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2,309 results for “virtual reality”
A contextual fear conditioning paradigm in head-fixed mice exploring virtual reality
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Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
Data from: Gait coordination in overground walking with a virtual reality avatar
<p>Little information is currently available on interpersonal gait synchronisation in overground walking. This is caused by difficulties in continuous gait monitoring over many steps while ensuring repeatability of experimental conditions. These challenges could be overcome by utilising immersive virtual reality (VR), assuming it offers ecological validity. To this end, this study provides some of the first evidence of gait coordination patterns for overground walking dyads in VR. Six subjects covered the total distance of 27 km while walking with a pacer. The pacer was either a real human subject or their anatomically and biomechanically representative VR avatar driven by an artificial intelligence algorithm. Side-by-side and front-to-back arrangements were tested without and with the instruction to synchronise steps. Little evidence of spontaneous gait coordination was found in both visual conditions, but persistent gait coordination patterns were found in the case of intentional synchronisation. Front-to-back rather than side-by-side arrangement consistently yielded in the latter case higher mean synchronisation strength index. Although the mean magnitude of synchronisation strength index was overall comparable in both visual conditions when walking under the instruction to synchronise steps, quantitative and qualitative differences were found which might be associated with common limitations of VR solutions.</p>
Learning my way: a pilot study of navigation skills in Cerebral palsy in Immersive Virtual Reality
<p>The dataset includes data about 15 children with Cerebral Palsy (CP) and 13 typically developing (TD) peers that performed a new navigation task in Immersive Virtual Reality (IVR) in order to assess the individual navigation strategies and their modifiability in a situation resembling real life.</p> <p> </p>
Virtual Reality Traces for Traffic Classification
<p>We use two Python scripts located in the 'Python Scripts' folder: one for `Feature Extraction' and another for the `Classification Model'. Initially, we extract features from raw packet traces, which have been stored in the 'Packet Traces' folder. The `Feature Extraction' script generates CSV output files, which become the input for the `Classification Model' script. The resulting input for the training and testing phase is stored in the folders of `Input For Training' and `Input For Test', respectively. Specifically, we have four designated folders: `Packet Traces', `Input For Training', `Input For Test', and `Python Scripts'.</p>
Video: virtual reality experience
<p><strong>Supplementary Materials for the publication:</strong></p> <p>A preparatory virtual reality experience reduces anxiety before surgery in gynecologic oncology patients: a randomized controlled trial.</p> <p> </p>
Exploring Immersive Virtual Reality in Higher Education: Research Gap and future Direction - A Scoping review
<p><span>The rapid advancement of technology in the post-COVID-19 era has positioned immersive learning as a transformative approach to enhance educational experiences. Despite its vast potential, recent research developments reveal persistent challenges and gaps that impede widespread adoption. This study conducts a scoping review using the PRISMA methodology to systematically analyze current literature, identify research gaps, seek the challenge, and propose future research directions. From an initial pool of 414 papers, 75 were selected, comprising 60 research studies and 15 review papers. Notably, 55 studies focus on immersive virtual reality (IVR) purely for educational enhancement in traditional academic settings, while 20 explore the implementation of IVR in education with gaming activities. The analysis indicates a predominance of mixed-methods research within education, computer engineering, and computer science. Most studies are limited by short durations (typically 30 minutes) and small participant groups (under 50), raising concerns about the generalizability of findings. Key themes identified include learning context (21 papers), learning design strategies (10 papers), and immersion elements such as avatars and haptic feedback (6 papers). While positive impacts like increased satisfaction, motivation, engagement, knowledge enhancement, and usability are reported, negative effects such as motion sickness (13 papers) and dizziness (11 papers) persist. Crucially, only 11 studies exhibit high statistical power, underscoring the need for more robust research designs. Challenges identified encompass participant limitations, homogeneity, user discomfort, hardware unfamiliarity, and cognitive load—all intricately linked to design strategies. The implications of this review highlight the necessity for future research to focus on long-term studies, optimize user experience, develop cost-effective content creation methods, and integrate gamification into learning design. Addressing these areas is essential for overcoming current barriers and fully realizing the potential of immersive learning in education.</span></p>
Dataset underpinning "A pilot randomised trial of a brief virtual reality scenario in smokers unmotivated to quit: Assessing the feasibility of recruitment"
<p>This is the dataset underpinning the manuscript titled "A pilot randomised trial of a brief virtual reality scenario in smokers unmotivated to quit: Assessing the feasibility of recruitment".</p>
Could an Immersive Virtual Reality training improve navigation skills in children with cerebral palsy? A pilot controlled study
<p>The dataset includes demographic data, visuospatial abilities and navigational abilities in tipically developing children, in children with cerebral palsy that performed a 20-sessions motor training in immersive virtual reality (GRAIL system by Motek) and in children with cerebral palsy that performed a 20-sessions navigation training with specific applications designed for the GRAIL system. </p>
Data from: Is there a benefit for anesthesiologists of adding difficult airway scenarios for learning fiberoptic intubation skills using virtual reality training? A randomized controlled study
<p><strong><span>Introduction</span></strong><span><strong>:</strong> Fiberoptic intubation for a difficult airway requires significant experience. Traditionally only normal airways were available for high fidelity bronchoscopy simulators. It is not clear if training on difficult airways offers an advantage over training on normal airways. This study investigates the added value of difficult airway scenarios during virtual reality fiberoptic intubation training.</span></p> <p><span><strong>Methods:</strong> </span><span>A prospective multicentric randomized study was conducted 2019 to 2020, among 86 inexperienced anesthesia residents, fellows and staff. Two groups were compared: Group N (control, n=43) first trained on a normal airway and Group D (n=43) first trained on a normal, followed by three difficult airways. All were then tested by comparing their Global Rating Scores (GRS) on 5 scenarios (1 normal and 4 difficult airways).</span></p> <p><span><strong>Results:</strong> </span><span>The final evaluation GRS score for the normal airway testing scenario was significantly higher for group N than group D: median score 76% (IQR 56.5 - 90) versus 58% (IQR 51.5 - 69, p = 0.0039), but there was no difference in GRS scores for the difficult intubation testing scenarios. </span></p> <p><span><strong>Conclusions:</strong> </span><span>A single exposure to each of 3 different difficult airway scenarios did not lead to better fiberoptic intubation skills on previously unseen difficult airways, when compared to multiple exposures to a normal airway scenario. This finding may be due to the learning curve of approximately 5-10 exposures to a specific airway scenario required to reach proficiency. </span></p>
Data and code for intuitive movement-based prosthesis control in virtual reality
<p>This repository contains data and code for</p> <p><strong>Intuitive movement-based prosthesis control enables arm amputees to reach naturally in virtual reality</strong></p> <p>Effie Segas<sup>1</sup>, Sébastien Mick<sup>1,2</sup>, Vincent Leconte<sup>1</sup>, Rémi Klotz<sup>3</sup>, Daniel Cattaert<sup>1</sup>, Aymar de Rugy<sup>1</sup></p> <p><sup>1</sup> Univ. Bordeaux, CNRS, INCIA, UMR 5287, F-33000 Bordeaux, France</p> <p><sup>2</sup> ISIR UMR 7222, Sorbonne Université, CNRS, Inserm, F-75005, France</p> <p><sup>3</sup> CMPR Tour de Gassies, F-33520 Bruges, France</p> <p> </p> <p> </p> <p>It contains a dataset <strong>(DataOnline_2022_SPCA21 folder</strong>) of three experiments of able-bodied participants (<strong>Exp1 and Exp2 folders</strong>) and amputee participants (<strong>Exp3 folder</strong>) performing a pick-and-placed task in a virtual reality environment with or without movements-based Artificial Neural Networks (ANN) control involved. More information about these experiments could be find in the link publication (see <strong>related identifiers section</strong>).</p> <p>Basic code files to perform data analysis and ANN training are provided in the <strong>CodeOnline_2022_SPCA21 folder. </strong></p> <p>All the information needed to understand the structure of the <strong>DataOnline_2022_SPCA21 </strong>and <strong>CodeOnline_2022_SPCA21 </strong>folders and files are provided in <strong>CodeOnline_2022_SPCA21 folder.</strong></p> <p>The file <strong>SummaryOfFiles </strong>gives a description of all the files of the <strong>DataOnline_2022_SPCA21 folder,</strong> a <strong>file tree </strong>is also provided at the end of the document.</p> <p>The files <strong>MainDataExplained </strong>and <strong>SensorsDataExplained</strong> list and give a description of the variables recorded during experiments in the phase files (e.g. PHASE.json and PHASE_sensors.json respectively).</p> <p>The three <strong>Exp<em>X</em>FilesWorkflow</strong> <strong>files </strong>(with <em>X</em> =1,2,3) give an overview of the work-flow of experimental files creation during the <em>X</em> experiment.</p> <p>The CodeExplanations file lists and gives a description of code files available in the <strong>CodeOnline_2022_SPCA21 folder. </strong>The <strong>DependencyTree</strong> <strong>file</strong> shows the code organisation. The <strong>GuideInstall file</strong> contains information needed to run the code files properly.</p>
Analysis: Systematic literature review PRISMA model results about extended, virtual and augmented reality applied to Science Communication
<div> <p>The results of the analysis made after the PRISMA process of the systematic literature review carried out about results about extended, virtual and augmented reality applied to Science Communication.</p> </div>
Virtual Reality and Charitable Giving: The Influence of Space, Presence, and Attention
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Experimenting with Adaptive Bitrate Algorithms for Virtual Reality Streaming over Wi-Fi
<p>Dataset of resulting files from capturing VR traffic in Wi-Fi 6 of a fork of the Air Light Virtual Reality (ALVR) software, used to stream games from a PC to a VR HMD in real time. The dataset includes: </p> <ul> <li>Parsed Wireshark captures in TSV format, both captured from server and network emulator, and corresponding ALVR session log are found for each experiment. In each folder, all files of netem, server or ALVR are found (with names corresponding to the emulated network effect, which is applied via the netem computer). We are using Constant BitRate (CBR) for each test, at 100 Mbps. The plots are added in the corresponding folder for each effect, and a metric comparison between WS and ALVR. </li> <li>ALVR session logs for a comparison on the logged metrics under tests of Mobility, using different strategies for bitrate adaptation: CBR, ABR and our own contribution.</li> <li>ALVR session logs for a comparison on the logged metrics under tests of emulated capacity drops, using different strategies for bitrate adaptation: CBR, ABR and our own contribution.</li> </ul> <p>The Wireshark captures have been parsed from a PCAPNG into a CSV file (via tshark) containing the principal fields of each packet separated by a space (TSV format). Since the PCAPNG captures were over 1 GB each, we keep only a subset of the first bytes of the payload and the main fields, and discard the rest. There are additional CSV files for TCP UL packets, which we parsed separately from the same captures for us to validate the measured RTT of ALVR.</p> <p>The ALVR session logs contain raw json strings in .txt format, logged from the server using our fork of ALVR. We're using some additional events from the ones ALVR originally used, in order to log our metrics at arbitrary points in the code. </p> <p>The first 22 bytes of the payload in each packet are used to parse into the StreamSocket fields that ALVR uses, and record timestamps to validate the metrics of ALVR manually; which can be used to reproduce our results. Namely, each row of the csv (frame.time_relative, ip.src, ip.dst, frame.len, data.data) contains the timestamp of the packet, its IP source, destination, length and first 22 bytes of the payload as a hexadecimal string.</p> <p>To analyze the CSV via python, the original StreamSocket fields can be recovered for each packet, using iteratively the following lines: </p> <div> <div> <blockquote> <div> data_bytes = bytes.fromhex(row["data.data"])</div> <div> shard_length, stream_id, packet_index, shards_count, shard_index, _ = struct.unpack(">IHIIII", data_bytes)</div> </blockquote> </div> </div> <p> </p>
Replication Kit for the work "Automated and non-Automated Usability Testing of Touchscreens in Virtual Reality"
<p>This replication kit for the work "Automated and non-Automated Usability Testing of Touchscreens in Virtual Reality" contains the Unity project on which the case study was performed, the used AutoQUEST version, the with AutoQUEST recorded data and the AutoQUEST save file of the evaluated usability smells.</p>
Ruinon Landslide, Northern Italy - Evolution and Environmental Factors Through a Virtual Reality Environment - UNITY Dataset
<p><br>This project is part of the thesis titled "Exploring Landslide Evolution and Environmental Factors Through a Virtual Reality Environment", submitted for the MSc. of Geoinformatics Engineering degree at Politecnico di Milano. The development was carried out by Huzaifa Mohammed Khair Khider Abdulaziz and Mohamed Ridaeldin Mukhtar Mohamed during the Academic Year 2023-2024; supervised by Prof. Maria Antonia Brovelli and co-supervised by Dr. Vasil Yordanov.</p> <h2>Video examples of the environment:</h2> <p><a title="Ruinon landslide, Italy - Surrounding area and environmental influencing factors in a VR Environment" href="https://youtu.be/Rpan76VAWFE" target="_blank" rel="noopener">Ruinon landslide, Italy - Surrounding area and environmental influencing factors in a VR Environment</a></p> <p><a href="https://youtu.be/cuq58My4218" target="_blank" rel="noopener">Ruinon landslide, Italy - Landslide evolution over time in the VR Environment.</a></p> <h2>How to use:</h2> <p>To run the project using the Meta Oculus Quest 2, follow these detailed steps:</p> <p> 1. Setting Up Oculus Quest 2 for PC VR</p> <p> A. Install Oculus Software on PC:<br> 1. Download and install the Oculus PC app (Oculus Link) from the official Meta website.<br> 2. Open the Oculus app after installation and sign in to your Oculus account.</p> <p> B. Connecting Oculus Quest 2 to PC:<br> 1. Use a USB-C Cable: Connect the Oculus Quest 2 to your PC using a compatible USB-C cable (Oculus Link cable or any high-quality USB-C cable).<br> 2. Enable Oculus Link:<br> - Once connected, put on your Oculus Quest 2 headset.<br> - In the headset, you’ll see a prompt asking if you want to enable Oculus Link.<br> - Select Enable to connect the headset to the PC.<br> <br> C. Wireless Option (Air Link):<br> 1. Enable Air Link (Optional):If you prefer wireless connectivity, ensure both the PC and Oculus are connected to the same high-speed Wi-Fi network.<br> 2. In the Oculus PC app, go to Settings > Beta and toggle on Air Link.<br> 3. In your headset, go to Settings > Experimental Features and enable Air Link. Pair the headset with the PC following the on-screen instructions.</p> <p> 2. Running the VR Project (VR_Terrain_Visualization.exe):<br> <br> 1. After the Oculus Quest 2 is connected to the PC (either via Oculus Link or Air Link), open Windows Explorer and navigate to the folder containing the project file: VR_Terrain_Visualization.exe.<br> 2. Double-click the VR_Terrain_Visualization.exe file to launch the virtual reality environment.<br> 3. Once the application is running, your Oculus Quest 2 headset will automatically display the project, immersing you in the virtual landslide visualization.<br> <br> 3. Interacting with the VR Environment:<br> <br> - Oculus Quest 2 controllers will allow you to move around, explore the terrain, and interact with different elements of the virtual environment.<br> - Ensure the controllers are working correctly with Unity’s VR features as per the setup.</p> <p> </p>
Virtual Reality Intervention in Chronic Musculoskeletal Pain Syndromes and Fibromyalgia
<p>XLSX file dataset for "Effectiveness and User Experience of a <span>Virtual</span> <span>Reality</span> Intervention in Chronic Musculoskeletal Pain Syndromes and Fibromyalgia". PLOS Digital Health.</p>
Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality [Dataset]
<p>The calcium imaging data of the telencephalon of head-tethered adult zebrafish during GO/NOGO tasks in the virtual reality environment and the behavior data were deposited.</p> <p>The codes to process the neural activity data by calcium imaging to perform Non-negative Matrix Factorization </p> <p>For details, see "Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality" Torigoe et al., Nature Communications in press.</p>
IAVRS - INTERNATIONAL AFFECTIVE VIRTUAL REALITY SYSTEM: database of validated 360° images
<p>IAVRS database contains 46 360° images validated for emotion emotion validation. </p>
Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies
<pre>We introduce a new bimodal dataset recorded during affect elicitation by means of audio-visual stimuli for human emotion recognition based on facial and corporal expressions. Our dataset was collected using three devices: an RGB camera, Kinect 1, and Kinect 2. The Kinect 1 and Kinect 2 sensors provide 121 and 1347 face key points, respectively, offering a more comprehensive analysis of facial expressions. Additionally, for the 2D RGB sequences, we utilized the feature points provided by the open-source OpenFace, which includes 2D 68 facial landmarks. From these landmarks, we selected 26 facial points that were most relevant for our emotion recognition task. To gather the data, we conducted experiments involving 17 participants. We captured both facial and skeleton keypoints, allowing for a comprehensive understanding of the participants' emotional expressions. By combining the RGB and RGB-D data from the various devices, our dataset provides a rich and diverse set of information for human emotion recognition research. This new dataset not only expands the available resources for studying human emotions but also offers a more detailed analysis with the increased number of facial keypoints provided by the Kinect sensors. Researchers can leverage this dataset to develop and evaluate more accurate and robust models for human emotion recognition, ultimately advancing our understanding of how emotions are expressed through facial and corporal cues. Please cite as: K. Amara, O. Kerdjidj and N. Ramzan, "Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies," in <em>IEEE Access</em>, doi: 10.1109/ACCESS.2023.3285398. </pre> <p> </p> <p>Please state your name, contact details (e-mail), institution, and position, as well as the reason for requesting access to our database.</p> <p>For additional info contact:</p> <p>kahina.amara88@gmail.com or kamara@cdta.dz</p> <p>Naeem.Ramzan@uws.ac.uk</p> <p>okerdjidj@ud.ac.ae</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.