Project Name: SFB/Transregio 161
Funding Information Identifier: 251654672
Author Affiliation: Universität Stuttgart
License: CC BY 4.0
1 to 10 of 13 Results
May 29, 2024 - Visualisierungsinstitut der Universität Stuttgart
Koch, Maurice; Kurzhals, Kuno; Ngo, Quynh Quang; Weiskopf, Daniel, 2024, "Dataset for NMF-based Analysis of Mobile Eye-Tracking Data", https://doi.org/10.18419/darus-4023, DaRUS, V1, UNF:6:Ejbvxe7W2i/4uT/GMXGVbQ== [fileUNF]
This mobile eye-tracking dataset consists of 27 recordings of three participants (all authors) walking through a small art gallery. Participants were instructed to attend individual paintings in specific orders, resulting in five distinct scanpath patterns. The recordings' durati... |
May 16, 2024 - Visualisierungsinstitut der Universität Stuttgart
Koch, Maurice; Pathmanathan, Nelusa; Weiskopf, Daniel; Kurzhals, Kuno, 2024, "Dataset for "How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis"", https://doi.org/10.18419/darus-4141, DaRUS, V1, UNF:6:sGarZdQENrgDcSTnOSbSeA== [fileUNF]
This dataset was recorded in an AR environment comprised of three physical and three virtual scene objects. Four participants were instructed to gaze at the six objects from different depth levels (50cm, 150cm, 300cm) in two orders (left-to-right, right-to-left). There are seven... |
Mar 13, 2024 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Krake, Tim; Klötzl, Daniel; Hägele, David; Weiskopf, Daniel, 2024, "Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess - Supplemental Material", https://doi.org/10.18419/darus-3845, DaRUS, V1
In this supplemental material, we provide the appendix (mathematically exact propagation of uncertainty) and the video material for uncertainty-aware seasonal-trend decomposition based on loess (UASTL). This material complements the main document: The paper on Uncertainty-Aware S... |
Becher, Michael; Müller, Christoph; Reina, Guido; Weiskopf, Daniel; Ertl, Thomas, 2024, "Your visualisations are going places: Performance data for scientific visualisation on gaming consoles", https://doi.org/10.18419/darus-4003, DaRUS, V1
The data set contains performance data (mainly frame times) for rendering spherical glyphs and scalar fields on Xbox Series consoles, mobile game consoles and a reference PC with different GPUs. |
Feb 26, 2024 - 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, V1
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. |
Aug 16, 2023 - SFB-TRR 161 B04 "Adaptive Algorithms for Motion Estimation"
Schmalfuss, Jenny; Mehl, Lukas; Bruhn, Andrés, 2023, "Distracting Downpour - Adversarial Weather Attacks for Motion Estimation (Replication Data)", https://doi.org/10.18419/darus-3677, DaRUS, V1
This dataset contains the generated weather configurations as png and npz files. |
Nov 9, 2022 - Visualisierungsinstitut der Universität Stuttgart
Krake, Tim; Klötzl, Daniel; Eberhardt, Bernhard; Weiskopf, Daniel, 2022, "Constrained Dynamic Mode Decomposition - Supplemental Material", https://doi.org/10.18419/darus-3107, DaRUS, V1
In this supplemental material, we provide additional application results of constrained Dynamic Mode Decomposition and a parameter study for the delay parameter d that complement the evaluation in the main document: The paper on Constrained Dynamic Mode Decomposition published wi... |
Oct 13, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/darus-3104, DaRUS, V1
This dataset contains the supplemental material for "Uncertainty-Aware Multidimensional Scaling". Uncertainty-aware multidimensional scaling (UAMDS) is a nonlinear dimensionality reduction technique for sets of random vectors. This dataset consists of a PDF document that contains... |
Müller, Christoph; Heinemann, Moritz; Weiskopf, Daniel; Ertl, Thomas, 2022, "Energy consumption of scientific visualisation and data visualisation algorithms", https://doi.org/10.18419/darus-3044, DaRUS, V1, UNF:6:dEyIoAgP890tWqA/WShryw== [fileUNF]
This data set comprises a series of measurements of GPU power consumption when raycasting spherical glyphs, raycasting scalar fields and when showing web-based data visualisation on Observable HQ. The data sets for sphere rendering were: pos_rad_intensity : 500000 : 0 : 10 10 10... |
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 pap... |
