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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...
Mar 1, 2024 - SFB-TRR 161 INF "Collaboration Infrastructure"
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.
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