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288 results for “Digital Modeling”
Digital twin of a standard electrochemical cell for cyclic voltammetry based on Nernst-Planck-Poisson model
<p>The project contains a COMSOL file used for the simulations in the publication "<em>Digital twin of a standard electrochemical cell for cyclic voltammetry based on Nernst-Planck-Poisson model</em>".</p>
Mosaicked Digital Elevation Models over Nepal
<p>The DEMs in this dataset are generated by mosaicking four individual DEM tiles over the 2015 Nepal earthquake epicentral region. The mosaicked DEMs are available in the WGS84 vertical datum. The DEMs represent surface elevation before the occurrence of the 2015 earthquake event. The DEMs are used for topographic phase removal in the Differential InSAR studies.</p>
Comparison among three different Digital Surface Models and their respective hydraulic outcomes in the flood-prone urban area of Navaluenga (Ávila, Spain)
<p>Three different Digital Surface Models (DSMs) generated from LiDAR data are presented. The LiDAR information has been considered as raw data (DSM3) and subjected to some transformations to better represent the urban environment (DSM1). DSM2 is an intermediate state between DSM1 and DSM3. </p> <p>On the other hand, a hydraulic model has been run for each DSM and for two return periods (25 and 500 years), obtaining in all cases the graphical outputs of depths, velocities, Froude numbers and hazard. </p> <p>The different DSMs are named DSM1, DSM2 and DSM3, which can be downloaded in TIN format. The hydraulic outputs associated with the different DSMs can be downloaded in raster format and are named as follows: the Digital Surface Model to which it refers, the return period considered and the type of hydraulic output (depth, velocity, Froude number and hazard).</p> <p>DSM1: Digital Surface Model 1 (TIN format).<br> dsm1_25depth: Depths obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25haz: Hazard obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25veloc: Velocities obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format). <br> dsm1_500depth: Depths obtained when considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500haz: Hazard obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500veloc: Velocities obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).</p> <p>DSM2: Digital Surface Model 2 (TIN format).<br> dsm2_25depth: Depths obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25haz: Hazard obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25veloc: Velocities obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format). <br> dsm2_500depth: Depths obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500haz: Hazard obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500veloc: Velocities obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).</p> <p>DSM3: Digital Surface Model 2 (TIN format).<br> dsm3_25depth: Depths obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25haz: Hazard obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25veloc: Velocities obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format). <br> dsm3_500depth: Depths obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500haz: Hazard obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500veloc: Velocities obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).</p>
A Scalable Data Management System Data Model facilitating the integration of any inspection system and the automated data digitalization process.
<p>In the context of the EU-funded project PILOTING (No. 871542), a scalable Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. In particular, a three-phase methodology was adopted for the creation of the DMS Data Model (DMS-DM) in order to define the architectural design of the DMS. <br> The first phase was to semantically define the entities of the PILOTING ecosystem, identifying assets and activities that were important to the data providers, and including them in the data model design. The second phase was to identify relationships between the entities, focusing on information that must pre-exist for entities to be semantically accurate, hierarchies between entities, especially physical assets, and the data needed to form the inspection plan. For the third phase, the information collected by the previous two stages of the data model construction was used to create the overall data model, taking into consideration the possible integration with third parties and the needs of the I&M Visualization portal. <br> The constructed document is attempted to present the designed Entity-Relationship-Diagram (ERD) of the DMS-DM. Additional definitions of the existing entities and the relations between them are also depicted. </p>
Seismic Inverted Impact force and Digital Elevation Models before and after the 2018 Baige Landslide
<p>For the data of inverted seismic force, the seismic signals recorded by the broadband seismic stations were prepared for the inversion of the force-time function by the following series of actions:</p> <ul> <li>Removing the instrumental response</li> <li>Resampling to 0.5 s</li> <li>Integrating the signals from velocity to displacement</li> <li>Rotating the horizontal components to the radial and transverse direction</li> <li>Filtering the signals between periods of 30 and 140 s</li> </ul> <p>For the digital elevation models (DEMs) of the Baige landslide, the pre-failure DEM with 10 m grid spacing was obtained from the Sichuan Bureau of Surveying Mapping and Geoinformation (SBSMG). At the same time, the post-failure DEM was derived from the UAV-based photogrammetry.</p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the results of photogrammetric processing (Digital Elevation Models, Orthomosaics and subset data used for volumetric calculation and visualization) named: “DEM_1967.7z”: inside the zipped folder “1967_DEM.tif” (digital elevation model produced for the year 1967), “DEM_1984.7z”: inside the zipped folder “1984_DEM.tif” exist (digital elevation model produced for the year 1984). In addition, under “1967_Orthomosaic.7z" and "1984_Orthomosaic.7z” zipped folders, there are orthomosaic files produced namely, “1967_orthomosaic.tif” and "1984_orthomosaic.tif” for the year 1967 and 1984, respectively. The DEMs and Orthomosaics subset from the results for sites (data example 1 and data example 2) reside under "Data_Examples.zip". Accuracy of the resulted data were assessed and the extracted elevation values are under "Accuracy_assessment.zip". All DEMs and Orthomosaics are in GeoTIFF format in the Adindan UTM Zone 37 N (EPSG: 20137) projected coordinate system.</p> <p> Potential application of the presented dataset include:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p>The inputs for the above resulted DEMs and Orthomosaics are found under Zenodo repository "10.5281/zenodo.7271617". </p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the inputs used for Structure from Motion Multiview Stereo photogrammetry processing for the year 1967 and 1984 i.e Unprocessed scanned historical aerial Photographs, camera position coordinates, flight index and Ground Control Points. All the scanned historical aerial photographs data are in TIFF format except four photographs in JPEG format under a zipped folder ("1967_Scanned_HAPs_Part1.7z and 1967_Scanned_HAPs_Part2.7z" for the 1967 Historical Aerial Photographs and "1984_Scanned_HAPs_Part1.7z and 1984_Scanned_HAPs_Part2.7z" for the 1984 Historical Aerial Photographs). The "Flight_Index.Zip" contains shapefiles of the camera position and polygon of consecutive aerial photograph index; "GCP.Zip" contains text file of the GCPs used for the 1967 and 1984; and "Camera_Position.Zip" contains the file of the camera position (Label, Easting, Northing and Altitude) of each historical aerial photographs. </p> <p>The results of the above dataset could be accessible on Zenodo repository "10.5281/zenodo.7269999".</p> <p>Anyone can reuse the presented dataset to produce DEMs and Orthomosaics; and use for the following application areas:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p> </p>
Interview guide from the paper "Act Against the Aggression A Preliminary Model of a Digital Platform Aimed at Cyberaggressions in the Portuguese University Environment"
<p>Interview guide used in the study "Act Against the Aggression A Preliminary Model of a Digital Platform Aimed at Cyberaggressions in the Portuguese University Environment".</p>
Acoustical-electrical models of tight rocks based on digital rock physics and double-porosity theory
<p>These data are the compressional-wave waveform and electrical and CT data obtained by Ba et al. through ultrasonic experimental and conductivity and CT scan measurements on tight-oil rocks.</p> <p>Details are given in the uploaded introduction document regarding the format of dataset.</p>
Machine-actionable data descriptions for Austria's digital landscape model
<p>The digital landscape model (DLM) is a compilation of several data sets that, among other things, form the data basis for official maps. It covers a variety of the 34 themes of the EU directive INSPIRE. The introduction of these themes enriched the metadata and made similar datasets easier to find. Unfortunately, the competent authority (BEV) only provides the associated data descriptions as a PDF export of an Excel spreadsheet without translations. This dataset contains the raw data and visualizations.</p>
Data obtained during classification of intertidal habitats using UAV imagery in the Galapagos Archipelago (Orthophotos, digital elevation models (DEM) and orthophoto-draped 3D models)
<p>In the repository 5 folders exist. 1) Digital elevation models (DEMs), 2) Intertidal habitat map, 3) Othophoto draped 3D models, 4) Orthophotos, and 5) Processing reports. The data has been collected in Puerto Ayora at Santa Cruz in August 2017, the most urbanized island of the Galapagos Archipelago. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. This dataset is cited in an open-access publication: https://doi.org/10.3390/drones7070416. </p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p> <p>Introduction</p> <p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p> <p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme. 2023-08-13a covers the lower part, 2023-08-13b the upper/source area.</p> <p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p> <p>Shovi_2023-08-13a_DEM_2m.tif</p> <p>Shovi_2023-08-13a_DEM_20m.tif</p> <p>Shovi_2023-08-13b_DEM_2m.tif</p> <p>Shovi_2023-08-13b_DEM_20m.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the block matching algorithm and the set of processing parameters from Marti et al. (2016)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Marti, R. et al.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10(4), 1361–1380, doi:10.5194/tc-10-1361-2016, 2016.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 38N (EPSG:32638)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p> <p>===========================</p> <p>Citation:</p> <p>Please cite this repository as :</p> <p>Berthier E. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023, Dataset distributed on Zenodo: 10.5281/zenodo.8252339</p>
Digital Elevation Model of Hintereis- and Kesselwandferner 08.10.2010
<p>DTM_RegionHintereis_2010_r1.tif: DEM of Hintereis- and Kesselwandferner, acquisition date 08.10.2010 by airborne laser scan (ALS), cellsize 1x1 m, UTM WGS84, ellipsoid heights<br> DTM_RegionHintereis_2010_r1_gc.tif: Same as above but corrected for geoid heights.</p> <p>These DEMs are a 1x1 m version of the 10x10 m DEM from 08.10.2010 available on https://doi.org/10.1594/PANGAEA.875889</p> <p>Reference: Bollmann et al. 2015: https://onlinelibrary.wiley.com/doi/10.1002/ppp.1852</p> <p>Data Source / Acknowledgement: The ALS data were provided by the Institute of Geography, University of Innsbruck, Austria, financed by the project ‘Multiscale Snow/Icemelt Discharge Simulation into Alpine Reservoirs’ (MUSICALS).</p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p><p>Introduction</p><p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p><p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme.</p><p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p><p>Shovi_2023-08-13a_DEM_2m.tif</p><p>Shovi_2023-08-13a_DEM_20m.tif</p><p>Shovi_2023-08-13b_DEM_2m.tif</p><p>Shovi_2023-08-13b_DEM_20m.tif</p><p>===========================</p><p>Methods</p><p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the semi global matching algorithm and the set of processing parameters from Deschamps-Berger et al. (2020)</p><p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA's Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p><p>Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.<br><br>===========================</p><p>Data Specifications:</p><p>Cartographic projection: UTM zone 38N (EPSG:32638)</p><p>Origin of Elevation: meters above WGS84 ellipsoid</p><p>Raster data format: GeoTIFF</p><p>NoData value : -9999</p><p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p><p>===========================</p><p>Acknowledgements: </p><p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p><p>===========================</p><p>Dataset Attribution:</p><p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p><p>===========================</p><p>Citation:</p><p>Please cite this repository as described below:</p><p>Etienne Berthier. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023</p>
Old model (Digital terrain model)
[Scientific Project](http://www.icac.cat/recerca/projectes-de-recerca/projecte/20942/) [Archaeological report 2016](https://www.researchgate.net/publication/311705780_Archaeological_report_-2016-_of_excavation_in_the_visigothic_settlement_of_Valencia_la_Vella_Riba-roja_del_Turia_Valencian_Country_Spain?_iepl%5BviewId%5D=XzB9abVr2fOR1kpvvnyc8cX4&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BtargetEntityId%5D=PB%3A311705780&_iepl%5BinteractionType%5D=publicationTitle) Source: Objaverse 1.0 / Sketchfab
Improving Medication Therapy Through a Digital Interdisciplinary Medicine Therapy Optimisation Model
ClinicalTrials.gov study NCT05629936. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Standing giants: A digital biomechanical model for bipedal postures in sauropod dinosaurs
Open the record for dataset details and reuse information.
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
Open the record for dataset details and reuse information.
Data from: Human face-off: a new method for mapping evolutionary rates on three-dimensional digital models
Open the record for dataset details and reuse information.
Digital research data from: Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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