Datensatz:

Replication Data for: Towards a Better Understanding of Graph Perception in Immersive Environments

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Datum der Erstveröffentlichung

2025

Autor:innen

Wang, Yao
Zhang, Lin
Bulling, Andreas

Andere Beitragende

Repositorium der Erstveröffentlichung

Universitätsbibliothek Stuttgart

Version des Datensatzes

V1
DOI (Link zu den Daten)
https://doi.org/10.18419/darus-5259
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oops

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Deutsche Forschungsgemeinschaft (DFG): 251654672

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Publikationsstatus
Published

Zusammenfassung

As Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments. However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance. To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment. Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation. The files of this dataset are documented in <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=415768">README.md</a>.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Computer and Information Science, Information Visualization, Data Visualization, Visual analytics, Virtual reality, Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing

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ISO 690WANG, Yao, Lin ZHANG, Ying ZHANG, Wilhelm KERLE-MALCHAREK, Karsten KLEIN, Falk SCHREIBER, Andreas BULLING, 2025. Replication Data for: Towards a Better Understanding of Graph Perception in Immersive Environments
BibTex
RDF
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    <dc:contributor>Schreiber, Falk</dc:contributor>
    <dc:creator>Zhang, Lin</dc:creator>
    <dcterms:issued>2025</dcterms:issued>
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    <dc:contributor>Bulling, Andreas</dc:contributor>
    <dc:creator>Schreiber, Falk</dc:creator>
    <dcterms:created rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-08-12T13:23:46Z</dcterms:created>
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    <dc:creator>Klein, Karsten</dc:creator>
    <dc:creator>Kerle-Malcharek, Wilhelm</dc:creator>
    <dcterms:abstract>As Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments.
However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance.
To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment.
Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation. 
The files of this dataset are documented in &amp;lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=415768"&amp;gt;README.md&amp;lt;/a&amp;gt;.</dcterms:abstract>
    <dc:creator>Wang, Yao</dc:creator>
    <dc:contributor>Zhang, Lin</dc:contributor>
    <dc:contributor>Zhang, Ying</dc:contributor>
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    <dc:contributor>Wang, Yao</dc:contributor>
    <dc:creator>Bulling, Andreas</dc:creator>
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    <dcterms:title>Replication Data for: Towards a Better Understanding of Graph Perception in Immersive Environments</dcterms:title>
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    <dc:contributor>Klein, Karsten</dc:contributor>
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