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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3,581 to 3,590 of 3,690 Results
Jun 30, 2022 - SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"
Dennig, Frederik L.; Fischer, Maximilian T.; Blumenschein, Michael; Fuchs, Johannes; Keim, Daniel; Dimara, Evanthia, 2022, "Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"", https://doi.org/10.18419/DARUS-2869, DaRUS, V1, UNF:6:mmqXqGYXSM0L6g/xQCjGUg== [fileUNF]
This is the replication data for our research article "ParSetgnostics: Quality Metrics for Parallel Sets." It contains the datasets used to obtain optimized Parallel Sets visualizations. We used the following six datasets for our experiments, which we describe on a per-file basis. All datasets are purely categorical datasets.
Tabular Data - 81.1 KB - 4 Variables, 2201 Observations - UNF:6:n0hJE5dgUmOgW4TXbPH6jw==
The well-known titanic dataset from Dawson R. J. M. (1995) [Daw95]. [Daw95] Dawson R. J. M.:The "unusual episode" data revisited, 1995. http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18.
Tabular Data - 33.3 KB - 4 Variables, 647 Observations - UNF:6:452w0FICojAEWwAMO7rPdQ==
A categorical dataset reconstructed from Hassan et al. [HP14] describing data storage security and cost data. It is manually reconstructed from the Parallel Sets visualization in Figure 2 of the publication. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data. [HP14] Sab...
Tabular Data - 44.1 KB - 3 Variables, 1077 Observations - UNF:6:jm91tihAXgNNRcyGYtStlA==
A categorical dataset reconstructed from Koh et al. [KSDK11] describing property sales information from Singapor. It is manually reconstructed from the Parallel Sets visualization in Figure 4 of the publication by Koh et al. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying...
Tabular Data - 23.8 KB - 3 Variables, 1013 Observations - UNF:6:feip2wKBPgQTWR5JZ3oK8g==
The first categorical dataset reconstructed from Rogers et al. describing the results of a HCI study. It is manually reconstructed from the Parallel Sets visualization in Figure 1 (a) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underly...
Tabular Data - 23.1 KB - 3 Variables, 1012 Observations - UNF:6:UPfzwP4EP7X8E9Uk4KxFbQ==
The second categorical dataset reconstructed from Rogers et al. describing the results of a HCI study. It is manually reconstructed from the Parallel Sets visualization in Figure 1 (b) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underl...
Tabular Data - 15.7 KB - 2 Variables, 1021 Observations - UNF:6:E70xYkGzgv0fjFoXL7T9uA==
A categorical dataset reconstructed from Schätzle et al. [SDB*19] describing language change in Icelandic. It is manually reconstructed from the Parallel Sets visualization in Figure 3 of the publication. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data. [SDB*19] Chri...
Jun 21, 2022 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2022, "Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"", https://doi.org/10.18419/DARUS-2826, DaRUS, V1, UNF:6:AuvgRc09o1rESd63AqlW9Q== [fileUNF]
Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. We propose a question-answering paradigm to study visualisation recallability and present VisRecall -- a novel dataset consisting of 200 information visual...
ZIP Archive - 4.6 MB - MD5: 4f82bd8d7e56649dc799dc0153f35189
ZIP Archive - 3.7 MB - MD5: 0302e172f0cdb8cef6a84f4ecfb31caf
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