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2,309 results for “Virtual Reality”
Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality Dataset
<p>This dataset accompanies the paper “Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality” published in the Computer and Graphics journal from Elsevier. It contains 3 datasets related to the experiments described in the paper. All datasets are in “.csv” format and can be easily loaded by statistical analysis tools (e.g. a dataset can be loaded in r using the command read.csv(“filename.csv”)). It also contains the C# Unity implementation of the distortion function presented in the paper.</p> <p>Paper reference:</p> <p>Galvan Debarba H, Boulic R, Salomon R, Blanke O, Herbelin B. Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality. Computers & Graphics. 2018; ISSN 0097-8493. Elsevier.</p> <p>DOI: doi.org/10.1016/j.cag.2018.09.001</p>
Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>dataset </strong>used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.</p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
CREATTIVE3D multimodal dataset of user behavior in virtual reality
<p>In the context of the <a href="https://project.inria.fr/creattive3d/">ANR CREATTIVE3D</a> project, we join the expertise of computer science, neuroscience, and clinical practitioners, with the aim to analyze the impact that a simulated low-vision condition has on user navigation behavior in complex road crossing scenes: a common daily situation where the difficulty to access and process visual information (e.g., traffic lights, approaching cars) in a timely fashion can lead to serious consequences on a person's safety and well-being. As a secondary objective, we also aim to investigate the potential role virtual reality could play in rehabilitation and training protocols for low-vision patients.</p> <p>This dataset contains the data as part of the study described in <a href="https://hal.science/hal-04102737">An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality</a>.</p> <p>The dataset is metadata for the pre-print <a href="https://inria.hal.science/hal-04429351">Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset</a></p> <p>To use this dataset, please cite:</p> <blockquote> <pre>@unpublished{wu:hal-04429351, TITLE = {{Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset}}, AUTHOR = {Wu, Hui-Yin and Robert, Florent Alain Sauveur and Gallo, Franz Franco and <br> Pirkovets, Kateryna and Quere, Cl{\'e}ment and Delachambre, Johanna and <br> Ramano{\"e}l, Stephen and Gros, Auriane and Winckler, Marco and Sassatelli, Lucile and <br> Hayotte, Meggy and Menin, Aline and Kornprobst, Pierre}, URL = {https://inria.hal.science/hal-04429351}, NOTE = {working paper or preprint}, YEAR = {2023}, MONTH = Dec, KEYWORDS = {Virtual reality ; Dataset ; Context ; Low vision ; 3D environments ; User study}, PDF = {https://inria.hal.science/hal-04429351/file/2023_CREATTIVE3D_dataset_arxiv_.pdf}, HAL_ID = {hal-04429351}, HAL_VERSION = {v1}, }<br><br>@inproceedings{robert2023integrated, title={An integrated framework for understanding multimodal embodied experiences in interactive virtual reality}, author={Robert, Florent and Wu, Hui-Yin and Sassatelli, Lucile and Ramanoel, Stephen and <br> Gros, Auriane and Winckler, Marco}, booktitle={Proceedings of the 2023 ACM International Conference on Interactive Media Experiences}, pages={14--26}, year={2023} }</pre> </blockquote> <h3> </h3> <h3>Versions</h3> <p>2024-12-18: Updated readme with description of labels, columns, and suggestions on how to start exploring the dataset. We also provide the questionnaire responses and observation notes in English (questionnaire_translation_EN.csv).</p>
Subjective Quality Assessment of Foveated Omnidirectional Images in Virtual Reality (FOIQA)
<p>This study presents a novel dataset called 'Foveated Omnidirectional Image Quality Assessment' (FOIQA) for the subjective quality evaluation of foveated 2D omnidirectional images. This dataset addresses the limitations of existing datasets by leveraging a high-resolution head-mounted display and a gaze-contingent evaluation approach. We provide individual opinion scores, mean opinion scores, and gaze data associated with both the test and reference images. </p>
Video Examples from: Creating Audio Object-focused Acoustic Environments for Room-Scale Virtual Reality
<p>Video recordings illustrating the issues and possible solutions mentioned in the paper.</p> <p>Please use headphones when watching the videos.</p>
Auditory Selective Attention Switch in a Virtual Reality Classroom Environment
<p><strong>General</strong></p> <p>The audio-visual Auditory Selective Attention VR Proof of Concept (asaVRpoc) project serves to investigate the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p>The dataset contains:</p> <ul> <li>Unity project for visual display and the experiment structure</li> <li>Matlab code for experiment preparation and HpFT measurement</li> <li>Data collected in the experiment (experiment performance, head tracking, questionnaires)</li> </ul> <p><strong>Experiment preparation using Matlab</strong></p> <p>The code and software used to prepare the experiment is provided in the folder<em> "matlab_asaVRpoc"</em>.</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox commit hash: 598675ef704c178365f53d41e03ff4b11dea390f). A developmental version of Virtual acoustics (VA) 2020b (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p>Software requirements:</p> <ul> <li>Matlab 2019a or higher</li> <li>ITA Toolbox for Matlab installed</li> </ul> <p> </p> <p><strong>Experiment conduction in Unity</strong></p> <p>The Unity project is provided in the folder<em> "unity_pc_asaVRpoc"</em>.</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>Note that the <em>acoustic stimuli are NOT provided</em> with this Unity project. The stimuli are available on request from the Institute for Hearing Technology and Acoustics, RWTH Aachen University.</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br> The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/).</p> <p>Software requirements:</p> <ul> <li>Unity 2019.4.21.f1.</li> <li>SteamVR 1.19.7</li> <li>Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</li> </ul> <p> </p> <p><strong>Data evaluation</strong></p> <p>The collected data is provided in the folder<em> "dataEvaluation_asaVRpoc"</em>. This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data and responses from the simulator sickness questionnaire (before and after the experiment) and the igroup presence questionnaire (after the experiment). Matlab code for the evaluation of the head tracking data and the questionnaires is provided.</p> <p> </p>
Self-attribution of distorted reaching movements in immersive virtual reality
<p>This dataset and Unity 3D code scripts are associated to the following paper : H. Debarba, R. Boulic, R. Solomon, O. Blanke, B. Herbelin (Computers & Graphics, Vol 76, November 2018, pp 142-152, <a href="https://www.sciencedirect.com/science/article/pii/S0097849318301353?utm_campaign=STMJ_75273_AUTH_SERV_PPUB&utm_medium=email&utm_dgroup=&utm_acid=810891&SIS_ID=0&dgcid=STMJ_75273_AUTH_SERV_PPUB&CMX_ID=&utm_in=DM377782&utm_source=AC_30">in Open Access</a>) : “Self-attribution of distorted reaching movements in immersive virtual reality”. <a href="https://doi.org/10.1016/j.cag.2018.09.001">https://doi.org/10.1016/j.cag.2018.09.001</a></p> <p> </p>
Shopping in Immersive Virtual Reality: Effects of Visual, Auditory, and Cognitive Demands on Mental Workload
<p>The dataset - of the journal article "Shopping in Immersive Virtual Reality: Effects of Diminishing Visual, Auditory, and Cognitive Demands on Workload" - consists of heart rate and eye-tracking data per participant and experimental condition. It also contains the figures inserted in the manuscript, the MATLAB scripts, the Unity project of an immersive virtual supermarket, and a demo video of a participant performing a grocery task across the experimental conditions in the virtual supermarket.</p>
Virtual reality exposure therapy for social anxiety_Premkumar et al 2024
<p>The dataset from the project titled "Augmenting virtual-reality exposure therapy for social anxiety with biofeedback: a randomised controlled trial"</p>
Akzeptanz von Virtual Reality in der Aus- und Weiterbildung im Katastrophenschutz - Daten und Auswertung
<p>These data are the base of a paper submitted to DELFI 2023. The CSV file stores answers from firefighters and ambulance personnel. We used the technology usage inventory (TUI) after participants experienced firefighterVR and i:medtasim.</p> <p>The python-script calculates the min, the max, mean, standard deviation, and stanines of the data.</p> <p>The XLSX produces a bar-plot based on the calculated values from the python script.</p>
Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics
<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years). The cohort was 68% male and 32% female. In terms of education, 16% had attended only high school, while 32% had a bachelor's degree, 42% a master's degree, and 11% a doctorate. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p><strong>Experiment Description:</strong></p> <p>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>—a VR environment that renders virtual arms from a first-person perspective—while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals have been recorded synchronously in a bipolar montage, and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---TASK #<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMO<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHP<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHPVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---ME<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p> </p>
VRGestures: Controller and Hand Gesture Datasets for Virtual Reality
<p>15 VR Controller gestures</p> <p>11 one-handed VR Hand Gestures for each hand</p> <p>2 two-handed VR Hand Gestures </p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
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>
IHTApark. Multi-detailed 3D architectural model for sound perception research in Virtual Reality
<p><strong>IHTApark – Multi-detailed 3D architecture model</strong></p> <p>This dataset describes visual and acoustic 3D architectural models of the park next to the IHTA.</p> <p>Institute of Hearing Technology and Acoustics (IHTA), RWTH Aachen, 52056 Aachen, Germany</p> <p>Files are stored in FBX format for geometry, JPEG format for visual textures, and Unreal Engine for the virtual reality scenes.</p> <p><strong>VERSION 1: Visual photogrammetry + Acoustic model</strong></p> <p>As used in the publication:</p> <p>[1] Llorca-Bofí, J. and Vorländer, M. (2021). Multi-Detailed 3D Architectural Framework for Sound Perception Research in Virtual Reality. Front. Built Environ. 7:687237.doi: https://doi.org/10.3389/fbuil.2021.687237</p> <p>Data is available separately for each definition, and for each visual and acoustic cue. The level of detail for each definition is shown here:</p> <ul> <li>Visual cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> <li>Acoustic cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> </ul> <p>This version of the model includes only the modules used for the description of the referenced paper. The authors reserve the right to complete other levels of detail if future applications require them.</p> <p>An additional data file contains a unique file in [IHTApark_UnrealEngine] Unreal Engine format, with the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_UnrealEgine] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_UnrealEngine]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark</strong> map under the folder <strong>Content > Maps</strong> by double clicking on it.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p><strong>VERSION 2: Object-based visualization in three different weather conditions</strong></p> <p>As used and described in the publication:</p> <p>[2] Submitted to journal.</p> <p>The file [IHTApark_3weath_comp] Unreal Engine format contains the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_3weath_comp] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_3weath_comp]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark_warm</strong>, <strong>IHTApark_wet </strong>or<strong> IHTApark_snowy</strong> maps under the folder <strong>Content > Maps</strong> by double clicking on it to visualize each weather condition.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p>The folder [IHTApark_3weathers_audio] contains the sound signals, as .wav files, in fist order ambisonics format (B-format).</p> <p> </p>
Early Virtual-Reality-Based Home Rehabilitation after Total Hip Arthroplasty: A Randomized Controlled Trial
<p>The benefits of early virtual-reality-based home rehabilitation following total hip arthroplasty (THA) have not yet been assessed. The aim of this randomized controlled study was to compare the efficacy of early rehabilitation via the Virtual Reality Rehabilitation System (VRRS) versus traditional rehabilitation in improving functional outcomes after THA. Subjects were randomized either to an experimental (VRRS; n = 21) or a control group (control; n = 22). All participants were invited to perform a daily home exercise program for rehabilitation after THA with different administration methods—namely, an illustrated booklet for the control group and a tablet with wearable sensors for the VRRS group. The primary outcome was the hip disability (HOOS JR). Secondary outcomes were the level of independence and the degree of global perceived effect of the rehabilitation program (GPE). Outcomes were measured before surgery (T0) and at the 4th (T1), 7th (T2), and 15th (T3) day after surgery. Mixed-model ANOVA showed no significant group effect but a significant effect of time for all variables (<em>p </em>< 0.001); no differences were observed in HOOS JR between VRRS and the control at T0, T1, T2, or T3. Further, no differences in the level of independence were found between VRRS and the control, whereas the GPE was higher at T3 in VRSS compared to the control (4.76 ± 0.43 vs. 3.96 ± 0.65; <em>p </em>< 0.001). Virtual-reality-based home rehabilitation resulted in similar improvements in functional outcomes with a better GPE compared to the traditional rehabilitation program following THA. The application of new technologies could offer novel possibilities for service delivery in rehabilitation.</p>
The Passenger Experience of Mixed Reality Virtual Display Layouts in Airplane Environments - Participants Questionnaire
<p>Dataset for questionnaire data from the paper "<strong>The Passenger Experience of Mixed Reality Virtual Display Layouts in Airplane Environments</strong>" published at ISMAR 2021</p>
The human splenic microcirculation is entirely open as shown by 3D models in virtual reality. Supplementary files
<p>These materials supplement our paper "The human splenic microcirculation is entirely open as shown by 3D models in virtual reality".</p> <p><strong>Summary</strong></p> <p>The human spleen is equipped with an organ-specific microcirculation. The initial part of the venous circulation is formed by spleen-specific large microvessels, the sinuses. Sinuses eventually fuse to form venules and veins. For more than 170 years there have been debates, whether splenic red pulp capillaries join sinuses, i.e., whether the microcirculation is closed or open - or even simultaneously closed and open. We have now solved this question by three-dimensional reconstruction of a limited number of immunostained serial sections of red and white pulp areas, which were visualized in virtual reality. Splenic capillaries have special end structures exhibiting multiple small diverging endothelial cell processes, which always keep a certain distance to the walls of sinuses. Only very few capillary ends were difficult to diagnose. Positive identification of these end structures permits to conclude that the human splenic microcirculation is entirely open. This is also true for the perifollicular capillary network and for capillaries close to red pulp venules. Follicles are supplied by a relatively dense open perifollicular capillary net, which is primarily, but not exclusively, fed by sheathed and few non- sheathed capillaries from the surrounding red pulp network.</p> <p> </p> <p><strong>Interactive models</strong></p> <p>Each <em>file_sX.zip</em> contains a 3D model, registered sequence of serial sections (the input data to generate and validate the model), and an interactive / VR viewer. After you have unzipped the file, there are multiple batch files. Any of them can be used for an interactive model display on a normal monitor, but we recommend the file <em>start_index.bat</em>. Now, if you have a virtual reality headset or a 4K monitor, there are better options:</p> <ul> <li>If you have a HTC Vive-compatible headset (i.e., the "wand" controllers, typically coming with HTC Vive, Vive Pro, Vive Pro 2, etc.), please use <em>start_vive.bat</em>.</li> <li>If you have a Valve Index headset (i.e., the "knuckles" controllers, should also work with Oculus devices), please use <em>start_index.bat</em>.</li> <li>If you have a high-resolution monitor (4K or better), please use <em>start_interactive_4k.bat</em> for a high-resolution interactive model.</li> </ul> <p>In the zip-file is also a <em>README.txt</em> file with instructions on the controls for the interactive and VR viewer. It is also available in the viewer at press of F1 button. In a nutshell: </p> <ul> <li>ASWD control the movement</li> <li>I (the "i" key) turns the sections on and off</li> <li>JK advance the sections</li> <li>CV adjust the height</li> <li>E turns the model on and off.</li> </ul> <p>The viewer executables are built for Windows. They work with Windows 10, should work with previous versions of Windows (64 bit) and also with future versions, such as Windows 11. Users of other OS, such as MacOS or Linux, can use <a href="https://www.meshlab.net/">MeshLab</a> to look at the model. Any image viewer can be used to inspect the sections in the <em>img</em> folder of the unpacked zip file. However, in this case, no VR experience and no simultaneous view of both 3D reconstruction and the sections is possible.</p> <p> </p> <p><strong>Videos</strong></p> <p>The videos with the same content are mostly supplied in three versions:</p> <ul> <li>On any modern hardware you should be able to play the H.265 videos, ending in <em>...4K_h265_10bit.mov</em>, there are in 4K resolution</li> <li>If it is not the case, but you want 4K resolution, use the H.264 version, ending in <em>...4K_h264_10bit.mov,</em> it is also in 10 bit quality</li> <li>A fallback for weaker hardware and maximal compatibility is the H.264 FullHD version. It should play anywhere. Those files end in <em>...1080p_h264.mov.</em></li> </ul> <p> </p> <p><strong>Supplementary Figures S1 and S2</strong></p> <p>The supplementary figures with their legends are available in the file <a href="https://zenodo.org/record/6599487/files/supp.pdf"><em>supp.pdf</em></a>.</p> <p> </p> <p><strong>3D models corresponding to Figs. 4a-d</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s1.zip?download=1">Supplementary file S1</a>. 3D model of ROI 1 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s2.zip?download=1">Supplementary file S2</a>. 3D model of ROI 2 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s3.zip?download=1">Supplementary file S3</a>. 3D model of ROI 3 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s4.zip?download=1">Supplementary file S4</a>. 3D model of ROI 4 with open capillary ends in red</p> <p> </p> <p><strong>File corresponding to Fig. 7a</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s5.zip?download=1">Supplementary file S5</a>. 3D model of sinus network and open capillary ends in red</p> <p> </p> <p><strong>Files corresponding to Figs. 9a,b</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s6.zip?download=1">Supplementary file S6</a>. 3D model of ROI 2 with perifollicular capillary network in red correspondig to Fig. 9a</p> <p><a href="https://zenodo.org/record/6599487/files/file_s7.zip?download=1">Supplementary file S7</a>. 3D model of ROI 2 with perifollicular capillary network in red and open ends in yellow corresponding to Fig. 9b</p> <p> </p> <p><strong>Videos corresponding to Figs 5a-f</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s1_4K_h264_10bit.mov">Supplementary video S1</a>. Two capillaries with open ends in Fig. 5a-c</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s2_4K_h264_10bit.mov">Supplementary video S2</a>. Capillary with at least two open ends in Fig. 5d-f</p> <p> </p> <p><strong>Videos corresponding to Figs 6a-d</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s3_1080p_h264.mov">Supplementary video S3</a>. Quality control of open ends shown in Fig. 5a-c and Fig. 6a,b</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s4_1080p_h264.mov">Supplementary video S4</a>. Quality control of open end shown in Fig 5d-f and Fig. 6c,d</p> <p> </p> <p><strong>Videos corresponding to Fig. 4d and Figs 8a-f</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s5_1080p_h264.mov">Supplementary video S5</a>. Control of open capillary end shown in Fig. 8a-c</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s6_1080p_h264.mov">Supplementary video S6</a>. Control of open capillary end shown in Fig. 8d-f</p>
Augmented Objects as Portals into Virtual Worlds: Using Audio to Create Immersive Experiences in Extended Realities - UMBRELLA AUDIO SPATIALIZATION DEMO
<p><strong>Technical demonstration</strong></p> <p>The results of the projection mapping system in the project are clear from the <a href="https://blog.zhdk.ch/immersivearts/dreaming-of-time-and-space/">main documentation video</a>; however, the impact of the spatial audio system in particular, is best experienced from directly underneath the umbrellas, where one can best appreciate the various levels of mixed reality. Unfortunately, it is difficult to document these effects within the artistic context of the project, and as such, we include a brief set of examples to better demonstrate the 6 degree of freedom sound spatialization capabilities of the umbrella system.</p> <p><em><strong>NOTE:</strong></em> The audio in the following examples is recorded from a fixed perspective (initially underneath the umbrella) and rendered binaurally. Unfortunately, the ambisonic microphone used does not capture directionality very well when the source (in this case, the umbrella speakers) is less than ~1 meter away, and in retrospect, a single channel of pink noise was not a wise choice as a source material, as it appears to cause additional phasing issues. Additionally, the effectiveness of binaural audio varies from listener to listener, so <em>the perceived effect in the video is not as strong as when experienced in person</em>; nonetheless, it is possible to get the basic idea of the spatialization algorithm in action from these examples.</p> <p>PLEASE WEAR HEADPHONES IN ORDER TO EXPERIENCE THE 3D EFFECT.</p> <p>In addition to the view of the entire scene from an outside perspective, several other views of the underlying software are displayed throughout the video, including:</p> <ul> <li> <p>A radar view of the scene (umbrella and sound source) as seen by the space manager software, where the:</p> <ul> <li> <p>Blue circle = umbrella</p> </li> <li> <p>Cyan triangle, yellow square = sound source</p> </li> </ul> </li> <li> <p>A view of elements of the spatialization software running on the umbrella, specifically the:</p> <ul> <li> <p>Relative gain calculations and current output levels of each speaker</p> </li> <li> <p>Results of supporting calculations (e.g. sound location after transformation from the global to local coordinate system, and scaling factors used to attenuate the overall volume of the sound as the distance from the umbrella to the sound changes)</p> </li> </ul> </li> </ul> <p><strong>Demo #1</strong></p> <p>Stationary umbrella with a moving virtual sound source (anchored to a rigid body)</p> <p><strong>Demo #2</strong></p> <p>Rotating umbrella with a stationary sound source (anchored to a rigid body)</p> <p><strong>Demo #3</strong></p> <p>Moving umbrella with a fixed sound source (anchored to a rigid body)</p> <p><strong>Demo #4</strong></p> <p>Moving umbrella with a fixed sound source (anchored to a virtual point in space, located above the microphone); as the umbrella approaches the source, the sound first fades into the room, then collapses into the umbrella, as show in Figure 7 ("Fading between umbrella and room with distance") in the main paper</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.