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21 results for “Virtual public”
Dataset related to article "How Academics and the Public Experienced Immersive Virtual Reality for Geo-education"
<p>The dataset is associated with the paper entitled: How Academics and the Public Experienced Immersive Virtual Reality for Geo-education.</p> <p>It contains feedback regarding users’ experience with Immersive Virtual Reality for geological exploration, through a tailored approach developed by Tibaldi et al. (2020) where the Virtual Landscape is based on 3D photogrammetry-based high-resolution models.</p> <p>Such feedback has been acquired through anonymous questionnaires during nine dissemination events held in 2018 and 2019 in various locations (Vienna in Austria, Milan and Catania in Italy and Santorini in Greece), in the framework of the following projects: i) the MIUR project ACPR15T4_00098–Argo3D (http://argo3d.unimib.it/); ii) 3DTeLC Erasmus+Project 2017-1-UK01-KA203-036719 (<a href="http://www.3dtelc.com">http://www.3dtelc.com</a>); iii) EGU 2018 Public Engagement Grant (https://www.egu.eu/outreach/peg/) .</p> <p>In the dataset, feedback has been grouped into categories, based on users age and background:</p> <p>i) Middle and High School Students (Schools students, results in Sheet 1);</p> <p>ii) MSc Students in Earth Sciences (MSc, results in Sheet 2);</p> <p>iii) Academics/Researchers in Earth Sciences, that include PhD students and postdocs (Academics, results in Sheet 3);</p> <p>iv) Lay Public (i.e. participants that do not belong to the other groups, results in Sheet 4).</p> <p>It lists a total of 459 records; further details are available in the manuscript.</p> <p>If you use this dataset, please do cite the following papers:</p> <p>Bonali et al., How Academics and the Public Experienced Immersive Virtual Reality for Geo-education. Geosciences.</p> <p>Tibaldi, A.; Bonali, F.L.; Vitello, F.; Delage, E.; Nomikou, P.; Antoniou, V.; Becciani, U.; Van Wyk de Vries, B.; Krokos, M.; Whitworth, M. Real world–based immersive Virtual Reality for research, teaching and communication in volcanology. Bull. Volcanol. 2020, 82, 1–12.</p> <p> </p> <p> </p> <p> </p>
Data publication: Virtual experiments for steel fiber reinforced high performance concrete (HPC)
<p>This data set contains all necessary inputs for the virtual experiments using an ellipsodal RVE for steel fiber reinforced high performance concrete (HPC), including discretization data, boundary conditions, material parameters and numerical results. The discretization is realized in terms of the finite element method. </p>
Data and code for 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality
<p>This repository contains data and code for</p> <p>Lento B., Segas E., Leconte V., Doat E., Danion F., Péteri R., Benois-Pineau J., de Rugy A. (2024). <strong>3D-</strong><strong>ARM</strong><strong>-Gaze</strong><strong>: a </strong><strong>public </strong><strong>dataset of </strong><strong>3D </strong><strong>A</strong><strong>rm </strong><strong>R</strong><strong>eaching </strong><strong>M</strong><strong>ovements</strong><strong> </strong><strong>with Gaze information</strong><strong> </strong><strong>in </strong><strong>virtual reality</strong><strong>. </strong>doi:</p> <p>It contains a dataset <strong>(DBAS22_DataOnline </strong>folder) of natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. More details could be find in the link publication (see Related identifiers section).</p> <p>The <strong>DBAS22_DocOnline</strong> folder contains all the documentation files. The <strong>MainDataExplained </strong>file lists and describes the variables recorded during the experimental phases. In the <strong>SummaryOfFiles </strong>document, you will find descriptions for all the files within the <strong>DBAS22_DataOnline</strong> folder, and at the bottom, there is also a file tree that illustrates the file structure. The <strong>DBAS22FilesWorkflow </strong>document offers an overview of the workflow of experimental file creation during the experiment.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder contains all the scripts to perform data analysis, listed and described in the files <strong>CodeExplanations </strong>and <strong>DependenciesRelations</strong>. The <strong>GuideInstall </strong>file contains information needed to run the Python code files.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder also contains the DataPlayer Unity project. Instructions for running the project are provided in the <strong>DataPlayerGuide </strong>file and SupplementaryVideo2 (see Related identifiers section for more details). The folder <strong>DBAS22_DataPlayer_StandAloneApp </strong>contains the standalone version of the DataPlayer, which doesn't require any software installation.</p> <p>The <strong>DBAS22_VideoOnline</strong> folder contains all the videos. </p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Related with: Long-short term memory prediction of user's locomotion in Virtual Reality publication (Dataset)
<p>Dataset: Captured motion data from 44 users.</p> <p>Scenes:</p> <p>SL -> Scene Lab.</p> <p>SR -> Escape Room.</p> <p>MF -> Shooter forest.</p> <p>Since it is recorded inside a game engine and all records take place inside their processing, the timestamp is written down for each register (Time_sice_startup field). Additionally, the anonymized identification of the user is recorded (User field).</p> <p>The dataset includes the following characteristics for Oculus Quest 2 HMD and each controller.</p> <ul> <li> DevicePosition (x, y, z): Position recorded.</li> <li> DeviceRotation (w, x, y, z): Rotation expressed with a quaternion.</li> <li> Forward (x, y, z): The unit vector that points to the specific device in the forward direction used in our new model. It can also be obtained by rotating $(0,0,1)$ with the quaternion.</li> <li> DeviceVelocity (x, y, z): Linear velocity of that device in that frame. It represents the rate of change in position.</li> <li> DeviceAcceleration (x, y, z): Linear acceleration of that device in that frame.</li> <li> DeviceAngularVelocity (x, y, z): The angular velocity vector in that frame of the device is measured in radians per second.</li> <li> DeviceAngularAcceleration (x, y, z): The angular acceleration at that frame. </li> </ul> <p>Also for each goal in the scene:</p> <ul> <li> GoalName (x, y, z): Position of that goal. If the element is static, the same position will always be recorded.</li> <li> GoalName_Quat (w, x, y, z): As in the previously defined fields, a rotation is expressed as a quaternion.</li> <li> GoalName_LocalScale (x, y, z): Scale of that element locally related to its parent in the hierarchy. They have no relatives in their hierarchy, so it is the real scale.</li> </ul>
Source data for VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images publication
<p>Source data used to create figures in <em>VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images</em> (Nature Communications, 2023)</p>
A Publicly Available Virtual Cohort of Four-chamber Heart Meshes for Cardiac Electro-mechanics Simulations
<p><strong>Motivation: </strong> Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients.</p> <p><strong>Dataset Description: </strong>We present the first database of four-chamber heart models suitable for electro-mechanical simulations. Our database consists of twenty-four four-chamber heart models generated from end-diastolic CT acquired from heart failure patients recruited for cardiac resynchronization therapy upgrade. We also provide a higher resolution version for each of the twenty-four meshes.</p> <p>We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1.1mm. The elements of all the twenty-four meshes are labelled as follows: 1) Left ventricle myocardium 2) Right ventricle myocardium 3) Left atrium myocardium 4) Right atrium myocardium 5) Aorta wall 6) Pulmonary artery wall 7) Left atrium appendage ring 8) Left superior pulmonary vein ring 9) Left inferior pulmonary vein ring 10) Right inferior pulmonary vein ring 11) Right superior pulmonary vein ring 12) Superior vena cava ring 13) Inferior vena cava ring 14) Mitral valve plane 15) Tricuspid valve plane 16) Aortic valve plane 17) Pulmonary valve plane 18) Left atrial appendage valve plane 19) Left superior pulmonary vein valve plane 20) Left inferior pulmonary vein valve plane 21) Right inferior pulmonary vein valve plane 22) Right superior pulmonary vein valve plane 23) Superior vena cava valve plane 24) Inferior vena cava valve plane.</p> <p>Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80˚ to -60˚, respectively. We defined a system of universal ventricular coordinates on the meshes, see Figure 1B: an apico-basal coordinate varying continuously from 0 at the apex to 1 at the base; a transmural coordinate varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate varying continuously from – π at the left ventricular free wall, 0 at the septum and then back to + π at the left ventricular free wall; intra-ventricular coordinate defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -100. </p> <p>We also refined each mesh from 1.1mm resolution down to 0.39mm resolution. Each refined mesh has tags defined on its elements (same numbering as described above) and ventricular fibres.</p> <p><strong>Database format: </strong>We provide a zipped folder for each mesh. Each folder contains the coarse and the finer versions of the same mesh. All twenty-four 1mm-meshes are supplied in case format, readable with paraview. All binary files containing the meshes data (ens and geo formats) are provided within the zipped folder. Points coordinates are given in mm. Element tags are assigned to the elements of the mesh as well as fibres and sheet directions. Fibres and sheet directions are assigned to the ventricles according to a rule-based method, while non-ventricular elements are assigned with default vectors [1; 0; 0] and [0; 1; 0]. UVCs are assigned to the nodes of the meshes. We also provide the location of the cardiac resynchronisation therapy right-ventricular electrode used to initiate ventricular excitation. This is given as a label on the nodes called electrode endo rv, which is 1 at the stimulated nodes. Finer meshes are provided in vtk format, also readable in paraview. For these meshes, we provide element tags, fibres and sheet directions on the ventricles, all in the same file.</p>
Dataset - Virtual Influencers and Public Perception: Social Media Sentiment Analysis and A Comprehensive Bibliometric
<p>The dataset contains a collection of articles related to Virtual Influencers and Public Perception: Social Media Sentiment Analysis and A Comprehensive Bibliometric in the Scopus database.</p>
Dataset for the publication: User experience, game satisfaction and engagement with the virtual simulation VR FestLab for alcohol prevention: A quantitative analysis among Danish adolescents
<p>Anonymized dataset for the publication: User experience, game satisfaction and engagement with the virtual simulation VR FestLab for alcohol prevention: A quantitative analysis among Danish adolescents</p>
Dataset for the publication: Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model
<p>This dataset includes the data from the publication <em>Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model. </em>The first table sheet lists the data of the variables; the second table sheet describes the coding of the variables. </p>
Virtual Reality Exposure Therapy for Public Speaking Anxiety
ClinicalTrials.gov study NCT03885414. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
CONTENT ANALYSIS OF APPEALS RECEIVED AT THE VIRTUAL AND PUBLIC RECEPTION CENTRES OF THE PRESIDENT OF THE REPUBLIC OF UZBEKISTAN RELATED TO THE HEALTH SECTOR
Open the record for dataset details and reuse information.
Figure 1 from: Smith V, Rycroft S, Brake I, Scott B, Baker E, Livermore L, Blagoderov V, Roberts D (2011) Scratchpads 2.0: a Virtual Research Environment supporting scholarly collaboration, communication and data publication in biodiversity science. ZooKeys 150: 53-70. https://doi.org/10.3897/zookeys.150.2193
Figure 1 - Scratchpad usage statistics from February 2007 to September 2011. The black dashed line represents the number of Scratchpad community sites (in hundreds) and the blue solid line represents the number of registered users (in thousands). As of September 2011 we have switched to recording the number of active users (currently 4424) since this figure provides a more accurate guide to usage.
Figure 2 from: Smith V, Rycroft S, Brake I, Scott B, Baker E, Livermore L, Blagoderov V, Roberts D (2011) Scratchpads 2.0: a Virtual Research Environment supporting scholarly collaboration, communication and data publication in biodiversity science. ZooKeys 150: 53-70. https://doi.org/10.3897/zookeys.150.2193
Figure 2 - Screenshots of the Scratchpad 2 publication module showing an example workflow. Top, the section writing tool showing material and methods section; middle, the relationship selector that allows a taxon and additional materials to be associated with a section of the publication; and bottom, supplementary files such as illustrations, photos or graphs can be added to complete the publication.
Use of Virtual Reality to Communicate Concepts of Genomics to the General Public
ClinicalTrials.gov study NCT00316056. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Virtual Reality as a Tool for Training Public Speaking Skills in Higher Education Students
ClinicalTrials.gov study NCT07392554. IPD Sharing: NO. Countries: 1. Publications: 0.
Enhancing Generalization of Virtual Reality Exposure for Public Speaking Anxiety
ClinicalTrials.gov study NCT07323498. IPD Sharing: NO. Countries: 1. Publications: 0.
Virtual Reality Exposure for Public Speaking Anxiety
ClinicalTrials.gov study NCT06214039. IPD Sharing: YES. Countries: 1. Publications: 0.
ACT and Virtual Reality for Public Speaking Fear
ClinicalTrials.gov study NCT05573620. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Virtual CBTm for Public Safety Personnel
ClinicalTrials.gov study NCT05121194. IPD Sharing: NO. Countries: 1. Publications: 0.
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.