade. We also provide a higher resolution version for each of the twenty-four meshes.</p> <p>We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1.1mm. The elements of all the twenty-four meshes are labelled as follows: 1) Left ventricle myocardium 2) Right ventricle myocardium 3) Left atrium myocardium 4) Right atrium myocardium 5) Aorta wall 6) Pulmonary artery wall 7) Left atrium appendage ring 8) Left superior pulmonary vein ring 9) Left inferior pulmonary vein ring 10) Right inferior pulmonary vein ring 11) Right superior pulmonary vein ring 12) Superior vena cava ring 13) Inferior vena cava ring 14) Mitral valve plane 15) Tricuspid valve plane 16) Aortic valve plane 17) Pulmonary valve plane 18) Left atrial appendage valve plane 19) Left superior pulmonary vein valve plane 20) Left inferior pulmonary vein valve plane 21) Right inferior pulmonary vein valve plane 22) Right superior pulmonary vein valve plane 23) Superior vena cava valve plane 24) Inferior vena cava valve plane.</p> <p>Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80˚ to -60˚, respectively. We defined a system of universal ventricular coordinates on the meshes, see Figure 1B: an apico-basal coordinate varying continuously from 0 at the apex to 1 at the base; a transmural coordinate varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate varying continuously from – π at the left ventricular free wall, 0 at the septum and then back to + π at the left ventricular free wall; intra-ventricular coordinate defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -100. </p> <p>We also refined each mesh from 1.1mm resolution down to 0.39mm resolution. Each refined mesh has tags defined on its elements (same numbering as described above) and ventricular fibres.</p> <p><strong>Database format: </strong>We provide a zipped folder for each mesh. Each folder contains the coarse and the finer versions of the same mesh. All twenty-four 1mm-meshes are supplied in case format, readable with paraview. All binary files containing the meshes data (ens and geo formats) are provided within the zipped folder. Points coordinates are given in mm. Element tags are assigned to the elements of the mesh as well as fibres and sheet directions. Fibres and sheet directions are assigned to the ventricles according to a rule-based method, while non-ventricular elements are assigned with default vectors [1; 0; 0] and [0; 1; 0]. UVCs are assigned to the nodes of the meshes. We also provide the location of the cardiac resynchronisation therapy right-ventricular electrode used to initiate ventricular excitation. This is given as a label on the nodes called electrode endo rv, which is 1 at the stimulated nodes. Finer meshes are provided in vtk format, also readable in paraview. For these meshes, we provide element tags, fibres and sheet directions on the ventricles, all in the same file.</p>
FESOM2. Results of one year simulations with pi mesh (default, not rotated).
<p>FESOM2. Results of one year simulations with pi mesh (default, not rotated).</p>
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>
Fort San Lorenzo, Panama Lidar Mesh RAW
3D Model, from Lidar data decimated. The data of Fort San Lorenzo was collected in March 2013 by Mr. Daryl Johnson, Founder of Summit Engineering and Design in Ferndale, Washington. The data was donated to CyArk who archived the data and provided a copy to the site. Located on the west bank of the modern Panama Canal, Fort San Lorenzo is an outstanding example of Spanish Colonial military architecture. Construction of the structure was started in 1595 upon the orders of King Philip the Second of Spain to protect the mouth of the Chagres River, a strategic waterway connecting the Atlantic and Pacific oceans. CyArk 2018: Fort San Lorenzo - LiDAR - Terrestrial . Collected by Summit Engineering and Design . Distrubuted by Open Heritage 3D. https://doi.org/10.26301/jndy-6p72 Source: Objaverse 1.0 / Sketchfab
Court House Simplified 3d Mesh
Source: Objaverse 1.0 / Sketchfab
Gabriel archangel (mesh)
Budapest - Heroes square Source: Objaverse 1.0 / Sketchfab
Menino Lobo Mesh
Source: Objaverse 1.0 / Sketchfab
Sandford Stm Simplified 3d Mesh
Sandford St Martin Model photogrammetry Drone Photography Source: Objaverse 1.0 / Sketchfab
Temple of Baal | Mesh
Temple of Baal.; inner part of the "Cella" with northern. Syria Tadmur, None. [Between 1898 and 1946] Photograph. Retrieved from the Library of Congress, https://www.loc.gov/item/mpc2005002233/PP/. (Accessed August 05, 2017.)  Source: Objaverse 1.0 / Sketchfab
Archeo Simplified 3d Mesh
Chantier de fouille archéologique de la villa romaine. Source: Objaverse 1.0 / Sketchfab
PyGBe Performance Check Meshes
<p>This zip-file includes the meshes needed to run reproduce performance benchmarks for PyGBe, a Barba group open source Python GPU code for Boundary elements that does molecular electrostatics calculations with a continuum approach. https://github.com/barbagroup/pygbe</p>
Unstructured meshes for extrusion article
<p>Unstructured meshes for extrusion article.</p>
Reference Mesh and Point Cloud
<p>Data Structure</p><p>gt_mesh.ply (cleaned reference mesh)</p><p>gt_pd.ply (reference faro scanner point cloud)</p><p>icp_kinect (the transformation matrix that is used to transform Kinect pose stored in transformation_colmap.json to align with the gt_mesh/pd )</p><p>icp_iphone (the transformation matrix that is used to transform iPhone pose stored in transformation_colmap.json to align with the gt_mesh/pd )</p>
TSA Model3 Simplified 3d Mesh
This is a 3D map of TSA Source: Objaverse 1.0 / Sketchfab
Annaberg Plantation Usa, Mesh
Mesh from photogrammetry, drone Trimble UX5 Site Description The Annaberg Sugar Plantation in the Virgin Islands is part of one of the largest historic migrations of people and resources throughout the world. For centuries, the Virgin Islands was a crossroads for indigenous peoples, colonial powers, and the transatlantic slave trade. The plantation structures that the National Park Service preserves on the site today demonstrate the agricultural success of the Danish Colonial era, but also its human cost. A 40-foot high windmill constructed with sails to direct the wind was state of the art at the time of its completion in 1805. Not far from the windmill, built into the foundation of the sick house, the Annaberg "Dungeon" served as a means of punishment for enslaved laborers....more info: https://doi.org/10.26301/vv0h-dq68 CyArk 2019: Annaberg Plantation - LiDAR - Terrestrial , Photogrammetry . Collected by CyArk , Trimble . Distrubuted by Open Heritage 3D. https://doi.org/10.26301/vv0h-dq68 Source: Objaverse 1.0 / Sketchfab
Simón Bolívar Scanner 3D Simplified 3d Mesh
Source: Objaverse 1.0 / Sketchfab
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.