The Transregional Collaborative Research Centre 161 “Quantitative Methods for Visual Computing” is an interdisciplinary research centre at the University of Stuttgart and the University of Konstanz, funded by Deutsche Forschungsgemeinschaft (DFG) under project number 251654672. Ulm University and Ludwig-Maximilians-Universität München are participating institutions in the second and third funding period. The Max Planck Institute for Biological Cybernetics in Tübingen in was a participating institution in the first funding period.

The goal of SFB/Transregio 161 is establishing the paradigm of quantitative science in the field of visual computing, which is a long-term endeavour requiring a fundamental research effort broadly covering four research areas, namely quantitative models and measures, adaptive algorithms, interaction and applications. In the third funding period, which started in 2023, new research directions are being approached. One is visual explainability, assessing and quantifying how well the users of a visualisation system understand the phenomena shown visually. The second direction targets mixed reality, covering all forms of augmented and virtual reality as a cross-cutting field of various visual computing subfields, irrespective of applied technology. The third research theme aims to bring research results in the world, moving away from experiments in the laboratory and in the wild to openly accessible applications that provide research results, methods, data sets, and other outcomes from SFB/Transregio 161 to a wide range of stakeholders in academia, industry, teaching, and society in general.

In SFB/Transregio 161, approximately 40 scientists in the fields of computer science, visualisation, computer vision, human computer interaction, linguistics and applied psychology are jointly working on improving the quality of future visual computing methods and applications.

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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 Sof...
Tabular Data - 1.3 MB - 8 Variables, 3000 Observations - UNF:6:MAoeMQ143uppJ/W6lUsOhg==
Tabular Data - 4.3 MB - 8 Variables, 9445 Observations - UNF:6:ERaGx3GYsw/WTGloE0ANNw==
Tabular Data - 951.9 KB - 8 Variables, 2200 Observations - UNF:6:nGvaIRk1SuiVJGs9NrbiTw==
Tabular Data - 4.7 MB - 8 Variables, 10245 Observations - UNF:6:gF0WNHAi8l/kp/Q7ZxVdpA==
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