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Persistent Identifier
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doi:10.18419/DARUS-5950 |
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Publication Date
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2026-06-29 |
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Title
| Replication Data for: Understanding and Enhancing Stereoscopic 3D Graph Perception with Eye Tracking |
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Author
| Wang, Yaohttps://ror.org/04vnq7t77ORCIDhttps://orcid.org/0000-0002-3633-8623
Zhang, Linhttps://ror.org/04vnq7t77ORCIDhttps://orcid.org/0009-0008-7651-4127
Zhang, Yinghttps://ror.org/0546hnb39ORCIDhttps://orcid.org/0000-0003-4071-0923
Huettner, Timohttps://ror.org/04vnq7t77ORCIDhttps://orcid.org/0009-0004-2978-1258
Kerle-Malcharek, Wilhelmhttps://ror.org/0546hnb39ORCIDhttps://orcid.org/0009-0001-3415-1136
Klein, Karstenhttps://ror.org/0546hnb39ORCIDhttps://orcid.org/0000-0002-8345-5806
Schreiber, Falkhttps://ror.org/0546hnb39ORCIDhttps://orcid.org/0000-0002-9307-3254
Bulling, Andreashttps://ror.org/04vnq7t77ORCIDhttps://orcid.org/0000-0001-6317-7303 |
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Point of Contact
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Use email button above to contact.
Wang, Yao (University of Stuttgart)
Bulling, Andreas (University of Stuttgart) |
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Description
| Immersive Analytics (IA) utilises stereoscopic 3D (S3D) graph visualisations in Virtual and Augmented Reality to harness spatial understanding and engagement beyond 2D screen displays. Despite growing research interest, little is known about how users perceive such structures, or how their gaze behaviour relates to task performance. We address this through a series of eye tracking studies on visual analytical tasks over S3D graphs in VR. Our first study, on single-layer graphs, reveals systematic links between gaze behaviour and task performance, with distinct exploration strategies across tasks. We then extend to multilayer graphs and show how their spatial arrangement and complexity reshape gaze behaviour. Finally, by mining the gaze patterns of successful analysts, we enhance the visualisations with targeted visual cues; in a follow-up study, these improve task correctness in the majority of trials. Overall, our findings deepen the understanding of graph perception in immersive environments, from single to complex multilayer structures, and demonstrate how eye tracking can assess human behaviour and inform the design of more effective S3D graph visualisations. The files of this dataset are documented in README.md. (2026-06-24) |
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Subject
| Computer and Information Science |
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Keyword
| Information Visualisation https://id.loc.gov/authorities/subjects/sh2002000243.html (LCSH)
Data Visualization https://id.loc.gov/authorities/subjects/sh2002000243.html (LCSH)
Visual Analytics https://id.loc.gov/authorities/subjects/sh2007004134.html (LCSH)
Virtual Reality https://id.loc.gov/authorities/subjects/sh92000880.html (LCSH) |
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Topic Classification
| Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing (DFGFO) https://w3id.org/dfgfo/2024/443-05 |
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Related Publication
| Is Supplement To: Y. Wang, L. Zhang, T. Hüttner, Y. Zhang, W. Kerle-Malcharek, K. Klein, F. Schreiber, A. Bulling. Understanding and Enhancing Stereoscopic 3D Graph Perception with Eye Tracking. Under review. |
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Contributor
| Researcher: Zhang, Lin
Project Manager: Wang, Yao
Researcher: Zhang, Ying
Researcher: Hüttner, Timo
Researcher: Kerle-Malcharek,, Wilhelm
Researcher: Klein, Karsten
Project Leader: Falk, Schreiber
Project Leader: Bulling, Andreas |
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Project
| SFB/Transregio 161 (Level 0) |
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Funding Information
| DFG: 251654672 |
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Depositor
| Wang, Yao |
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Deposit Date
| 2026-05-11 |
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Date of Collection
| Start Date: 2025-10-06; End Date: 2025-10-20 |
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Data Type
| graph visualisation; eye tracking data; survey data |
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Related Dataset
| https://doi.org/10.18419/DARUS-5259, https://doi.org/10.18419/DARUS-3387 |