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1 to 8 of 8 Results
Supplemental materials for "Two Empirical Studies on Audiovisual Semiotics of Uncertainty"
Supplemental materials for "Two Empirical Studies on Audiovisual Semiotics of Uncertainty"
May 9, 2025 - Visualisierungsinstitut der Universität Stuttgart
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...
Supplementary Material for Uncertainty-aware Spectral Visualization
Sep 16, 2024
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...
Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions
Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions
Sep 2, 2024
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.
Replication Data for: Uncertainty-Aware Principal Component Analysis
Dec 7, 2022
Görtler, Jochen; Spinner, Thilo; Weiskopf, Daniel; Deussen, Oliver, 2022, "Replication Data for: Uncertainty-Aware Principal Component Analysis", https://doi.org/10.18419/DARUS-2321, DaRUS, V1
This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionality reduction on datasets consisting of multivariate probability distri...
Supplemental Material for Uncertainty-Aware Multidimensional Scaling
Oct 13, 2022
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 a detailed mathematical derivation for the normal distribution UAMDS...
Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5
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.
Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution
Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution", https://doi.org/10.18419/DARUS-3055, DaRUS, V1
Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supplemental.zip and open index.html in a web browser.
Source Code for Uncertainty-Aware Multidimensional Scaling
Aug 8, 2022
Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/DARUS-2995, DaRUS, V1
This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to project a set of multivariate normal distributions to low-dimensional space...
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