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908 results for “Mesh”
Armed to the teeth: Supplementary material, R code, Rdata, mesh, landmarks
<p>The structure, composition, and shape of teeth have been related to dietary specialization in many vertebrate species, but comparative studies on snakes' teeth are lacking. Yet, snakes have diverse dietary habits that may impact the shape of their teeth. We hypothesize that prey properties, such as hardness and shape, as well as feeding behavior, such as aquatic or arboreal predation, or holding vigorous prey, impose constraints on the evolution of tooth shape in snakes. We compared the morphology of the dentary teeth of 63 species that cover the phylogenetic and dietary diversity of snakes, using 3D geometric morphometrics and linear measurements. Our results show that prey hardness, foraging substrate, and the main foraging mechanical challenge are important drivers of tooth shape, size, and curvature. Overall, long, slender, curved teeth with a thin layer of hard tissue are observed in species that need to maintain a grip on their prey. Short, stout, less curved teeth are associated with species that undergo high or repeated loads. Our study demonstrates the diversity of tooth morphology in snakes and the need to investigate its underlying functional implications to better understand the evolution of teeth in vertebrates.</p>
MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations [Supplementary data]
<p>Supplementary data for '<a href="https://arxiv.org/abs/2211.00713">MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations</a>'. </p>
Venetian Lagoon mesh for SHYFEM: VenlagBio_20 and VenlagBio_20_shelf
<p>This dataset contains two high-resolution finite element meshes of the Venice lagoon developed for the SHYFEM hydrodynamical model. The grids rely on bathymetric surveys produced by the CORILA consortium in 2003 in the most shallow areas and 2013 bathymetric data of Madricardo et al. in the navigable channels. While “VenlagBio_20” is limited to the inner basin of the Venice lagoon, “VenlagBio_20_shelf'' includes an additional open-sea area. "VenlagBio_20" is formed by 6017 nodes and 10407 elements, while “VenlagBio_20_shelf'' contains 6688 nodes and 11525 elements. The depth of the meshes is relative to the IGM zero of 1942 (the actual italian-national reference depth level). The coordinates are defined in meters using the Gauss-Boaga projection EPSG:3004 (Monte Mario / Italy zone 2). These meshes were developed in the context of the VENEZIA2021 research program.</p>
3D ToF MPI multi-frequency mesh dataset
<p>The accuracy of indirect 3D Time-of-Flight (3D ToF) measurements is often limited by multi-path interferences (MPI) caused by multi-layer ToF conditions. Taking multiple measurements of the same scene at different modulation frequencies allows separating the interfering signal components of the individual paths according to several optimization methods described in literature.</p> <p>Orthogonal matching pursuit (OMP) optimization has been reported to achieve good path separation performance and superior results compared to particle swarm optimization (PSO). This work presents improved PSO performance for MPI separation based on new experimental data and refined PSO strategy.</p> <p>The current PSO approach achieves good distance separation in the setup used with low RMS distance errors in the order of 20 cm in situations where the OMP approach shows RMS errors higher than 100 cm. The previously reported minimum distance difference limitation between two separate objects of 2.7 m for the OMP algorithm could be reduced to roughly 0.75 m for the PSO algorithm. The trade-off between image accuracy and computing effort is explored and presented with respect to PSO parameter settings.</p> <p>This dataset provides researchers with measurement data to develop their own multi-layer algorithms and contribute to the ongoing development of great 3D ToF cameras.</p>
Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes
<p>We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to offload sets of patches to the GPU. Our studies show that multithreaded, concurrent, non-deterministic access to the GPU leads to performance breakdowns, since the GPU memory bookkeeping as offered through OpenMP's map clause, i.e., the allocation and freeing, becomes another runtime challenge besides expensive data transfer and actual computation. We, therefore, propose to retain the memory management responsibility on the host: A caching mechanism acquires memory on the accelerator for all CPU threads, keeps hold of this memory and hands it out to the offloading threads upon demand. We show that this user-managed, CPU-based memory administration helps us to overcome the GPU memory bookkeeping bottleneck and speeds up the time-to-solution of Finite Volume kernels by more than an order of magnitude.</p>
Example meshes for 'Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation'
<p>Example meshes for 'Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation'</p> <p>The meshes contain DTI. To read the mesh to FEniCS, see e.g., <a href="https://github.com/bzapf/braintransport/blob/24ba4e37a3d6fadb37c249ca7f717a7355f48f30/optimal_velocity/postprocess_phi.py#L12">here </a>.</p> <p>Find the simulation codes for the manuscript here:</p> <p><a href="https://github.com/bzapf/braintransport">https://github.com/bzapf/braintransport</a></p>
SeisSol Examples TPV33 mesh file
<p>This mesh file is being used to run the SCEC benchmark TPV33 with SeisSol.</p> <p>I have used the scripts in https://github.com/SeisSol/Examples/commit/2158653e100100c1570cbd5dbdc7172719762753 to create the mesh.</p>
Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Atrial meshes
Open the record for dataset details and reuse information.
Raw electrophysiology recording files from toads implanted with flexible mesh electronics
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Armed to the teeth: Supplementary material, R code, Rdata, mesh, landmarks
Open the record for dataset details and reuse information.
Full information on the eORCA1 grid (mesh_mask) used in IPSL-CM6A-LR configuration
<p>This file contains all relevant information on the ORCA1 grid. See https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf for more details on the grid.</p>
Buddha - Draft Meshing from SfM
This model is the result of draft meshing directly from Structure-from-Motion output. It has been cleaned up in Meshlab (bounding box cut-out) and retextured in Meshroom. **Details** - Meshroom 2019.1.0 - Input: 220 images - Camera: Sony A7S II Source: Objaverse 1.0 / Sketchfab
Meshes and initial data for 2D and 3D experiments with PyNosh
<p>Data files used in Experiments with PyNosh.</p>
TVD differencing on three-dimensional unstructured meshes with monotonicity-preserving correction of mesh skewness (Accompanying data)
<p>This data set contains the data accompanying the article F. Denner and B. van Wachem, TVD differencing on three-dimensional unstructured meshes with monotonicity-preserving correction of mesh skewness, Journal of Computational Physics (2015), http://dx.doi.org/10.1016/j.jcp.2015.06.008.</p>
Triangular Mesh of the Brain of a Lion (Panthera leo)
<p>Triangular Mesh of the Brain of a Lion (<i>Panthera leo</i>) from http://braincatalogue.org/Lion</p>
PyGBe regression test meshes
<p>This zip-file includes the meshes needed to run the regression tests of 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> <p>Note:</p> <ul> <li> Lysozyme folder: contains the meshes for all the Lysozyme test.</li> <li>sphere folder: contains the meshes related to the one-sphere cases and two-spheres ones. </li> </ul>
Results for paper "Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators"
<p>This spreadsheet contains the results used to generate the plots in the paper "Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators".</p>
In-memory integration of existing software components for parallel adaptive unstructured mesh workflows: PHASTA 3D dam break
<p>Input mesh, solution field, configuration files, and job scripts for running the adaptive PHASTA-Chef 3D dam break case.</p> <p>Data stream, POSIX file, and ramdisk results are also included. The python scripts used to generate the bandwidth and open/close time plots are included.</p>
PyGBe-LSPR Convergence-Test Meshes
<p>This zip-file includes the meshes needed to run the convergence tests of the Localized Surface Plasmon Resonance application of PyGBe, a Barba-group open-source Python GPU code that does nanoparticle plasmonics calculations. https://github.com/barbagroup/pygbe</p> <p>Note:</p> <ul> <li>sphere_lspr folder: contains the meshes for all the single nano-sphere tests. </li> <li>sphere_multiple_lspr: contains the meshes for the multiple nano-spheres test. </li> </ul> <p> </p>
Greenland ice sheet self-consistent spatial discretization mesh
<p>Greenland ice sheet<br> ===================</p> <p>Two unstructured mesh spatial discretisations of the Greenland ice sheet, with minimum element sizes of 5km and 1km.</p> <p>These are stored in unstructured VTU files defined by the visualisation toolkit VTK [2].</p> <p>Two associated state PVSM files for Paraview [3] are also provided to reproduce visualisations shown in [1]. Note that Paraview requires absolute pathnames, so it may be necessary to edit file references to the VTU files in these state files.</p> <p>Files<br> -----</p> <p>- GreenlandIcesheet1km.vtu<br> - GreenlandIcesheet5km.vtu<br> - GreenlandIcesheet1km.pvsm<br> - GreenlandIcesheet5km.pvsm</p> <p>Author<br> ------</p> <p>- Dr Adam S. Candy <a.s.candy@tudelft.nl>, <candy@cantab.net><br> - Technische Universiteit Delft<br> - Imperial College London</p> <p>References<br> ----------</p> <p>[1] Candy, A.S., 2016. A consistent approach to unstructured mesh generation for geophysical models. In review. Preprint available at https://arxiv.org/abs/1703.08491.</p> <p>[2] The Visualization Toolkit (VTK), version 5.10.1. URL: http://www.vtk.org.</p> <p>[3] Paraview, version 4.3.1. https://www.paraview.org.</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.