Group headed by Andreas Bulling of the Human-Computer Interaction and Cognitive Systems department of the University of Stuttgart.
Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 10 of 25 Results
Jun 29, 2026 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Zhang, Lin; Zhang, Ying; Huettner, Timo; Kerle-Malcharek, Wilhelm; Klein, Karsten; Schreiber, Falk; Bulling, Andreas, 2026, "Replication Data for: Understanding and Enhancing Stereoscopic 3D Graph Perception with Eye Tracking", https://doi.org/10.18419/DARUS-5950, DaRUS, V1
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...
Feb 24, 2026
Sood, Ekta; Kögel, Fabian; Bulling, Andreas, 2024, "VQA-MHUG", https://doi.org/10.18419/DARUS-4428, DaRUS, V3
We present VQA-MHUG - a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA), collected using a high-speed eye tracker. To the best of our knowledge, this is the first resource containing multimodal human gaze data over a textual question and the corresponding image. Our corpus en...
Nov 17, 2025 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)", https://doi.org/10.18419/DARUS-3884, DaRUS, V3
Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a q...
Sep 3, 2025 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Zhang, Lin; Zhang, Ying; Kerle-Malcharek, Wilhelm; Klein, Karsten; Schreiber, Falk; Bulling, Andreas, 2025, "Replication Data for: Towards a Better Understanding of Graph Perception in Immersive Environments", https://doi.org/10.18419/DARUS-5259, DaRUS, V1
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 addr...
May 22, 2024
Zermiani, Francesca, 2024, "InteRead", https://doi.org/10.18419/DARUS-4091, DaRUS, V1, UNF:6:peWc+ExRsnPhsVEeOyMu0w== [fileUNF]
The InteRead dataset is designed to explore the impact of interruptions on reading behavior. It includes eye-tracking data from 50 adults with normal or corrected-to-normal eyesight and proficiency in English (native or C1 level). The dataset encompasses a self-paced reading task of an English fictional text, with participants encountering interrup...
May 16, 2024
Bulling, Andreas, 2024, "InvisibleEye", https://doi.org/10.18419/DARUS-3288, DaRUS, V1
We recorded a dataset of more than 280,000 close-up eye images with ground truth annotation of the gaze location. A total of 17 participants were recorded, covering a wide range of appearances: Gender: Five (29%) female and 12 (71%) male Nationality: Seven (41%) German, seven (41%) Indian, one (6%) Bangladeshi, one (6%) Iranian, and one (6%) Greek...
Apr 8, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)", https://doi.org/10.18419/DARUS-3138, DaRUS, V1, UNF:6:NwphGtoYrBQqd2TyRh0OHA== [fileUNF]
This dataset contains stimuli and collected participant data of VisRecall++. The structure of the dataset is described in the README-File. Further, if you are interested in related codes of the publication, you can find a copy of the code repository (see Metadata for Research Software) within this dataset.
Mar 22, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "Saliency3D: A 3D Saliency Dataset Collected on Screen (Dataset and Experiment Application)", https://doi.org/10.18419/DARUS-4101, DaRUS, V1
While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. Our experimental design reduces the cost and complexity of 3D saliency...
Jun 26, 2023 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao, 2023, "Data for: "Scanpath Prediction on Information Visualizations"", https://doi.org/10.18419/DARUS-3361, DaRUS, V2, UNF:6:cqkNueYjBVCLYaXEqJq3yw== [fileUNF]
We propose Unified Model of Saliency and Scanpaths (UMSS) - a model that learns to predict multi-duration saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has b...
Mar 14, 2023
Bulling, Andreas, 2023, "MPIIFaceGaze", https://doi.org/10.18419/DARUS-3240, DaRUS, V1
We present the MPIIFaceGaze dataset which is based on the MPIIGaze dataset, with the additional human facial landmark annotation and the face regions available. We added additional facial landmark and pupil center annotations for 37,667 face images. Facial landmarks annotations were conducted in a semi-automatic manner as running facial landmark de...
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.