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908 results for “Mesh”
Torso Organ Meshes
<p>High density smooth triangulated organ surfaces derived from the <a href="http://lifesciencedb.jp/bp3d/" target="_blank" rel="noopener">BodyParts3D</a> database by The Database Center for Life Science, and a corresponding labelled tetrahedral mesh of the human torso incorporating the organ surfaces.</p> <p>Organs:</p> <ul> <li>Lungs</li> <li>Heart (wall, without valves) with two blood volumes for left and right side.</li> <li>Trachea and major bronchi lumen (not walls), i.e. air.</li> <li>Major blood vessels (truncated, rounded off): <ul> <li>SVC: Superior Vena Cava (wall)</li> <li>IVC: Inferior Vena Cava (wall)</li> <li>PA: Pulmonary Artery (wall)</li> <li>LSPV: Left Superior Pulmonary Vein</li> <li>LIPV: Left Inferior Pulmonary Vein</li> <li>RSPV: Right Superior Pulmonary Vein</li> <li>RIPV: Right Inferior Pulmonary Vein</li> </ul> </li> </ul> <p>Coordinates are provided in millimetres. Organ shapes were adapted to avoid intersections and increase spacing where necessary. Vessel walls are 2 mm thick.</p> <p>Tetrahedral meshes are provided as Gmsh MSH files. </p> <p>Triangulations are provided as OBJ and binary STL files for individual surfaces. These need to be merged (flipping the normals in the process as appropriate) to create closed surfaces. </p> <p>There are four surfaces per vessel:</p> <ul> <li>outer surface</li> <li>inner surface</li> <li>"ring": a surface connecting inner and outer at a cut between heart wall and vessel wall</li> <li>"cap": a surface closing the inner surface, creating a blood volume</li> </ul> <p>FEM models with electrodes may be generated using <a href="https://eidors3d.sourceforge.net/" target="_blank" rel="noopener">EIDORS</a>.</p>
Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks - Supporting Dataset
<div>Supporting dataset for the numerical experiments in the manuscript <em>Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks</em>, consisting of a tar.gz archive containing the following directories:</div> <h3>learnext_dataset</h3> <div>Dataset used to train the DeepONet mesh motion model. For one period of structure deformation in the FSI benchmark problem 2 of Turek and Hron (2006), contains the harmonic mesh motion in input and biharmonic mesh motion in output, relative to the undeformed domain.</div> <h3>mesh</h3> <div>Mesh of the FSI benchmark problem 2 used to run FSI simulations to test DeepONet mesh motion.</div> <h3>Warmstart checkpoint</h3> <div>State checkpoint of FSI benchmark problem 2 run for 15 simulation seconds with trained DeepONet mesh motion. Used to warmstart the FSI simulations to verify quantities of interest produced from DeepONet mesh motion by comparing it with ones from biharmonic mesh motion.</div> <h3>grav-test</h3> <div>Dataset used in gravity-driven deformation test of DeepONet mesh motion.</div> <h3>best_run_model</h3> <div>Saved, pretrained branch and trunk networks from the best run of the hyperparameter study and problem-file needed to build the DeepONet mesh motion from it.</div>
Input data of the mixed meshes used in: J. Grošelj, M. Kapl, M. Knez, T. Takacs, V. Vitrih, C1-smooth isogeometric spline functions of general degree over planar mixed meshes: The case of two quadratic mesh elements, Applied Mathematics and Computation 460 (2024) 128278; DOI: 10.1016/j.amc.2023.128278
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Datasets used in "A 23-Year Severe Hail Climatology Using GridRad MESH Observations"
<p>These datasets are those used in the Murillo et al. (2021) publication in the AMS journal of Monthly Weather Review. For any questions, please email Dr. Murillo at emurillo@ucar.edu.</p>
Inputs for 2D HEC-RAS model for sensitivity analysis of DEM and mesh grid resolutions over part of the Virginia Tech StREAM Lab
<p>Inputs into a 2D HEC-RAS model used in the article written by Elizabeth M. Prior, Nathan Michaelson, Jonathan A. Czuba, Thomas J. Pingel, Valerie A. Thomas, and W. Cully Hession titled "Lidar DEM and computational mesh grid resolutions modify roughness in 2D hydrodynamic models".</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater digitalised from AMT-7 1998.
<p>Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we have digitalised measurements from 1998 taken during AMT-7 (Atlantic Meridional Transect), which may be useful for future investigators. The measurements are originally presented in the following reference:</p> <p>Kettle, A. J., Rhee, T. S., von Hobe, M., Poulton, A., Aiken, J., and Andreae, M. O.: Assessing the flux of different volatile sulfur gases from the ocean to the atmosphere, J. Geophys. Res., 106, 12193–12209, https://doi.org/10.1029/2000JD900630, 2001.</p> <p><br>Measurements from AMT-7 were extracted from the graphs in the manuscript using an image extraction technique (https://apps.automeris.io/wpd/, last accessed July 2024). To ensure concurrency of the measurements and deal with the error in the time stamp related to the use of an image extraction technique, data from AMT-7 was binned in 6-hourly bins. Data gaps are listed with an improbable number of -999.</p> <p> </p> <p><span>Definitions of acronyms, site abbreviations, or other project-specific designations:</span></p> <p><span>Lat=latitude (negative indicates south)</span></p> <p><span>Lon=longitude (negative indicates west)</span></p> <p><span>t_Kettle01 = The mean measured concentration during each 6 hour bin is shown, 3 hours either side of the listed time. The timestamp indicates sampling time in local solar time, expressed as DD/MM/YYYY HH:MM</span></p> <p><span>MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3)</span></p> <p><span>DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3)</span></p> <p><span>Chla_mg_m3 = surface chlorophyll a concentration in mg m^(-3)</span></p> <p><span>SST_C= sea surface temperature in degrees Celsius </span></p> <p><span>SSS_PSU= sea surface salinity in practical salinity units (PSU)</span></p>
Enhancing the Agricultural Efficiency of Welded Mesh Solutions
<p>Modern agriculture is critically dependent on productivity and efficiency. Farmers are perpetually in pursuit of innovative methods to optimize operations and augment productivity. One solution that is gaining popularity is welded mesh, a flexible material that offers numerous benefits in a variety of agricultural applications.</p> <h1>Agricultural Landscapes Welded mesh undergoes transformation.</h1> <p>Contemporary farming methods are revolutionized by the adaptability and efficacy of <a href="https://www.dukesmetal.com/meshes/" target="_blank" rel="noopener"><strong>welded mesh</strong></a> solutions in a variety of applications. Welded mesh offers unparalleled durability and adaptability, making it suitable for a wide range of applications, including perimeter fencing, animal enclosures, and agricultural protection. It is an excellent choice for ensuring sustainable farming practices and enhancing operational efficiency due to its adaptability to satisfy a variety of agricultural needs.</p> <p>The utilization of welded mesh in agriculture has significantly altered the methods of infrastructure development employed by farmers. The modular design of the farm enables it to rapidly adjust to evolving requirements due to its scalability and ease of installation. Whether used as trellises for vertical farming or to establish secure boundaries for cattle, welded mesh solutions provide a robust foundation that supports agricultural productivity and encourages optimal land use.</p> <h2>The Advantages of Utilizing Welded Mesh Systems</h2> <p>Farmers can derive numerous advantages from the implementation of welded mesh systems in order to optimize agricultural productivity. In addition to its structural strength and endurance, welded mesh enhances farm security, reduces labour expenses, and promotes environmentally friendly agricultural practices. These systems promote sustainable agriculture by optimizing agricultural yield through efficient land management and protection, while simultaneously reducing resource waste.</p> <p>Welded mesh systems provide benefits that extend beyond structural integrity for the purpose of enhancing farm management methods. By establishing secure boundaries and confinement areas, farmers mitigate the risks of wildlife encroachment and illicit entry. This proactive approach not only safeguards invaluable assets but also cultivates an environment that is conducive to sustainable agricultural practices. Moreover, the modular design of welded mesh systems enables the flexible application of these systems to a variety of agricultural applications, including modest family farms and substantial commercial operations.</p> <h2>An Innovative Producer of Perforated Metal That is Revolutionizing Agricultural Methods</h2> <p>Perforated metal, which is manufactured by industry leaders, is a critical factor in the evolution of agricultural techniques. It is a critical instrument for the construction of efficient fencing and enclosures due to its versatility and durability. Farmers may be guaranteed of low maintenance costs and long-term dependability due to its resistance to external factors.</p> <p>Perforated metal panels are meticulously crafted by specialized producers to withstand the severe conditions of agricultural settings. They provide robust barriers that effectively protect cattle and produce from external threats. The high-strength composition of these materials may provide farmers with the assurance of longevity and significant cost reductions in comparison to traditional fencing materials.</p> <h2>Benefits of Selecting a Reliable Steel Wire Supplier</h2> <p><a href="https://www.dukesmetal.com/product/wire-rope-and-cable/" target="_blank" rel="noopener"><strong>Steel wire supplier</strong></a> play a substantial role in the agricultural industry by providing the premium materials necessary for the production of welded mesh. Farmers can be confident that their products meet the highest quality standards due to their expertise in procurement and processing. These vendors enable the improvement of agricultural efficacy by offering a wide range of products, including intricate mesh designs for crop protection and durable fencing wires.</p> <p>It is imperative that producers seeking welded mesh solutions choose a reliable supplier of steel wire. These sources offer a wide range of wire varieties, including those that are flexible, corrosion-resistant, and strong, to accommodate specific agricultural requirements. Farmers are able to install durable fencing and enclosure systems that can withstand the test of time and environmental challenges due to their collaboration with perforated metal producers.</p>
hypergraphs created from meshes of the RPI Formula Hybrid suspension upright
<p>MDS/PUMI meshes were converted to hypergraphs<br> (mesh elements -> graph vertices, mesh vertices -> hyperedges)<br> using the `testFileIO` tool from EnGPar (git hash 82fbd65).<br> </p>
meshes of the RPI Formula Hybrid suspension upright
<p>Meshes generated with Simmetrix SimModSuite and converted to PUMI/MDS using PUMI (d7ec4ca)</p>
Synthetic meshes of hippocampi, for statistical shape analysis validation
<p>Those synthetic data have been generated for the validation of the iterative centroid method published in "Statistical shape analysis of large datasets based on diffeomorphic iterative centroids", which code can be found here: https://github.com/cclairec/Iterative_Centroid</p> <p>The 50 random populations are all generated from a same shape S0. We generated random deformations around S0 and symmetrised the deformations, so the centre of the populations is S0. More details in the paper.</p> <p>Each data set is composed by 50 shapes. The Matlab files contain:</p> <p>- moment: The initial momentum vector used to generate each subject of the population</p> <p>- SkelSuj_Rigid: The first line contains the vertices and faces of each subject. The second line contains the parameters to generate the initial momentum vectors and the subjects.</p>
Mesh for an artificial fishtail
<p>Tetrahedron mesh for the artificial Fishtail described in <a href="https://www.sciencedirect.com/science/article/pii/S2405896318300594">Modeling and Motion Planning for an Artificial Fishtail</a>. The Mesh has been created with <a href="https://www.csimsoft.com/trelis.jsp">Trelis</a>.</p> <p>The file meas_pos.csv contains some virtual measurement positions.</p>
Audio from: 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model'
<p>Audio files associated with the paper 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model', presented at the International Congress of Phonetic Sciences 2019, Melbourne, Australia.</p>
A comparison between SBP-SAT and DG operators for computational aeroacoustics on non-conforming meshes
<p>Contains:</p> <ul> <li>classic SBP operators of global order three, four and five</li> <li>classic DRP SBP operators of global order three, four and five</li> <li>DDRP SBP operator of order four using 32 nodes</li> <li>Projection operators for h- and p-refinement</li> </ul>
Schmid et al. 2014, Supplementary material and FEM meshes
<p>Supplementary material to "Inference of alveolar capillary network connectivity from blood flow dynamics"<br>Kerstin Schmid, Andy L. Olivares, Oscar Camara, Wolfgang M. Kuebler, Matthias Ochs, Andreas C. Hocke, and Sabine C. Fischer</p> <p>Content:</p> <ul> <li>Supplementary material to the paper</li> <li>Four exemplary meshes for Ansys fluent for <ul> <li>two different randomly generated alveolar capillary networks (A1 and A2)</li> <li>with one arteriole (1a) and one venule (1v) each with diameter 20 µm </li> <li>as coarse (M) and as fine mesh (S).</li> </ul> </li> </ul> <p>All other meshes used in the publication are available from sabine.fischer@uni-wuerzburg.de</p>
Mesh-Chebi SSSOM Mapping File
<p>This data file gives a one-to-one mapping between a MESH term and a CHEBI term. This mapping data was derived through CAS terms, a third dictionary. Both the CHEBI ontology and the MESH dictionary associates their terms to CAS term (xrefs).</p>
GRIST-QUVR-Mesh
<p>Quasi-uniform (QU) and variable-resolution (VR) mesh files for supporting reviews and documents.</p> <p>---</p> <p>2021 added:</p> <p>1. x20.835586 (EA zoom; points from a MPAS 60-3km mesh).</p>
Native space mesh descriptors computed with FreeSurfer 6 for all ABIDE I subjects
<p># ABIDE I FreeSurfer 6 'native space descriptors' data</p> <p><br> This archive contains the following ABIDE I FreeSurfer 6 native space mesh descriptors:<br> thickness, area, volume, sulc, curv, jacobian_white.</p> <p><br> ## Credits</p> <p>This data is derived from the MRI scans of the ABIDE I dataset:</p> <p>* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/</p> <p>Quoting from that website:</p> <p> "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first<br> ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17<br> international sites, sharing previously collected resting state functional<br> magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets<br> made available for data sharing with the broader scientific community.<br> This effort yielded 1112 dataset, including 539 from individuals with<br> ASD and 573 from typical controls (ages 7-64 years, median 14.7 years<br> across groups). This aggregate was released in August 2012. Its<br> establishment demonstrated the feasibility of aggregating resting<br> state fMRI and structural MRI data across sites; the rate of these<br> data use and resulting publications (see Manuscripts) have shown its<br> utility for capturing whole brain and regional properties of the brain<br> connectome in Autism Spectrum Disorder (ASD). In accordance with<br> HIPAA guidelines and 1000 Functional Connectomes Project / INDI<br> protocols, all datasets have been anonymized, with no protected<br> health information included."</p> <p>Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).<br> The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.</p> <p>## How this data was produced</p> <p>The following steps were used to create the data:</p> <p>* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).<br> * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.<br> - We did not run any quality metrics on the scans or exclude any subjects.</p> <p><br> ## Contained files</p> <p>* In order to reduce the size of this dataset, for each subject, we only included the following files:<br> - <subject>/surf/lh.thickness : the per-vertex cortical thickness for the left hemisphere<br> - <subject>/surf/rh.thickness : the per-vertex cortical thickness for the right hemisphere<br> - <subject>/surf/lh.area : the per-vertex area of the white surface for the left hemisphere<br> - <subject>/surf/rh.area : the per-vertex area of the white surface for the right hemisphere<br> - <subject>/surf/lh.volume : the per-vertex cortical volume for the left hemisphere<br> - <subject>/surf/rh.volume : the per-vertex cortical volume for the right hemisphere<br> - <subject>/surf/lh.sulc : the per-vertex sulcal depth for the left hemisphere<br> - <subject>/surf/rh.sulc : the per-vertex sulcal depth for the right hemisphere<br> - <subject>/surf/lh.curv : the per-vertex mean curvature of the white surface for the left hemisphere<br> - <subject>/surf/rh.curv : the per-vertex mean curvature of the white surface for the right hemisphere<br> - <subject>/surf/lh.jacobian_white : the per-vertex jacobian for the left hemisphere<br> - <subject>/surf/rh.jacobian_white : the per-vertex jacobian for the right hemisphere</p> <p>All files are in binary FreeSurfer curv format.</p> <p><br> ## What is NOT contained</p> <p>* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.</p> <p>## Author and License</p> <p>Note: For the authors of the original ABIDE I dataset, see the Credits section above.</p> <p>This lgi data was created by:</p> <p> Dr. Tim Schäfer<br> Postdoc Computational Neuroimaging<br> Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy<br> University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany<br> http://rcmd.org/ts</p> <p>The data is published under the following license:</p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)</p> <p>See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.</p> <p>See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.</p> <p> </p>
Science Mesh Videos | Unlocking scientific collaboration through technology in Europe
<p>The Science Mesh enables researchers, educators, data curators and analysts to retain control over their remote or domestic datasets, while becoming FAIR compatible and integrated with the European Open Science Cloud (EOSC) at the same time. This will offer researchers opportunities to assemble an efficient, reliable, collaborative and transparent research tool chain. Our target stakeholders will be able to directly access the service provided by Science Mesh from easy-to-use interfaces and discover the different functionalities. The added-value for the stakeholders of CS3MESH4EOSC relies on the possibility for them to contribute in developing application plugins or easily set-up federations in their own scope and develop their own activities on top of them.</p>
Zebrafish with lyz:EGFP expressing neutrophils: Mesh Well Inserts Z-stack 3
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA). Mesh well inserts are used and half of the zebrafish on the plate were injected with csf3r morpholino. The overall z-stack is broken into four files.</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</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.