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908 results for “Meshes”
Subset Data 5: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 10801 to 13500)
<p><em><strong>Subset Data 5: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 10801 to 13500)</strong></em></p> <ul> <li>2700 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB.</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
Subset Data 3: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 5401 to 8100)
<p><em><strong>Subset Data 3: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 5401 to 8100)</strong></em></p> <ul> <li>2700 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB.</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
Subset Data 7: 607 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 16201 to 16807)
<p><em><strong>Subset Data 7: 607 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 16201 to 16807)</strong></em></p> <ul> <li>607 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB.</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
Meshing of Spiny Neuronal Morphologies using Union Operators
<p>Resulting datasets of the conference paper "<em>Meshing of Spiny Neuronal Morphologies using Union Operators</em>". The paper is published in 2022 in EG UK Computer Graphics & Visual Computing (2022).</p> <p><a href="https://doi.org/10.2312/cgvc.20221168">DOI: 10.2312/cgvc.20221168</a></p> <p>The dataset consists of the resulting meshes of a set of exemplar neurons created using the union-operator-based meshing algorithm that is described in the paper. </p>
Repository of Thoracolumbar Osteo-Ligamentous Spine Patient-Personalised FE Meshes
<p><em><strong>Repository of Thoracolumbar Osteo-Ligamentous Spine Patient-Personalised FE Meshes:</strong></em></p> <ul> <li>42 FE input files (.inp extension, Abaqus software, Simulia) representing patient-personalised thoracolumbar spine hexahedral meshes, including point coordinates, mesh connectivity IDs and element sets. Each input file is almost 99MB (totally 3.86GB), and it includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments.</li> <li>An excel file: "<a href="../records/10994164/files/Descriptive_List%20(42_P-S_FE_Models).xlsx?download=1">Descriptive_List (42_P-S_FE_Models).xlsx</a>" reporting measured spinopelvic parameters for 42 patient-personalised FE hexahedral meshes. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort and patient-personalised FE meshes. Model ID in the excel file is correspondent to the model’s name.</li> <li>One png file: "<a href="../records/10994164/files/42_P-S.png?download=1">42_P-S.png</a>" representing the first 10 patient-personalised thoracolumbar spine hexahedral models out of 42 FE models.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../records/10994164/files/Descriptive_List%20(42_P-S_FE_Models).xlsx?download=1">Descriptive_List (42_P-S_FE_Models).xlsx</a>" is correspondent to the same model number in the 42 stereolithography (stl) files (.stl extension) representing the thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/42%20Patient-Specific%20stl%20files.rar?download=1">42 Patient-Specific stl files.rar</a>).</p> <p>2-Models are reconstructed thanks to the Statistical Shape Modelling (SSM) and mesh morphing techniques, i.e., spine sagittal geometrical parameters could be measured by a clinical software like sterEOS or Surgimap, then, different shape modes of morphed-mesh SSM tool could be activated in order to obtain spine deformity. Then, pros and cons analyses between patient geometrical data and synthesized geometrical information could exploit FE patient-personalised thoracolumbar osteo-ligamentous spine models.</p> <p>3- FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
Gold standard for tractograms and meshes io testing
<p>Gold standard for tractograms (<em>trk, tck, trx, dpy, vtk, fib</em>) and meshes (<em>gii, pial, vtk, ply, stl, obj</em>) IO testing for the StatefulTractogram (SFT) and StatefulSurface (SFS) in Dipy.</p>
Snappable Meshes Performance Dataset
<p><strong>Overview</strong></p> <p>This dataset contains performance and navigation benchmarks obtained by generating eight maps with the snappable meshes algorithm multiple times. Data in columns is organized as follows:</p> <ul> <li><code>run</code> - Number of generation run.</li> <li><code>genset</code> - Generated map (maps "(a)" to "(h)" from the Benchmark scene.</li> <li><code>navset</code>- Number of navigation points used to generate the navigation metrics.</li> <li><code>tg</code> - Duration of the map generation process, in milliseconds.</li> <li><code>tv</code> - Duration of the map validation process, in milliseconds.</li> <li><code>c</code> - Average percentage of valid connections between navigation points.</li> <li><code>ar</code> - Relative area of the largest fully-connected (i.e., fully-navigable) region.</li> <li><code>nclu</code> - Number of isolated regions, i.e., of regions which are not connected to any another.</li> <li><code>genseed</code> - Seed used for the generation process.</li> <li><code>navseed</code> - Seed used for determining the validation metrics.</li> </ul> <p>A number of results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes" are obtained from this dataset.</p> <p><strong>Reproducibility of results</strong></p> <p>The results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes", namely in Figure 8 and Figure 10, can be reproduced with the Jupyter notebook included with this dataset (file <code>analysis.ipynb</code>).</p> <p><strong>Licenses</strong></p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see <code>LICENSE_DATA.txt</code>).</p> <p>The code in the Jupyter Notebook is made available under the <a href="https://opensource.org/licenses/MIT">MIT</a> license (see <code>LICENSE_CODE.txt</code>).</p>
MeshDapp: Blockchain-enabled Payment System for Wireless Mesh Networks - Dataset
<p>The MeshDapp dataset includes data from the MeshDapp project - Fed4Fire+ Open Call 9 winner:</p> <p>Data includes: Network Measurement from the CityLab - Antwerp testbed and IRIS (TCD) testbed. </p> <p>MeshDapp Paper:https://mvdsi.seeu.edu.mk/mselimi/papers/DAPPS20ms.pdf </p>
NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes
<p>This repository holds all of the raw data generated by my C++ code for a paper "NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes".</p> <p>The C++ code solves the neutron diffusion equation using a novel spatial discretisation called the Virtual Element Method.</p> <p>Alongside the raw data (stored in VTK and HDF5 files) are post-processing python scripts which read the raw data, compute meaningful quantities of interest and generate plots/figures.</p>
gyroScatterEFF data: 590k element cyclone mesh
<p>Arrays and constants needed to run the gyroScatterEFF kernel on a single GPU without XGCm.<br> <br> Created with https://github.com/SCOREC/xgcm/tree/cws/extractGyroScatter @ <a href="https://github.com/SCOREC/xgcm/commit/5a8f0b47262c2466085e8fca983add502eef64b8">5a8f0b4</a>. <br> <br> The build on OLCF Summit used the following scripts: https://github.com/zhangchonglin/XGCm_build_scripts/tree/792c9378b6a12ca141811cbd788c6d6542e59938/Summit_gcc11.2.0_cuda11.5.2<br> <br> And the Cyclone 590k mesh case here: https://github.com/SCOREC/xgc1_data/tree/0d32f7f1354346a37df1fac517fa1f4fceff2015/Cyclone_ITG/Cyclone_ITG_deltaf_590kmesh <br> <br> At the time of dataset publication, the XGCm and xgc1_data repos were private.</p>
GEOLAB-PEBSTER-Deltares: Small-scale experiments on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<p><strong>DOI: 10.5281/zenodo.12627309</strong></p> <h1><strong>Small-scale experiments on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB-PEBSTER-Deltares project on Zenodo! This repository contains the measurement data from four small-scale experiments on Piled Embankments with Basal Steel Reinforcement (PEBSTER). Pile-supported (PS) embankments with basal geosynthetic reinforcement (GR) are commonly built in soft soil areas (van Eekelen and Han, 2020). Steel mesh reinforcement (SR) is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from four small-scale tests on steel-reinforced piled embankments, conducted in the Deltares laboratory (van Eekelen et al., 2024a,b). These tests are part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes a large-scale test (Schneider et al., 2024a,b). The small-scale tests were conducted at Deltares, Delft, Netherlands, while the large-scale test was conducted in Darmstadt, Germany, at the Institute of Geotechnics, Technical University of Darmstadt.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>Dataset of four small-scale experiments</h2> <p>Here, we share the measurement data from the small-scale experiments that were part of one of the GEOLAB TA projects: PEBSTER. Four piles (diameter 0.1 m, centre to centre 0.55m) passed through a steel plate, that supported a foam cushion, that was sealed and soaked. A tap allowed for drainage of the foam cushion, simulating the consolidation of the subsoil between the piles. The 0.55 m high embankment consisted of medium coarse sand, and was reinforced at its base with a steel mesh reinforcement. A surcharge load up to 100 kPa was applied with a water cushion.<br><br><span>The load distribution is measured by pressure cells and load transducers. </span>Soil strains and displacements were monitored at five elevations within the fill, utilizing distributed fibre optic sensing (DFOS) technology from the Nerve-Sensors family, as depicted in Figure 2. Additionally, the steel mesh reinforcement was extensively instrumented with optic fibres.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes Deltares, Netherlands, the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Keller (Germany, France, Poland), SHM System, Poland, and FOLAB, Germany.</p> <p> </p>
Global 32-4 km variable-resolution mesh for the MPAS-Atmosphere model
<div> <div> <div>This mesh was created by a collaboration between the Department of Energy’s Water Cycle and Climate Extremes Modeling (WACCEM) project and the</div> <div>Mesoscale and Microscale Meteorology (MMM) Laboratory at the National Science Foundation National Center for Atmospheric Research (NSF/NCAR).</div> <div> </div> <div>The mesh contains 1,830,914 horizontal grid cells. The circular refinement region has a radius of approximately 20 degrees. The 4-32km grid-spacing range aims to achieve convection-permitting resolution in the high-resolution domain and resolution sufficient for the jet stream and mid-latitude wave activities (Lu et al., 2015) in the low-resolution domain.</div> <div> </div> <div>The netcdf file "x8.1830914.grid.nc" includes the variables defining the global unstructured grid for the MPAS model as described in the <a href="https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf">MPAS Mesh Specification</a>. Following the other MPAS mesh data, we provide graph.info.part.* files necessary for the Message Passing Interface (MPI) parallelism. For example, a simulation using 1024 MPI ranks will use graph.info.part.1024. For the number of MPI tasks not provided in this dataset, a user needs to create a new partitioning file using the METIS tool and the graph.info file as described in the MPAS user guide (available <a href="https://mpas-dev.github.io/atmosphere/atmosphere_download.html" target="_blank" rel="noopener">here</a>).</div> <div> </div> </div> </div> <div>This data will also be available from the <a href="https://mpas-dev.github.io/atmosphere/atmosphere_meshes.html">MPAS mesh website</a>.</div> <div> </div> <div>The mesh generation is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research (BER) as part of the Regional and Global Model Analysis Program Area. We acknowledge the use of computational resources of the National Energy Research Scientific Computing Center (NERSC). The Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.</div>
Mobility demand mesh-grid datasets
<ul> <li><strong>15m_flat_bike_count.h5</strong>: Table that contains the number of bike rides that started on every bike station in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li><strong>15m_flat_bike_norm_abs.h5</strong>: Same as <strong>15m_flat_bike_count.h5</strong>, but normalized using the maximum number of rides recorded in that period, i.e.: 84. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li><strong>15m_flat_taxi_count.h5</strong>: Table that contains the number of taxi trip counts that started on every taxi zone in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li><strong>15m_flat_taxi_norm_abs.h5</strong>: Same as <strong>15m_flat_taxi_count.h5</strong>, but normalized using the maximum number of trips recorded in that period, i.e.: 418. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li><strong>15m_map_90_60_bike_norm_abs.h5</strong>: Mobility mesh-grid for bikes, calculated from<strong> 15m_flat_bike_norm_abs.h5</strong> counting the total number of rides per element of the grid and time interval. The final shape of the dataset is 280512 x 90 x 60.</li> <li><strong>15m_map_90_60_taxi_norm_abs.h5</strong>: Mobility mesh-grid for taxis, calculated from <strong>15m_flat_taxi_norm_abs.h5</strong> using linear interpolation. The final shape of the dataset is 280512 x 90 x 60.</li> <li><strong>station-locations-bike.csv</strong>: Geolocations of the bicycle racks of Chicago.</li> <li><strong>zone-centroids-taxi.csv</strong>: Geolocations of the centroids of the taxi zones of Chicago.</li> <li><strong>grid-locations-bike.csv</strong>: Locations of the elements of a 90x60 grid overlaying Chicago that contain bicycle racks.</li> <li><strong>holidays.csv</strong>: Holidays in Chicago for the period 2013-2020.</li> </ul>
MIDAS MeSH to Ontology mappings
<p>These mapping files contain mappings from the RDF version of MeSH to the Geographical Entity Ontology and the NCBI Taxonomy for use in the Ontology Based Catalogue for Infection Disease Epidemiology.</p>
3D meshes of wood samples (KUR project)
<p>The repository contains 3D meshes of 83 wood samples from the LEIZA reference collection documented as part of the project „Mass Finds in Archaeological Collections“ funded by the Federal Cultural Foundation, from 15.04.2008 to 31.12.2011 within the framework of the "Programme for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur). Each wood sample has two 3D meshes, one before conservation (KUR_3D_Vorzustand.zip) and one after conservation (KUR_3D_Endzustand.zip). A 3D fringe light projector (GOM ATOS III Rev.01) with a resolution of 0.25 mm was used to digitize the samples. Additional information can be found in the stored metafiles (*.json, *.ttl). The acquisition of 3D data was done during July 2009 - August 2014.</p>
CS3MESH4EOSC Final Event Science Mesh - Unlocking Open Science and Collaborative Research Landscape
<p>The recap video of CS3MESH4EOSC final event. The CS3MESH4EOSC final event, took place on 22 June 2023, at the EGI Conference in Poznan (Poland), showcased how the Science Mesh is contributing to an easier and more robust open science across Europe, thanks to novel approaches for data sharing and synchronisation.</p> <p><strong>The first half of the event</strong> will count with live demonstrations, where each data service from the Science Mesh will be presented from a user-perspective point of view. Event attendees will get practical information on how they can join the Science Mesh as a researcher, a software developer or a service provider. The event will also bring together representatives of Science Mesh use cases, who will explain how Science Mesh is making a difference in their lives, thanks to easier data sharing and synchronisation. A panel discussion with representatives of different sectors, from research to industry and education, will discuss the most urgent trends & priorities for cross-border science collaboration between different sciences.</p> <p><strong>The second half of the event</strong> will be focused on the technical novelties within the Science technical foundation. A series of demonstrations will be presented, followed by a panel discussion, with representatives of CS3MESH4EOSC members that are part of the EOSC Task Forces, on how the Science Mesh contributes to EOSC’s success and the overall EOSC Strategic Research and Innovation Agenda (SRIA).</p>
Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Rulff, P., Schankee Um, E., Amor-Martin, A. (2023) Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure. Accepted for publication in Computational Geosciences.</p> </blockquote>
Cipher Machine Hagelin CX-52_surface meshes (Inv. 1987-180)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Cipher Machine Enigma I_surface meshes (Inv. 1987-104)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Cipher Machine Kryptograph by Alexis Køhl_surface meshes (Inv. 46453)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</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.