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Dataset results
908 results for “Meshes”
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 9 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 9 August 2017. 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_566-DJI_570 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 the Vaigat Strait (NW Greenland) on 3 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 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. 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 the Vaigat Strait (NW Greenland) on 6 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 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. 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> <p> </p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 22 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. 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_659-662 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 Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. 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_528, 530, 531-533 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 Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. 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_330, 332, 334, 335, 336, 337, 338, 339 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>
Full information on the eORCA1 grid (mesh_mask) used in IPSL-CM6A-LR configuration
<p>eORCA1.2_mesh_mask.nc : This file contains all relevant information on the eORCA1 grid used in the NEMO_v3.6_STABLE configuration of the oceanic module of the IPSL-CM6A-LR climate model. See https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf for more details on the grid.</p> <p>eORCA_R1_bathy_meter_v2.2.nc: This file contains the bathymetry of the eORCA1 configuration used in IPSL-CM6A-LR.</p>
4DMRI moving lung meshes
<p><strong>Moving lung meshes extracted from 4DMRIs</strong></p> <p>The newest version of the dataset and the documentation can be found on <a href="http://gitlab.psi.ch/duetschler_a/4DMRI_moving_lung_meshes">https://gitlab.psi.ch/duetschler_a/4DMRI_moving_lung_meshes</a>.</p> <p>The moving lung meshes can for example be used to generate synthetic 4DCT(MRI)s. The code for generating 4DCT(MRI)s using a reference CT and the moving lung mesh data can be found on <a href="http://gitlab.psi.ch/duetschler_a/4DMRI_moving_lung_meshes">https://gitlab.psi.ch/duetschler_a/4DCT-MRI</a>.</p> <p>Please cite the following publication when using the data:</p> <p>Duetschler, A., Bauman, G., Bieri, O., Cattin, P.C., Ehrbar, S., Engin-Deniz, G., Giger, A., Josipovic, M., Jud, C., Krieger, M., Nguyen, D., Persson, G.F., Salomir, R., Weber, D.C., Lomax, A.J. and Zhang, Y. (2022), <em>Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations.</em> Med. Phys.. <a href="https://doi.org/10.1002/mp.15591">https://doi.org/10.1002/mp.15591</a></p>
Triangular mesh of the Brain of a Cheetah (Acinonyx jubatus)
<p>Triangular mesh of the Brain of a Cheetah (<i>Acinonyx jubatus</i>) from http://braincatalogue.org/Cheetah</p>
Triangular Mesh of the Brain of a Nile Crocodile (Crocodylus niloticus)
<p>Triangular Mesh of the Brain of a Nile Crocodile (<i>Crocodylus niloticus</i>) from http://braincatalogue.org/Nile_crocodile</p>
Triangular Mesh of the Brain of a Slow Loris (Nycticebus)
<p>Triangular Mesh of the Brain of a Slow Loris (<i>Nycticebus</i>) from http://braincatalogue.org/Slow_loris</p>
Triangular Mesh of the Brain of a Leopard (Panthera pardus)
<p>Triangular Mesh of the Brain of a Leopard (<i>Panthera pardus</i>) from http://braincatalogue.org/Leopard</p>
Triangular Mesh of the Brain of a Orangutan (Pongo pygmaeus)
<p>Triangular Mesh of the Brain of a Orangutan (<i>Pongo pygmaeus</i>) from http://braincatalogue.org/Orangutan</p>
Triangular Mesh of the Brain of a Sloth Bear (Melursus ursinus)
<p>Triangular Mesh of the Brain of a Sloth Bear (<i>Melursus ursinus</i>) from http://braincatalogue.org/Sloth_bear</p>
Triangular Mesh of a Black Rhinoceros Brain (Diceros Bicornis)
<p>Triangular Mesh of a Black Rhinoceros Brain (Diceros Bicornis) from http://braincatalogue.org/Black_rhinoceros.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - difference between meshes
<p>The difference between the overview mesh and mesh used for SDF for all ROI.</p> <p>Black is no difference, i.e. baseline, objects removed for SDF processing are red.</p>
Imperial College London femur and tibia surface mesh set
<p>The dataset contains bone geometries of the left and right thigh (femur) and shank (tibia and fibula) segmented from magnetic resonance (MR) scans of 35 healthy volunteers (22 male, 13 female; height 155 cm to 193 cm, weight 45 kg to 108 kg, body mass index 17.0 kg/m<sup>2 </sup>to 34.8 kg/m<sup>2</sup>).</p> <p>If you are using this dataset, please cite:</p> <p>Nolte, D., Tsang, C.K., Zhang, K., Ding, Z., Kedgley, A.E., Bull, A.M.J., 2016. Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models. J. Biomech. 49, 3576–3581. doi:10.1016/j.jbiomech.2016.09.005</p>
A geometry preserving, conservative, mesh-to-mesh isogeometric interpolation algorithm for spatial adaptivity of the multigroup, second-order even-parity form of the neutron transport equation
<p>In this paper a method is presented for the application of energy-dependent spatial meshes applied to the multigroup, second-order, even-parity form of the neutron transport equation using Isogeometric Analysis (IGA). The computation of the inter-group regenerative source terms is based on conservative interpolation by Galerkin projection. The use of Non-Uniform Rational B-splines (NURBS) from the original computer-aided design (CAD) model allows for efficient implementation and calculation of the spatial projection operations while avoiding the complications of matching different geometric approximations faced by traditional finite element methods (FEM). The rate-of-convergence was verified using the method of manufactured solutions (MMS) and found to preserve the theoretical rates when interpolating between spatial meshes of different refinements. The scheme’s numerical efficiency was then studied using a series of two-energy group pincell test cases where a significant saving in the number of degrees-of-freedom can be found if the energy group with a complex variation in the solution is refined more than an energy group with a simpler solution function. Finally, the method was applied to a heterogeneous, seven-group reactor pincell where the spatial meshes for each energy group were adaptively selected for refinement. It was observed that by refining selected energy groups a reduction in the total number of degrees-of-freedom for the same total L2 error can be obtained.</p>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
Mesh dataset for the paper "Bending-Reinforced Grid Shells for Free-form Architectural Surfaces"
<p>If using this dataset, please cite the paper: </p> <p>Laccone, F., Pietroni, N., Cignoni, P., Malomo, L.: Bending-Reinforced Grid Shells for Free-form Architectural Surfaces. Computer-Aided Design. Available online 23 December 2023, 103670.</p> <p>https://doi.org/10.1016/j.cad.2023.103670</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.