1 to 10 of 474 Results
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. |
Aug 4, 2023 - Publication: Microfluidic experiments
Karadimitriou, Nikolaos; Lee, Dongwon; Steeb, Holger, 2023, "Visual characterization of displacement processes in porous media", https://doi.org/10.18419/darus-3615, DaRUS, V1
This dataset correlates to the submitted article to IEEE VIS 2023, entitled “Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs”, by Straub et al. 2023. More specifically, this data set is the one used to create the graphs shown in all Fig... |
Jul 25, 2023 - PN 6-4
Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/darus-3589, DaRUS, V1
Source code of our visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Instructions on how to use our system can be found in the README. |
Apr 8, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)", https://doi.org/10.18419/darus-3138, DaRUS, V1, UNF:6:NwphGtoYrBQqd2TyRh0OHA== [fileUNF]
This dataset contains stimuli and collected participant data of VisRecall++. The structure of the dataset is described in the README-File. Further, if you are interested in related codes of the publication, you can find a copy of the code repository (see Metadata for Research Sof... |
Apr 27, 2022 - SFB-TRR 161 C06 "User-Adaptive Mixed Reality"
Chiossi, Francesco; Welsch, Robin; Villa, Steeven; Chuang, Lewis; Mayer, Sven, 2022, "Virtual Reality Adaptation using Electrodermal Activity to Support User Experience", https://doi.org/10.18419/darus-2820, DaRUS, V1
We report an experiment (N=18) where participants where engaged in a dual task setting in a Social VR (Virtual Reality) scenario. We present a physiologically-adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We i... |
Feb 2, 2023 - SusI Final Workshop
Gläser, Dennis; Seeland, Anett; Schulze, Katharina; Burbulla, Samuel, 2023, "Verification benchmarks for single-phase flow in three-dimensional fractured porous media: DuMuX source code", https://doi.org/10.18419/darus-3228, DaRUS, V1
This dataset contains the source code for simulating the benchmark cases of Berre et al. (2021) with the open-source simulator DuMuX. The benchmarks focus on flow and transport through fractured porous media, considering fracture networks of varying complexity. The code in this d... |
Nov 3, 2022 - SliMoReK Dataverse
Hinze, Christoph; Zeh, Lukas, 2022, "Validation data for comparison of SMC-PI controllers vs. P-PI controller", https://doi.org/10.18419/darus-3152, DaRUS, V1
Experimental comparison for sliding mode position controllers vs. P position controller. Please refer to Readme.md contained in the dataset for more detailed information. SMC-like controllers are: Quasi SMC qSMC with sign(s)~s/(|s| + \epsilon), k_s = 1250, epsilon=5, varying gain... |
Saini, Harnoor, 2022, "User-material for: "A biophysically guided constitutive law of the musculotendon-complex: modelling and numerical implementation in Abaqus"", https://doi.org/10.18419/darus-2229, DaRUS, V1
Background and Objective: Many biomedical, clinical, and industrial applications may benefit from musculoskeletal simulations. Three-dimensional macroscopic muscle models (3D models) can more accurately represent muscle architecture than their 1D (line-segment) counterparts. Neve... |
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... |
Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification"
Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5", https://doi.org/10.18419/darus-3154, DaRUS, V1
Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization. |
