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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PNG Image - 412.3 KB - MD5: a07dc87a638e9cfe4ff1875247676e35
Screenshot of https://observablehq.com/@robsutcliffe/dirty-planet/2
Comma Separated Values - 21.4 MB - MD5: 4dfe84d8d490647211c528638e5b0287
Pivot table holding averaged power readings for the sphere rendering application case. The time period covered is the respective wall-clock time in each row.
Comma Separated Values - 16.2 MB - MD5: d1f1af8e94ad4a6e8fdff03c52a9581b
Pivot table holding averaged power readings for the volume rendering application case. The time period covered is the respective wall-clock time in each row.
Aug 8, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/DARUS-2995, DaRUS, V1
This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to project a set of multivariate normal distributions to low-dimensional space...
application/java-archive - 1.6 MB - MD5: 27646e42ce06d907e9be697f4fe4c3ad
All-in-one Java library .jar file. Contains compiled classes, their .java sources, and dependency libraries.
Gzip Archive - 27.2 KB - MD5: 90d4d720ac5024af8fab2b95c82a65df
Tarball archive containing the Java project with build and readme files.
Jul 11, 2022 - SFB-TRR 161 A08 "A Learning-Based Research Methodology for Visualization"
Angerbauer, Katrin; Rodrigues, Nils; Cutura, Rene; Öney, Seyda; Pathmanathan, Nelusa; Morariu, Cristina; Weiskopf, Daniel; Sedlmair, Michael, 2022, "Supplemental Material for the paper : Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures", https://doi.org/10.18419/DARUS-2608, DaRUS, V1, UNF:6:uNArOgq9AXAmdvLcE/mMVA== [fileUNF]
Study data and supplemental material for the paper- Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures presented at CHI 2022. We performed a large scale data study on the color vision deficiency (CVDs) accessibility of paper figures, considering four CVDs. As images for our study we used the...
Tabular Data - 193.7 KB - 3 Variables, 6000 Observations - UNF:6:XR2sKRAbJBoypox+9RN28Q==
Data
3 tuple for neutralization effect
Tabular Data - 1.1 KB - 6 Variables, 21 Observations - UNF:6:Bi+4s/uQzUqFKEofn7029A==
Data
aggregated accessibility data by image type
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