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288 results for “Digital Modeling”
Data from: Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies
<p><strong>Background:</strong> Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting.</p> <p><strong>Methods:</strong> We sought to bridge this gap by linking preclinical data in the porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation.</p> <p><strong>Results:</strong> The model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time.</p> <p><strong>Conclusions:</strong> This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Wikidata's linked data for cultural heritage digital resources: an evaluation based on the Europeana Data Model
<p>Wikidata is an open data source with many potential applications. Our study aims to evaluate the usability of Wikidata as a linked data source for acquiring richer descriptions of digital objects within the context of Europeana, a data aggregator from the cultural heritage domain. Specifically, we aim to crawl and convert Wikidata using the standard approaches and operations developed for the (Semantic) Web of Data, i.e. using technologies like linked data consumption and RDF(S)/OWL ontology expression and reasoning. We also seek to re-use existing “semantic” specifications, such as conversions to and from generic data models like Schema.org and SKOS. We have developed an experimental set-up and accompanying software to test the feasibility of this approach. We conclude that Wikidata’s linked data is able to express an interesting level of semantics for cultural heritage, but quality can still be improved and a human operator still must assist linked data applications to interpret Wikidata’s RDF.</p>
Digital Elevation Models from Planetary Flyby Images of Mercury and the Moon with Shape and Albedo from Shading
<p>Supplemantary material to Krüll, I., Wohlfarth, K., Tenthoff, M., Wöhler, C., Galluzzi, V., Wright, J., Benkhoff, J., and Zender, J.: Shape and Albedo from Shading with Planetary Flyby Images of Mercury and the Moon, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-247, https://doi.org/10.5194/epsc2024-247, 2024.</p> <p><strong>Abstract</strong></p> <p>Surface reconstruction of planetary bodies such as the Moon and Mercury is crucial for geomorphological analysis, reflectance normalization, thermal modeling, rover landing site planning, and outreach activities. Stereo algorithms and Shape-and-Albedo-from-Shading (SAfS) are well-established methods for planetary 3D reconstruction. SAfS refines the surface slopes of a stereo Digital Elevation Model (DEM) and typically yields 3D models at image resolution. This approach is well-validated for scientifically calibrated instruments that observe the planetary body under favorable conditions. This work applied the SAfS algorithm to more challenging planetary flyby images acquired with uncalibrated off-the-shelf cameras. We investigated three scenarios: a fly-by image of the Moon captured by a GoPro during the Artemis I mission, a fly-by image of Mercury which was obtained with a monitoring camera during BepiColombo’s third flyby, and a telescope image taken in Wetter, Germany. We qualitatively and quantitatively assessed the algorithm's performance. The results of the two flyby images indicate that, despite the challenging conditions, the SAfS algorithm could reconstruct the surface up to image resolution and increase the level of detail of the input DEM. The reconstructed DEM of the telescope image is the one with the lowest resolution. All in all, our flyby-derived DEMs are accurate. They provide excellent outreach products, as demonstrated by ESA's BepiColombo flyby movie: https://www.esa.int/Science_Exploration/Space_Science/BepiColombo/BepiColombo_s_third_Mercury_flyby_the_movie</p> <p><strong>Dataset<br></strong></p> <p>We applied the SAfS algorithm to different Regions of Interest (ROIs) in the flyby and telescope images. The ROIs are marked in Artemis_Flyby_ROIs.png, Bepicolombo_Flyby3_ROIs.png and Moon_Telescope_ROIs.png, respectively. For each ROI a DEM is provided centered on the latitude and longitude (0-360, positive east) in the filename. Furthermore a Red/ Blue Stereo anaglyph of the original image was created with the SAfS DEM (for this purpose the height has been exaggerated).</p> <p> </p>
High-resolution digital elevation model of Klados Gorge, Crete, Greece
<p>High-resolution digital elevation model constructed from photogrammetric processing of drone images taken at Klados Gorge, Crete, Greece in 2017. The authors used <em>Agisoft</em> PhotoScan for the photogrammetric processing and to generate 3D spatial data for further use.</p>
Orthomosaics and digital elevation model of the 'Bear Trap' - a Norse ruin in Northwest Greenland
<p>This dataset consists of a digital elevation model (DEM) and an orthomosaic of the ‘Bear Trap’ (also called ‘Bjørnefælden’ in Danish, and ‘Putdlagssuaq’ or ‘The Great Trap’ Greenlandic Kalaallisut), a Norse ruin at the western end of the Nuussuaq Peninsula in NW Greenland. Images comprise 1032 low-altitude aerial images acquired from an unoccupied aerial vehicle (DJI Phantom 3 Standard). These images were processed using Agisoft Metashape Pro (v1.7; Linux Ubuntu) following the USGS protocols for processing imagery in coastal areas. The locations of 8 ground control points (GCPs) were surveyed with a high accuracy global navigation satellite system (GNSS) receiver (Emlid Reach). The base station and rover data were processed using the Emlid distribution of the free RTKLib software (<a href="https://docs.emlid.com/reach/common/tutorials/gps-post-processing/">https://docs.emlid.com/reach/common/tutorials/gps-post-processing/</a>). Geoid height was computed using the online UNAVCO Geoid Height Calculator (<a href="https://www.unavco.org/software/geodetic-utilities/geoid-height-calculator/geoid-height-calculator.html">https://www.unavco.org/software/geodetic-utilities/geoid-height-calculator/geoid-height-calculator.html</a>). During the image alignment step in Metashape, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were not used. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the intrinsic camera parameters were computed for each camera calibration group. GCPs were then imported and placed in each image. The dense point cloud was then computed using the ‘Ultra High’ quality setting, followed by the DEM and orthomosaic. The resolution of the orthomosaic is 1.83 cm/pixel. 5 cm resolution orthomosaic and DEM were also exported for use in QGIS. </p> <p> </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p> </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, in affiliation with the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper archaeological permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provide an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provide further details specific to The Bear Trap and surrounding archaeological contexts observed during the 2019 survey.</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. Submitted to <em>Arctic Anthropology</em>.</p>
Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling
<p>The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modelling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modelling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.</p>
Digital twin of infant microbiome (qnet models)
<p>The complexity of the gut ecosystem, with thousands of cross-talking microbial colonizers, together with sparsely observed abundance profiles, has limited progress. We have developed a computational framework to learn an approximate “digital twin” of the maturing infant microbiome, that once learned, can reliably forecast detailed ecosystem trajectories unfolding over weeks from few initial observations. This generative model (Q-net), inferred at the level of taxonomic classes of microbes automatically from standard 16S rRNA profiles, is used to uncover actionable patterns driving developmental fate in early life. Here we publish the key qnet models to accompany our upcoming publication.</p>
Digital elevation models, ortho images and outlines of Yala Glacier, Langtang Valley, Nepal Himalaya
<p>Datasets related to article "Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya". The data includes three digital elevation models (DEM), two ortho images and five outlines of Yala Glacier between 1981 and 2015.<br> </p> <p>Description of files:<br> 1) DEM and ortho image</p> <p>- 1981Yala_dem_20_-10_bias_cor.tif: 10 m resolution digital elevation model derived from a map that was generated using ground phogogrammetry images that were acquired in 1981 (Yokoyama, 1984; Fujita and Nuimura).</p> <p>- 2007Yala_dem_0_-4_bias_cor.tif: 2 m resolution digital elevation model derived from 14 oblique photographs that were acquired by a private jet with handheld cameras in 2007.<br> - 2007Yala_ortho.tif: an ortho images derived by the same data in 2007.</p> <p>- 2015Yala_dem_0_0_bias_cor.tif: 1 m resolution digital elevation model derived from 519 photographs that were acquired by a UAV-based photogrammetric survey in 2015.<br> - 2015Yala_ortho.tif: an ortho images derived by the same data in 2015.<br> <br> 2) Glacier boundary (shapefile Files)</p> <p>- Yala_area_1981: <br> - Yala_area_2007:<br> - Yala_area_2009:<br> - Yala_area_2012:<br> - Yala_area_2015:<br> <br> <br> Please see the related journal article for details on datasets.<br> <br> Sunako, S., Fujita, K., Izumi, T., Yamaguchi, S., Sakai, A., & Kayastha, R. (2023). Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya. Journal of Glaciology, 69(274), 425-432. doi:10.1017/jog.2022.118<br> </p>
INCA-CH seamless nowcasting system: 1km digital elevation model
<p>Digital elevation model at 1km horizontal resolution used in the INCA-CH seamless nowcasting system (in Swiss coordinates CH03). For INCA-CH parameters see here: <a href="https://zenodo.org/record/6470725">INCA-CH set of data</a></p>
The Svalbox Digital Model Database (DMDb)
<p><strong>The Svalbox Digital Model Database</strong><br> The Svalbox Digital Model Database (DMDb) is a regional DOM database geographically constrained to the Norwegian High Arctic Archipelago of Svalbard at 74-81 °N and 10-35 °E. Svalbard offers exceptional quality, vegetation-free outcrops with a wide range of lithologies and tectono-magmatic styles including extension, compression and magmatism. Data and metadata of the systematically digitized outcrops across Svalbard are shared according to FAIR principles through the Svalbox DMDb. Fully open-access and downloadable DOMs include not just the DOM itself but also the input data, processing reports and projects, and other data products such as footprints and orthomosaics. Rich metadata for each DOM comprises both the technical and geological parameters (metadata), enabling visualisation and integration with regional geoscientific data on the Svalbox portal.<br> <br> <strong>Release information</strong><br> The v2023.3 release of the Svalbox DMDb, documented in this contribution, covers 135 DOMs cumulatively covering 113.946 km2 and offering Proterozoic to Cenozoic stratigraphy.<br> <br> <strong>Release documentation</strong><br> The Svalbox DMDb v2023.3 is shared in the following formats:</p> <ul> <li>GeoJSON (.geojson)</li> <li>Geopackage (.gpkg)</li> </ul> <p>Documentation of the types and units associated with the data columns is documented through the SvalboxDMDb parameters files, available in:</p> <ul> <li>Comma-separated values (.csv)</li> <li>JSON (.json)</li> </ul> <p>Updated metadata is continuously updated to the Svalbox server and made available through:</p> <ul> <li><a href="https://www.svalbox.no/map/">https://www.svalbox.no/map/</a></li> <li>Svalbox ArcGIS REST access point (<a href="https://svalbox.unis.no/arcgis/rest/services/">https://svalbox.unis.no/arcgis/rest/services/</a>)</li> </ul> <p><strong>Citation</strong><br> In addition to model-specific DOIs, please cite the following papers when using the Svalbox DMDb and its models:</p> <ul> <li>Betlem, et al. "The Svalbox Digital Model Database: a geoscientific window to the High Arctic." Geosphere (in review).</li> <li>Senger, et al. "Using digital outcrops to make the high Arctic more accessible through the Svalbox database." Journal of Geoscience Education 69.2 (2021): 123-137.</li> </ul>
Drone orthomosaic and digital elevation model of the Atanikerluk archaeological site in NW Greenland
<p>This dataset consists of a digital elevation model (DEM) and an orthomosaic of the Atanikerluk archaeological site (NKAH 1724), located on the Nuussuaq Peninsula in NW Greenland near the settlement of Saqqaq. The DEM and orthomosaic were produced from 612 aerial images that were acquired by a DJI Phantom 3 drone. The images were processed using Agisoft Metashape Professional (version 1.7.1) following protocols established by the United States Geological Survey (USGS) for drone surveys of coastal regions (Over et al., 2021). Five ground control points were distributed throughout the survey area. The drone survey of the Atanikerluk site 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, in affiliation with the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper archaeological permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provide an overview of the archaeological surveys conducted during the VIMOA project.</p> <p>Over et al. (2021) Processing Coastal Imagery With Agisoft Metashape Professional Edition, Version 1.6—Structure From Motion Workflow Documentation. USGS Open File Report 2021-1039. doi:10.3133/ofr20211039. <a href="https://pubs.er.usgs.gov/publication/ofr20211039">https://pubs.er.usgs.gov/publication/ofr20211039</a></p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. Antiquity 94:e6 doi:10.15184/aqy.2019.230</p>
Data from: Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies
Open the record for dataset details and reuse information.
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Figures of the paper"A Conceptual Model of Chinese Oral Memory Based on Digital Humanities"
<p>Those are the figures used for the paper "A Conceptual Model of Chinese Oral Memory Based on Digital Humanities".</p>
Coastal Digital Elevation Models and Transects of the Reef Island Fuvahmulah, the Maldives
<p>The data contains <strong>two Digital Elevation Models</strong> (DEMs) of the coastal zone in the south-east of <strong>Fuvahmulah, the Maldives</strong> (location: latitude -0.30° and longitude 73.43°). DEM files are in the file format <em>*.tif</em> (raster data georeferenced to WGS84). The DEMs contain elevation data on the area adjacent to the seaport. One DEM was measured in 2017, the other in 2019. The 2017 DEM is based on aerial imagery, recorded with the consumer-grade unmanned aerial vehicle (UAV, or drone) DJI Phantom 4 in 2017, while the 2019 DEM was recorded with a DJI Phantom 4 Pro. The data was then processed in <em>Agisoft Photoscan</em> with a Structure-from-Motion - MultiView Stereo algorithm (SfM-MVS).<br> <br> In addition, the data set contains <strong>elevation</strong> and <strong>location data</strong> of 4 transects from sections along the east coast of Fuvahmulah. The data is stored in <em>*.csv </em>files, containing longitude, latitude, height and distance.<br> <br> Finally, there are two further data files with <strong>gridded topographic and bathymetric data</strong>, ready for use in the depth-integrated <strong>Boussinesq type model <em>BOSZ</em></strong> (see Roeber and Cheung, 2012; doi.org/10.1016/j.coastaleng.2012.06.001). Currently, the numerical wave model, incorporates lateral wave makers, so that the elevation data needs to be rotated to account for different wave directions <span class="math-tex">\(\theta\)</span>. The bathymetry was recorded with a <em>Dr. Fahrentholz LituBox 15/200</em> and truncated to 200 meters water depth. The coastal topography results from coastal DEMs, while the island's mainland is set to about 2-3 meters (no elevation information was available for the island's inland area). The data was gridded and interpolated onto the grid with <em>The Generic Mapping Tools (GMT)</em>. The grid size is 7.5 x 7.5 meter.<br> The files are in <em>MATLAB® 5.0</em> file format <em>*.mat</em>, containing the variables '<em>length</em>' and '<em>width</em>' of the domain in meters, '<em>X</em>' and '<em>Y</em>' (computation grid <span class="math-tex">\(x, y\)</span> in degrees <span class="math-tex">\(^\circ\)</span> longitude and latitude), as well as '<em>BATHY</em>', being the elevation data for the X,Y grid. In addition, friction values as used in the model is stored in the variable '<em>Friction</em>', as well as the grid increments '<em>DX</em>' and '<em>DY</em>' (<span class="math-tex">\(\Delta x\)</span> and <span class="math-tex">\(\Delta y\)</span> in degrees <span class="math-tex">\(^\circ\)</span> longitude and latitude).</p> <p>Each folder contains either a README-file or Jupyter Notebook (Python 3). The Notebooks allow the user to access and view the files (Python 3 and Jupyter Notebook installation required)</p>
Digital Heritage Module - Skull Model
Photogrammetry model of skull from UCL Digital Heritage Module (Prof Gai Jorayev; Students: Alison G, Runqi W, Yuexi Y, Che W, Emma R) Source: Objaverse 1.0 / Sketchfab
Digital Elevation Models of Shatter cones
<p>20 Digital Elevation Model of Shatter cones </p> <p>Haughton Dome (1 model using Helicon Focus, 1 Model from Laser Scanning)<br /> Jebal Waqf as Suwwan (1 model using Helicon Focus, 1 Model from Laser Scanning)<br /> Gosses Bluff (4 models using Helicon Focus)<br /> Serra da Cangalhia (1 model from Laser Scanning)<br /> Rochechouart (2 models from Laser Scanning)<br /> Steinheim Basin (3 models from Laser Scanning)<br /> Vargeão Dome (1 model from Laser Scanning)<br /> Vista Alegre (1 model from Laser Scanning)<br /> Vredefort (4 models from Laser Scanning)</p>
Posina Catchment Digital elevation model
<p>This is the DEM data set used in paper "Estimating the water budget components and their variability in a Pre-Alpine basin with JGrass-NewAGE" by Abera W., Formetta G., Borga M. and Rigon R. </p>
Digital research data from: Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar
<p>This is digital research data corresponding to a published manuscript, Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar. Chemosphere (2024), 347, 140688. https://doi.org/10.1016/j.chemosphere.2023.140688. </p> <p>Common isotherm and kinetic models cannot describe the pH-dependent sorption of heavy metal cations by biochar. In this paper, we evaluated a pH-dependent, equilibrium/kinetic model for describing the sorption of cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) by poultry litter-derived biochar (PLB). We performed sorption experiments across a range of solution pH, initial metal concentration, and reaction time. </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.