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288 results for “Digital Modeling”
Figure 8 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 8 Comparison of images taken with a Keyence VHX 5000 digital microscope (lens: Z20, A–C) and DISC3D (D). The whole specimen of Pogonocherus hispidus can be imaged at once with the VHX 5000 with a X30-magnification (A). To compare the digital resolution, we focus on the pronotum of the beetle (B: VHX 5000, ×30; C: VHX 5000, ×100, D: DISC3D, ×1.26). Scale bars: 1 mm.
Figure 7 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 7 Osmia adunca, two exemplary raw images of the front-light stack, with the focal plane going through the proximal (A) and the distal part (B) of the sample, the EDOF image (C), and a detail of the latter (D) demonstrate the resolution. Scale bars: 1 mm.
Figure 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 2 Illumination by two hemispherical white-coated domes (A–C). The back-light-dome can be removed for specimen mounting (D, E). No direct light from the LED-stripes hits the specimens (C, E).
Figure 17 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 17 Relation of surface area and volume for all insect species presented here. While the dipteran, hymenopteran and lepidopteran species had their wings unfolded, all beetles had the wings folded underneath their elytra. Models are not to scale.
Figure 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 3 The camera is mounted on a macro-rail (A). The camera position and orientation can be fine-tuned in all directions (B–D). The camera lens is covered by a pinhole-cap (B).
Figure 10 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 10 Comparison of dense-cloud-based mesh generation and visual consistency meshing of Thricops sp. (A EDOF image). Thin and delicate structure like wings and setae are not well modelled from the dense cloud (B) but well preserved by visual consistency meshing (C).
Figure 11 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 11 Comparison of mesh quality and number of polygons, exemplified by a model of the shell of Discus rotundatus. The model with 1 million faces (A) has a file size (3D-PDF) of 35 MB and shows more detail, but the reduced model with 75.000 faces (B) still well resembles the structure with a file size (3D-PDF) of only 3 MB.
Figure 9 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 9 Workflow of model generation of Pogonocherus hispidus with PhotoScan Pro. In total, 398 EDOF-images are taken with DISC3D (one example is shown in A). Using the image data, masks and camera positions estimated with the calibration sphere (see Fig. 6), a sparse cloud with optimized camera positions is generated (B). Two options for model generation are available: direct mesh calculation based on a dense point cloud (C) or meshing with visual consistency (D). Resulting meshes can be textured (E, F). Scale bar: 1mm.
Figure 12 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 12 Overview and size comparison of the specimens used in this study. Coleoptera: a Prosopocoilus savagei b Anoplotrupes stercorosus, *: specimen was broken during comparative measurements c Stenocorus meridianus d Typhaeus typhoeus e Rutpela maculata f Valgus hemipterus g Cryptocephalus sericeus h Pogonocherus hispidus i Phyllobius pyri j Tytthaspis sedecimpunctata; Lepidoptera: k Zygaena filipendulae; Hymenoptera: l Paraponera clavata m Osmia adunca n Sphecodes ephippius; Diptera: o Thricops sp., p Culex pipiens q Oscinella frit; Gastropoda: r Helicodonta obvoluta s Aegopinella nitens t Discus rotundatus.; Scale bar: 1 cm (keep in mind that not all specimens are equidistant to the lens; i.e., at the same height of the needle).
Videos of mice and squirrels handling various seeds and trained DeepLabCut models for tracking the digits
<p>Videos of freely moving and head-fixed mice handling flaxseeds, wheat berries, millet grains, couscous pellets, black-eyed peas, and peanut fragments, as well as wild squirrels handling peanuts, as used in Barrett et. al., "Manual dexterity of mice during food-handling involves the thumb and a set of fast basic movements", in preparation, plus trained DeepLabCut (DLC) models for tracking the digits of the mice during behaviour.</p> <p>The ZIP file "seed handling videos.zip" contains the videos analysed in the paper as H.264-encoded MP4s with a constant rate factor of 20, which has been shown to not significantly affect DLC tracking results (https://www.biorxiv.org/content/10.1101/457242v1). The ZIP file "squirrel seed handling videos.zip" contains the squirrel peanut handling videos in the same format. Each remaining ZIP file contains one or more DLC project folders (minus the videos subfolder), each containing a trained model for tracking the nose and digits 1-4 on both hands during seed handling. SeedHandling-John-2019-07-02.zip is for front view videos, SeedHandling-John-2019-07-04.zip bottom view, and SeedHandling-John-2019-07-05.zip head-fixed side view. "squirrel deeplabcut.zip" contains models for each set of squirrel videos (SeedHandling-John-2019-03-15\ for 20180921*.mp4 and SeedHandling-John-2019-05-20\ for20190424*.mp4). To use on your own data, simply unzip and use the analyze_videos function in DeepLabCut, setting the config file path to the folder you unzipped the model to. For more details on DeepLabCut, see Mathis et. al. (2018), Nat. Neurosci (https://www.nature.com/articles/s41593-018-0209-y) and http://www.mousemotorlab.org/deeplabcut.</p>
Mapping between Modeling Concepts and Modeling Techniques for Digital Ecosystems
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Kathmandu, Nepal. Digital Terrain Model.
<p>Kathmandu, Nepal. Digital Terrain Model.</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
Validity of Digital Models Obtained With iTero® and Lava Digital® in Comparison With Plaster Models.
ClinicalTrials.gov study NCT01934517. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
ClinicalTrials.gov study NCT06517979. IPD Sharing: NO. Countries: 1. Publications: 0.
Randomized Controlled Trial of the Smart O2O Model Development for Chronic Diseases Management Through Digital Health
ClinicalTrials.gov study NCT06150508. IPD Sharing: NO. Countries: 0. Publications: 1.
Anaheim, CA Digital Elevation Models
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Digital Elevation Model (AZ 7.5 - Minute)
7.5 Minute Digital Elevation Model for the state of Arizona. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). The DEM for 7.5-minute units correspond to the USGS 1:24000 scale topographic quadrangle map series for all of the United States and its territories. Each 7.5 minute DEM is based on 30- by 30-meter data spacing with Universal Transverse Mercator(UTM) projection. Each 7.5- by 7.5-minute block provides the same coverage as the standard USGS 7.5-minute map series.
Snow-Off Digital Terrain Model (DTM) from 2010 LiDAR for the Boulder Creek Critical Zone Observatory (CZO), Colorado
This 1m Digital Terrain Model (DTM) is a snow-off DTM derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from August 2010 for the Boulder Creek Critical Zone Observatory (CZO), near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from 1,375 LiDAR point cloud tiles subsampled from 10 points/m2 to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the Boulder Creek CZO and NCALM, both funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 598.92 km^2. The LiDAR point cloud data was acquired with an Optech Gemini Airborne Laser Terrain Mapper (ALTM) and mounted in a Piper Twin PA-31 Chieftain with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from four GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 0.5 x 1 km LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy is at least, but usually better than, 11 cm RMSE at 1 sigma and vertical accuracy is 5-30 cm RMSE at 1 sigma. The layer is available in IMAGINE format approx. 4 GB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. Accessory layers consist of index map layers for point cloud tiles and flight lines, each with detailed attribute information such as acquisition date and tile file name. The DTM is available through an unrestricted public license. Other LiDAR DSMs, DTMs, and point cloud data ava
Radarsat Antarctic Mapping Project Digital Elevation Model, Version 2
The high-resolution Radarsat Antarctic Mapping Project (RAMP) Digital Elevation Model (DEM) combines topographic data from a variety of sources to provide consistent coverage of all of Antarctica.
ScienceDex guides
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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.