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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141 to 150 of 3,690 Results
PNG Image - 3.2 KB - MD5: 1b61443026e1db22917e5c0867ecc308
training stimulus
Tabular Data - 202 B - 1 Variables, 2 Observations - UNF:6:jJdXhs2twUjeiK39RFsBCg==
application/x-msschedule - 8.6 KB - MD5: a36bf9d46731857af2551eed344a4978
Script used to generate sound stimuli. Created in supercollider: https://supercollider.github.io/
Python Source Code - 5.7 KB - MD5: 09a97329bd8c067b7424dbd66e3c57d6
Script used to generate visual stimuli.
Dec 19, 2024 - SFB-TRR 161 INF "Collaboration Infrastructure"
Garkov, Dimitar; Piselli, Tommaso; Di Giacomo, Emilio; Klein, Karsten; Liotta, Giuseppe; Montecchiani, Fabrizio; Schreiber, Falk, 2024, "Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis - Replication data", https://doi.org/10.18419/DARUS-4231, DaRUS, V1
This dataset contains the supplementary materials to our publication "Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis", where we report on a study we conducted. Please refer to publication for more details, also the abstract can be found at the end of this description. The dataset contains: The collection of graphs...
ZIP Archive - 49.2 MB - MD5: 59f0d86abab8398468a0d7c614e85a0f
Please note the ReadME file in the ZIP
ZIP Archive - 39.4 KB - MD5: 53833802fdfa9dbb13956311ee09c3a5
Licensed under CC BY 4.0
ZIP Archive - 2.6 GB - MD5: 32a4d766e25e100ede1230e1e83c20f7
For instructions, please see 1) the ReadME, and 2) the Unity scene "Server", component "Network Server". License is GPL-3.0
ZIP Archive - 78.9 KB - MD5: 121b7d75ca427c8fd83533343415f494
Accuracy, completion time, weighted NASA TLX ratings, demographics and prior experience, and task instance complexity data
ZIP Archive - 37.2 KB - MD5: 6cbf19a57c298d58152f70a1ced0c4dc
License is GPL-3.0
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