GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality

We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to si...

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Autores principales: Lohr, Dillon, Aziz, Samantha, Friedman, Lee, Komogortsev, Oleg V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060927/
https://www.ncbi.nlm.nih.gov/pubmed/36997558
http://dx.doi.org/10.1038/s41597-023-02075-5
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author Lohr, Dillon
Aziz, Samantha
Friedman, Lee
Komogortsev, Oleg V.
author_facet Lohr, Dillon
Aziz, Samantha
Friedman, Lee
Komogortsev, Oleg V.
author_sort Lohr, Dillon
collection PubMed
description We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to six times each over a 26-month period, each time performing a series of five different ET tasks: (1) a vergence task, (2) a horizontal smooth pursuit task, (3) a video-viewing task, (4) a self-paced reading task, and (5) a random oblique saccade task. Many of these participants have also been recorded for two previously published datasets with different ET devices, and 11 participants were recorded before and after COVID-19 infection and recovery. GazeBaseVR is suitable for a wide range of research on ET data in VR devices, especially eye movement biometrics due to its large population and longitudinal nature. In addition to ET data, additional participant details are provided to enable further research on topics such as fairness.
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spelling pubmed-100609272023-03-30 GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality Lohr, Dillon Aziz, Samantha Friedman, Lee Komogortsev, Oleg V. Sci Data Data Descriptor We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to six times each over a 26-month period, each time performing a series of five different ET tasks: (1) a vergence task, (2) a horizontal smooth pursuit task, (3) a video-viewing task, (4) a self-paced reading task, and (5) a random oblique saccade task. Many of these participants have also been recorded for two previously published datasets with different ET devices, and 11 participants were recorded before and after COVID-19 infection and recovery. GazeBaseVR is suitable for a wide range of research on ET data in VR devices, especially eye movement biometrics due to its large population and longitudinal nature. In addition to ET data, additional participant details are provided to enable further research on topics such as fairness. Nature Publishing Group UK 2023-03-30 /pmc/articles/PMC10060927/ /pubmed/36997558 http://dx.doi.org/10.1038/s41597-023-02075-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Lohr, Dillon
Aziz, Samantha
Friedman, Lee
Komogortsev, Oleg V.
GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
title GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
title_full GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
title_fullStr GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
title_full_unstemmed GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
title_short GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
title_sort gazebasevr, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060927/
https://www.ncbi.nlm.nih.gov/pubmed/36997558
http://dx.doi.org/10.1038/s41597-023-02075-5
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