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882 results for “3D modelling”
3D Models of the yellow coffins in the Museo Egizio, Torino (Italy)
<p>3D Models of yellow coffin lids in the <a href="https://www.museoegizio.it/" target="_blank" rel="noopener"><strong>Museo Egizio, Torino</strong></a>.</p> <p>The 3D models consider only the external upper part of coffin lids as far down as the lower part of the crossed forearms. </p> <p>The dataset contains:</p> <ol> <li>zip files with the 3D models generated with the software Agisoft Metashape 1.8.3 (.jpg; .mtl; .obj);</li> <li>.tif files with the orthophtgraphs of the coffins textured and not textured</li> <li>Exported Report of 3D models (.pdf)</li> <li>.pdn file with the overlapped layers (orthophotographs textured and not textured, drawings and points) generated with the open source paint. net</li> </ol> <p>The dataset is part of the results of the <a href="https://facesrevealed.museoegizio.it/"><strong>Faces Revealed</strong> <strong>Project</strong></a>. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 895130</p> <p><strong>If you publish material based on datasets contained in this archive, then, in your acknowledgements, please cite the original source, referring to it through the following DOI: 10.5281/zenodo.10589491</strong></p>
Proposal of a domain model for 3D representation of buildings for the 3D cadastre in Ecuador
<p><span>The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.</span></p>
3d Transition Metal K-edge XANES Dataset for Machine Learning Models
<p><strong>Data</strong><br><br>This dataset contains machine learning data for K-edge X-ray Absorption Near-Edge Structure (XANES) prediction models for eight 3d transition metals (Ti -Cu).</p> <ul> <li><strong>features_and_spectra:</strong> Material features (X) and corresponding XAS spectra (y) for each dataset split: training (train), validation (val), and test.</li> <li><strong> material_id_and_site:</strong> Material identifiers and site indices (according to <a href="https://github.com/AI-multimodal/Lightshow">Lightshow</a>) for each dataset split. </li> </ul> <p><strong>Funding</strong><br><br>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, at Brookhaven National Laboratory under Contract No. DE-SC0012704 and by Brookhaven National Laboratory (BNL), Laboratory Directed Research and Development (LDRD) grant no. 24-004.</p> <p> </p>
3D models of rock piles near the Bear Trap in Northwest Greenland
<p>This dataset consists of files that can be used to view 3D models of two rock piles adjacent to ‘The Bear Trap’, a Norse ruin at the western end of the Nuussuaq Peninsula in NW Greenland. 3D models of the first rock pile (rockpile01) were created from 326 photographs and the second model (rockpile02) used 267 images. Images were processed using Agisoft Metashape Pro v1.7. A 24.3 megapixel Sony a5100 APS-C mirrorless camera fitted with a 24 mm lens was used to acquire ground-level imagery of the rock piles. The settings used in Metashape can be found in the processing reports (pdf files) for each rock pile.</p> <p>The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts. </p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. <em>Antiquity</em> 94:e6 doi:10.15184/aqy.2019.230</p> <p>Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Manuscript submitted to<em> Arctic Anthropology</em></p>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
3D FEM-based inverse model of Nevado del Ruiz - St. Isabel volcanoes (Colombia)
<p><strong>Description of model and data</strong></p> <p>The files include a FEM-based inverse model for the optimization of parameters of a pressure source responsible for surface deformation. The investigated source parameters are the position of the source center, the three semi-axis, the source strike orientation, the source dip orientation, and the source overpressure. The observations used for the inversion are ascending and descending ground velocities. The optimization is based on Least-Squares objectives using the Monte Carlo method. The file of observations needed for the computation of the Least-Squares objectives (to be uploaded in the optimization node) requires four columns (x,y,z, velocities. All in meters, UTM coordinates-UTM zone 18N, and comma-separated).</p> <p>The model takes into consideration the heterogeneous distribution of material elastic properties. The model does not provide the files for the observations and material properties (at the link: https://zenodo.org/record/5575972), but the structure for the optimization model in which new files can be uploaded for a customized model.</p> <p>The model includes the compensation for the stresses induced by the topography (edifices’ load). The file for the construction of the topographic surface is included as a .txt file (the position x,y of the points is in UTM coordinates-UTM zone 18N, the altitude z is in meters). The far-field is modeled as a hemisphere and it is located at 35 km from the center of the model, which is between the Nevado del Ruiz volcano and Santa Isabel volcano.</p> <p>The model is built with Comsol Multiphysics v 5.6 using the modules Optimization and Structural Mechanics modules, and it is provided as a Comsol .mph file.</p> <p> </p> <p>Datasets and model are results of PICVOLC project. PICVOLC has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 79381</p>
Data for: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.
<p>Raw data set for Npj Regenerative Medicine article: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.</p>
3D Adjoint tomography model of the Italian lithosphere
<p>The project IMAGINE_IT (PI Dr. Dimitri Komatitsch) received 40 million CPU-hours on the Tier-0 GENCI/TGCC CURIE supercomputer as a winner of the 9<sup>th</sup> PRACE consortium call (2014). </p> <p>The awarded computational resources allowed us to construct a new 3D tomographic model for the Italian lithosphere, <em>Im25</em>,<em> </em>by combining spectral-element three-dimensional wavefield simulations and an adjoint-state method.</p> <p>To obtain the final model <em>Im25, </em>we performed 25 adjoint tomography iterations and two source inversion iterations for the initial wavespeed model and an intermediate one.</p> <p>The 3D model <em>Im25</em> resolves P- and S-wavespeed values in the Italian lithosphere for frequencies up to ~0.1 Hz (i.e., periods down to ~10 s). It provides improved images of the subsurface of the peninsula and its neighborhood, highlighting the complex structure of the Adriatic plate on the east of the Italian coast, the debated plumbing system of Mount Etna volcano, and the distribution of fluids and gas (CO<sub>2</sub>) in correlation with the Italian seismicity. </p> <p>The obtained <em>V<sub>p</sub></em> and <em>V<sub>s</sub></em><sub> </sub>models are in agreement with high resolution models developed by classical tomography studies in Italy at local scale, and the retrieved <em>V<sub>p</sub></em>/<em>V<sub>s</sub></em> values correspond to the interpretations on fluids and gas distribution in the Italian subsurface from studies in various geophysical fields. The <em>V<sub>s</sub></em> <em>Im25</em> model also compares favorably with <em>V<sub>s</sub></em> profiles extracted from other studies that use different techniques and datasets, and focus on specific zones of Italy.</p> <p>The provided dataset contains the values of <em>V</em><sub><em>p</em></sub> and <em>V<sub>s</sub></em> (in m/s) for model <em>Im25</em> at each point of the geometrical grid used to discretize the study volume.</p>
Experimental Seismic Data Obtained Using a 3D-Printed Model of the Los Angeles Basin Structure
<p>These data were obtained and analyzed by Park et al., (2022) "Seismic wave simulation using a 3D printed model of the Los Angeles Basin" (doi:10.1038/s41598-022-08732-w).</p> <p> </p>
3D Nuclei annotations and StarDist 3D model(s) (rat brain)
<p><strong>Name</strong>: 3D Nuclei annotations and StarDist3D model(s) (rat brain)</p> <p><strong><em>Images: </em></strong>From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/)</p> <p>These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62).</p> <p><strong><em>Conda environment</em></strong><em>: </em>A conda environment was created using the yml file <em>stardist0.8_TF1.15.yml</em></p> <p><strong><em>Training : </em></strong>Training was performed using the jupyter notebook <em>1-Training_notebook.ipynb</em>.<br> Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/)</p> <p>Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/).</p> <p><strong><em>Validation: </em></strong>To save metrics in a csv file and compare predictions to the annotations the jupyter notebook <em>2-QC_notebook.ipynb </em>can be used on the validation folder.</p> <p><strong>Large images</strong>: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif )<br> These images were obtained using the plugin <a href="https://imagej.net/plugins/bigstitcher/">BigSticher </a>on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.</p> <p> </p> <p> </p>
Supporting Information for "New 3D velocity model (mTAB3D) for absolute hypocenter location in southern Iberia and the westernmost Mediterranean"
<p>These files comprise supplementary information for the paper entitled "New 3D velocity model (mTAB3D) for absolute hypocenter location in southern Iberia and the westernmost Mediterranean" (Sánchez-Roldán et al., 2024a)</p> <p>These results were obtained after performing a relocation using the 3D P-wave velocity model mTAB3D (Sánchez-Roldán et al. 2024b).</p> <p>In "Files.zip", we provide the eight files with the absolute locations and the uncertainty parameters (extracted from the 68% confidence ellipse of the PDF’s) obtained after performing the relocation using mIGN1D and mTAB3D. The absolute location files follow this format:</p> <p>origin_time(YYYY-mm-ddTHH:MM:SS) longitude(º) latitude(º) depth(km) magnitude(mbLg)</p> <p>• origin_time: Hypocenter’s origin time after the relocation.</p> <p>• longitude: Hypocenter’s longitude in decimal degrees after the relocation.</p> <p>• latitude: Hypocenter’s latitude in decimal degrees after the relocation.</p> <p>• depth: Hypocenter’s depth in kilometers.</p> <p>• magnitude: Hypocenter’s magnitude (mbLg) computed by the Spanish Seismic Network.</p> <p>The files with the uncertainty values:</p> <p>horizontal_uncertainty(km) vertical_uncertainty(km) rms(s) no_arrivals</p> <p>• horizontal_uncertainty: Obtained after computing the geometrical mean between the horizontal semi-minor and semi-major axes of the 68% confidence ellipse in kilometers.</p> <p>• vertical_uncertainty: Vertical semi-axis of the 68% confidence ellipse.</p> <p>• rms: root-mean-square of residuals at maximum likelihood or expectation hypocenter.</p> <p>• no_arrivals: number of readings used for the absolute location.</p> <p><br>File S1. File_S1.dat: Eastern Betics Shear Zone catalog’s absolute locations with mIGN1D.</p> <p>File S2. File_S2.dat: Eastern Betics Shear Zone catalog’s statistics with mIGN1D.</p> <p>File S3. File_S3.dat: Eastern Betics Shear Zone catalog’s absolute locations with mTAB3D.</p> <p>File S4. File_S4.dat: Eastern Betics Shear Zone catalog’s statistics with mTAB3D.</p> <p>File S5. File_S5.dat: Al Hoceima 2016 catalog’s absolute locations with mIGN1D.</p> <p>File S6. File_S6.dat: Al Hoceima 2016 catalog’s statistics with mIGN1D.</p> <p>File S7. File_S7.dat: Al Hoceima 2016 catalog’s absolute locations with mTAB3D.</p> <p>File S8. File_S8.dat: Al Hoceima 2016 catalog’s statistics with mTAB3D.</p> <p>Additionally, we provide two figures showing the location of those hypocenters (alboran.jpg and ebsz.jpg), which are included as Figures 3 and 5, respectively, in Sánchez-Roldán et al. (2024a).</p> <p>References:</p> <p><span>Sánchez-Roldán, J. L.</span>, <span>Álvarez-Gómez, J. A.</span>, <span>Martínez-Díaz, J. J.</span>, <span>Herrero-Barbero, P.</span>, <span>Perea, H.</span>, <span>Cantavella, J. V.</span>, & <span>Lozano, L.</span> (<span>2024a</span>). <span>New 3D velocity model (mTAB3D) for absolute hypocenter location in southern Iberia and the westernmost mediterranean</span>. <em>Earth and Space Science</em>, <span>11</span>, e2023EA00299. <a href="https://doi.org/10.1029/2023EA002993">https://doi.org/10.1029/2023EA002993</a></p> <p>Sánchez-Roldán, J. L., Álvarez-Gómez, J. A., Martínez-Díaz, J. J., Herrero-Barbero, P., Perea, H., Lozano, L., & Cantavella, J. V. (2024b). MTAB3D: a 3-D velocity model for absolute hypocenter location in southern Iberia and westernmost Mediterranean. (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7766525" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7766525</a></p> <div> </div> <p> </p> <p> </p>
3D models (NXS): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p><span>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</span></p>
3D models (true color, TIF): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
3D Models and Silhouettes for Human Body Reshape with DL from the ANSUR Dataset
<p><strong>Citations</strong><br><br>If you use this dataset in your research, please cite the original paper: </p> <p>Curbelo, J.P., Spiteri, R.J. A methodology for realistic human shape reconstruction from 2D images. Multimedia Tools and Applications (2024), <a href="https://doi.org/10.1007/s11042-023-17947-6">DOI: 10.1007/s11042-023-17947-6</a></p>
Digital models of test objects captured by RFSAT Ltd using 3D photogrammetry
<p>This data set contains a number of digital models produced via 3D photogrammetric scanning as part of the SCAN4RECO project, funded by the European Horizon'2020 program. Scanning and processing of models was done with Pix4D Mapper and Autodesk ReMake software from images captured with Canon 5DS camera in 50 Megapixel image resolution. Example objects include Byzantine icons painted on wood, oil paintings on canvas and painted Venetian carnival paper masks.</p> <p>Second version of the data set includes historical icons of Saint DImitrios and Saint Archangel Michael, an icon of Saint Mary painted specially for testing SCAN4RECO technologies, as well as models of an original high-relief sculpture from OPD and of its 3D printed copy (made by Fraunhofer-IGD and hand painted by RFSAT)..</p> <p>Selected models can be also seen in the SCAN4RECO Virtual Museum developed by CERTH-ITI:<br> http://scan4reco.eu/scan4reco/content/scan4reco-virtual-museum</p>
Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'
<p>The dataset supporting our publication '<strong>Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model</strong>' in <em>Geophysical Research Letters.</em></p> <p>The main .zip archive contains a set of NetCDF files detailing:</p> <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> <p>Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback.</p> <p>The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>.</p> <p>A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above).</p> <p>Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.</p>
Left Atrium 3D Models Extracted from Static CT Images (AF and SR Models)
<p>A cohort of 45 x 2 patient-specific 3D-models from static computed tomography (CT) images provided by the University Medical Center Hamburg-Eppendorf in Germany. The study complies with EU Regulation 2016/679 and has recieved ethical approval from the regional committee. </p> <p>The cohort has been used in two research papers:</p> <ol> <li>Kjeldsberg et al. (2024), Estimation of inlet flow rate in simulations of left atrial flows: A proposed optimized and reference-based algorithm with application to sinus rhythm and atrial fibrillation, <em>J Biomech</em> [Accepted]</li> <li>Kjeldsberg et al. (2024), Beyond CHA2DS2–VASc: hemodynamic and morphologic discriminants for thrombus formation and stroke in atrial fibrillation patients,<em> Ann. Biomed. Eng</em>. [Submitted]</li> </ol> <p><strong>models_af.zip </strong>contains 45 3D surface models (.vtp) extracted during onset of atrial diastole where <strong>A</strong>trial <strong>F</strong>ibrillation movement was applied using a motion algorithm [*]</p> <p><strong>models_sr.zip </strong>contains 45 3D surface models (.vtp) extracted during onset of atrial diastole where <strong>S</strong>inus <strong>R</strong>hytmn movement was applied using a motion algorithm [*]</p> <p> </p> <p>[*] For details on the motion algorithm, see <a href="Harrison, J., 2024. Medical Imaging, Shapes and Statistics for Stroke Prediction in Atrial Fibrillation (Doctoral dissertation, Inria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France).">Harrison – Medical Imaging, Shapes and Statistics for Stroke Prediction in Atrial Fibrillation </a></p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
Semantic 3D Tree Model Dresden 2017
<p>The semantic 3D tree model contains reconstructed tree crowns within the City of Dresden (Germany). Area-wide availability of such models and their integration into semantic 3D city models facilitates enriched visualizations of urban areas as well as 3D spatial modeling that simulates the interaction of trees with buildings and the built environment.</p> <p>The individual tree crowns were modeled using geometric primitives and correspond to the CityGML Level of Detail (LoD) 2. Individual modeling parameters were determined for each tree aiming for a realistic volume replication. LiDAR data from a survey in the year 2017 were used to parameterize the tree crowns. Tree crowns were modeled via ellipsoids fitted to crown extent, cylinders were used for trunk representation. The framework for segmenting individual trees in the LiDAR point cloud and for modeling individual tree crowns via geometric primitives is described in <a href="https://doi.org/10.1016/j.ufug.2022.127637">this article</a>.</p> <p>The tree models are available as CityGML files in the coordinate system ETRS89/UTM zone 33 (EPSG: 25833). The dataset was divided into tiles. The tile number results from the coordinate of the lower left corner in the coordinate reference system.</p> <p>The source data used was made freely available by the “Landesamt für Geobasisinformation Sachsen” (GeoSN) under the license "Data license Germany - attribution - Version 2.0" and can be downloaded under the following links:<br> LiDAR: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html</a><br> 3D Building Model: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html</a><br> Aerial Imagery: <a href="https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html">https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html</a></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.