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882 results for “3d model”
Improving generalisability of 3D binding affinity models in low data regimes
<p>Structures of the PDBBind dataset (general protein-ligand) prepared with CCDC protein preparation software. After preparation, 18310 structures out of the total 19443 remained (1133 failed).</p>
A 3D multi-cellular tissue model of the human omentum to study mechanisms of ovarian cancer metastasis
<p>Here, the design of 3D multi-cellular tissue model of the human omentum is presented to study mechanisms of ovarian cancer metastasis.</p>
Ground truth 3d sphere models
<p>This dataset contains three 3D model files of a sphere, each at a different scale, in .stl format.</p> <p>These models were generated from the 318400-polygon 3D model file of a sphere authored by Kazzee at Thingiverse: <a href="https://www.thingiverse.com/thing:156207">https://www.thingiverse.com/thing:156207</a> .</p>
Ground truth 3d sphere dice models
<p>This dataset contains three 3D model files of sphere dice, with three, six, and twelve concavities, in the .stl format.</p> <p>These 3D model files were modified through scaling from the Sphere Dice dataset by anvil777, items d3.stl, d6.stl, and d12.stl, available from <a href="https://www.thingiverse.com/thing:676660">https://www.thingiverse.com/thing:676660</a> .</p>
Towards safe human-to-robot handovers of unknown containers: pre-trained models and 3D hand keypoints annotations
<p>This repository contains additional data to be used with the implementation of the real-to-simulation framework of the paper <em>Towards safe human-to-robot handovers of unknown containers</em>. The data include pre-trained models and annotations of the 3D hand poses for selected recordings from the public training and testing sets of <a href="http://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM) dataset</a>. The pre-trained models are used for classifying the filling type and filling level of a container. 3D hand poses are annotated as 21 keypoints based on the <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> format.</p>
Brachiopod 3D models
<p>3D models of the brachiopod <em>Rafinesquina</em>, at various gape angles. Original specimen reposited with the Cincinnati Museum Center (CMC). Specimen CMC IP98748. </p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Palaeontological reconstruction (3D model) of Dolichoderus jonasi Dubovikoff et Zharkov, 2022 (worker)
<p>Supplementary file 1 from Dubovikoff, D. A., Zharkov, D. M. 2022. A new species of the genus <em>Dolichoderus</em> Lund, 1831 (Hymenoptera: Formicidae) from a Late Eocene European amber. Caucasian Entomological Bulletin 181, 147–152 (doi:10.23885/181433262022181-147152).</p> <p>Abstract. A new species of ants, <em>Dolichoderus jonasi</em> sp. n., from a Late Eocene amber (Rovno and presumably Baltic ambers) of Europe is described from three workers and one male. The new species differs from all known fossil and recent species of the genus by the following set of characters: the presence of thorns on the pronotum, a head tapering to the back with pronounced occipital angles, a dimpled (with numerous pits) sculpture on the head and thorax, the presence of a ridge on the posterior edge of the main surface of the propodeum with a row of large setae, the presence of large straight setae on the body arranged in rows, high and somewhat narrowed to the apex petiole scale. The described species cannot be assigned to any of species groups (complexes) in the genus. The phylogenetic relationships of the new species with other species of the genus are discussed. Based on the studied morphological features, the species is closest to representatives of the debilis complex, widespread in South and Central America. However, it has significant differences and should be considered as the separate jonasi complex. We used computer microtomography methods to study structures inaccessible for optical microscopes and accurate measurements, which made it possible to characterize all diagnostic characters of the new species. Reconstructions of a worker and a male using 3D modeling are presented. The discovery of D. jonasi sp. n. in European Late Eocene amber is another possible evidence of relations between the faunas of Europe and the Americas in the past.</p> <p> </p>
3D bioprinted alginate-gelatin hydrogel patches containing cardiac spheroids recover heart function in a mouse model of myocardial infarction
<p>Datasets for Roche et al (2023), '3D bioprinted alginate-gelatin hydrogel patches containing cardiac spheroids recover heart function in a mouse model of myocardial infarction'.</p>
Model output data for 3D Climate modelling of LP 890-9 c with a modern Venus-like atmosphere
<p>We make available the output data from 3D climate modelling of LP 890-9 c with a modern Venus-like atmosphere. The data here has been produced for the publication submitted to Monthly Notices of the Royal Astronomical Society: Letters under the title: «3D Global Climate Model of an Exo-Venus: a modern Venus-like Atmosphere for the Nearby Super-Earth LP 890-9 c». The data includes the temperature profiles, emission (thermal) phase curves and transmission spectra files calculated for JWST/NIRSpec Prism. We also make available larger versions of the synthetic observable figures. Proper credit should be given to the authors. For further information, please get in touch with the corresponding author (Diogo Quirino) at: dfquirino@fc.ul.pt</p>
Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04.</p> <p>1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results.</p> <p>Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.</p>
Automatic 3D CAD models reconstruction from 2D orthographic drawings
<p>This dataset is built to reconstruct 3D CAD models from 2D drawings and is based on the public dataset <a href="https://github.com/AutodeskAILab/Fusion360GalleryDataset">Fusion 360 gallery</a>. The dataset includes two parts that are the original data and the reconstructed data (in folders '/original_data" and '/reconstructed'). The part of the original data contains the '.svg' files of 2D drawings and the '.step' files of CAD models. Our reconstruction results are shown in the second part (folder '/reconstructed'), which includes the reconstructed 3D wireframes, 3D shapes with faces (storage in FreeCAD files '.FCStd'), and the images of reconstructed models (screenshot). We also test some cases from the <a href="https://deep-geometry.github.io/abc-dataset/">ABC dataset</a>, shown in the 2_ABC folder.</p> <p>Please cite our paper if you use the dataset.</p> <pre>@article{zhang2023automatic, title={Automatic 3D CAD models reconstruction from 2D orthographic drawings}, author={Zhang, Chao and Pinqui{\'e}, Romain and Polette, Arnaud and Carasi, Gregorio and De Charnace, Henri and Pernot, Jean-Philippe}, journal={Computers \& Graphics}, year={2023}, publisher={Elsevier} }</pre>
Qesem Cave, 3D models of bifaces, DFG-Project UT41/4-1
<p>3D models of bifaces from Qesem Cave, Stucture from Motion (SfM) modelled with Metashape AgiSoft.</p> <p>PDF-files.</p>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
CMC IP98741 Brachiopod 3D Models
<p>3D models of the pedicle valve and brachial valve of the brachiopod, <em>Rafinesquina</em>. These models demonstrate the function of the hinge, and how each valve interlocks. These models are derived from the specimen CMC IP98741 (Cincinnati Museum Center Invertebrate Paleontology), and were created by Aaron Morse and Benjamin Dattilo. </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>
3D structure model of the TgREMIND F-BAR dimer
<p>Coordinates of the AlphaFold2 3D structure model of TgREMIND F-BAR domain dimer (amino acids 80 to 345, UniProt S7W754_TOXGG). The model of the dimer was made using ColabFold v1.5.2 (Mirdita, M., Schütze, K., Moriwaki, Y. <em>et al.</em> ColabFold: making protein folding accessible to all. <em>Nat Methods</em> <strong>19</strong>, 679–682 (2022). https://doi.org/10.1038/s41592-022-01488-1; https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)</p> <pre> </pre>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
3D CAD models of artificial reefs designed by E.Riera from "Unleashing the Potential of Artificial Reefs Design" (STL files)
<p>Here you will find 3D CAD models in STL format.</p> <p>These are 3D CAD models of artificial reefs designed by E.Riera.</p> <p>These STL files (with others not available on open access) have been used to run a script on Python to extract parameters and elements from 3D CAD models to compute complexity indexes from the paper "Unleashing the Potential of Artificial Reefs Design" (<a href="https://doi.org/10.32942/X2G300">https://doi.org/10.32942/X2G300</a>)</p>
Points cloud of 3D CAD models of artificial reefs designed by E.Riera from "Unleashing the Potential of Artificial Reefs Design" (txt files)
<p>Here you will find points cloud of 3D CAD models in txt format.</p> <p>These are 3D CAD models of artificial reefs designed by E.Riera from the paper "Unleashing the Potential of Artificial Reefs Design" (<a href="https://doi.org/10.32942/X2G300">https://doi.org/10.32942/X2G300</a>).</p> <p>These txt files (with others not available on open access) have been used to run a script on R "Computation of fractal dimension" to compute the fractal dimension from the point clouds using the Minkowski-Bouligand method (or "box-counting") using the R statistical framework (version 4.0.3) and “est.boxcount” function of the package “Rdimtools” (You and Shung, 2022). </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.