1 to 10 of 82 Results
Jun 11, 2025 - SFB-TRR 161 INF "Collaboration Infrastructure"
Garkov, Dimitar; Lein, Etienne; Kielkopf, Niklas; Dullin, Christian; Klein, Karsten; Sommer, Bjorn; Jordan, Alex; Schreiber, Falk, 2025, "Software and Data for: Interactive delineation and quantification of anatomical structure with virtual reality", https://doi.org/10.18419/DARUS-4779, DaRUS, V1
This dataset contains the supplemental materials, the used tools, and the release of the software presented in the paper Interactive delineation and quantification of anatomical structure with virtual reality. The dataset is structured in the typical order of data processing: Imaging Tomographic Reconstruction Brainacle Software (Delineation, Quant... |
May 14, 2025 - SFB-TRR 161 B04 "Adaptive Algorithms for Motion Estimation"
Schmalfuss, Jenny; Oei, Victor; Mehl, Lukas; Bartsch, Madlen; Agnihotri, Shashank; Keuper, Margret; Bruhn, Andres, 2025, "RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo", https://doi.org/10.18419/DARUS-5047, DaRUS, V1
The RobustSpring dataset contains the image corruption data files for scene flow, optical flow and stereo estimation with the Spring dataset. Note that this repository contains only the Spring test data files. For easier handling, we organized them into sub-directories by image corruption type: brightness.zip : brightness image corruption contrast.... |
May 14, 2025 - SFB-TRR 161 INF "Collaboration Infrastructure"
Müller, Christoph; Ertl, Thomas, 2024, "Performance Data for the Visualisation of Time-Dependent Particles using DirectStorage", https://doi.org/10.18419/DARUS-4017, DaRUS, V2
Results of a series of performance measurements (frame times) to determine the impact of using the DirectStorage API for rendering time-dependent particle data sets in contrast to using traditional POSIX-style I/O APIs. |
Vriend, Sita; Hägele, David; Weiskopf, Daniel, 2025, "Supplemental materials for "Two Empirical Studies on Audiovisual Semiotics of Uncertainty"", https://doi.org/10.18419/DARUS-4137, DaRUS, V1, UNF:6:Zv5hIuIcj79NjLNQkI3uBw== [fileUNF]
We explored the potential of audiovisual semiotics, the use of audiovisual channels, to enhance users' intuitive perception of uncertainty by conducting two user studies. In the first experiment we assessed the intuitiveness of audio/visual pairs. In the second experiment, we investigated the intuitive audiovisual mappings of uncertainty. These sup... |
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... |
Nov 22, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao, 2024, "SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)", https://doi.org/10.18419/DARUS-3884, DaRUS, V2
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... |
Oct 25, 2024 - SFB-TRR 161 A08 "A Learning-Based Research Methodology for Visualization"
Angerbauer, Katrin; Van Wagoner, Phoenix; Halach, Tim; Vogelsang, Jonas; Hube, Natalie; Smith, Andria Lenae; Keplinger, Ksenia; Sedlmair, Michael, 2024, "Supplemental Material for the Paper: Is it Part of Me? Exploring Experiences of Inclusive Avatar Use For Visible and Invisible Disabilities in Social VR", https://doi.org/10.18419/DARUS-4426, DaRUS, V1
Supplemental Material for the paper titled: " Is it Part of Me? Exploring Experiences of Inclusive Avatar Use For Visible and Invisible Disabilities in Social VR" accepted for presentation at the ASSETS'24 conference. The structure of the folder is as following:
.
└── avatars
|── base avatars # base avatars generated with ReadyPlayer... |
Chiossi, Francesco; Haliburton, Luke; Ou, Changkun; Butz, Andreas; Schmidt, Albrecht, 2024, "Dataset for "Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory"", https://doi.org/10.18419/DARUS-3327, DaRUS, V1, UNF:6:7FzpUkbNmyXLFXVYJ8abKQ== [fileUNF]
Social media platforms use short, highly engaging videos to catch users’ attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions. We conducted a between-subjects experiment (𝑁 = 60) investigating the impact of engaging with TikTok, Twit... |
Sep 16, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Evers, Marina; Weiskopf, Daniel, 2024, "Supplementary Material for Uncertainty-aware Spectral Visualization", https://doi.org/10.18419/DARUS-4447, DaRUS, V1
In this supplemental material, we provide supplemental information (PDF document with derivations of the results presented in the paper and two additional use cases) and the supplementary video for uncertainty-aware spectral analysis. We model an uncertain time series as a multivariate Gaussian process. We propagate the uncertainty and explicitly c... |
Reichmann, Luca; Hägele, David; Weiskopf, Daniel, 2024, "Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions", https://doi.org/10.18419/DARUS-4441, DaRUS, V1, UNF:6:WoQ4MNffz92VcvZ/qCGL5w== [fileUNF]
This dataset contains the supplemental material for "Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions". The contents and usage of this dataset are described in the README.md files. |
