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

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>.