51 to 60 of 82 Results
Sep 29, 2022 - SFB-TRR 161 INF "Collaboration Infrastructure"
Garkov, Dimitar; Müller, Christoph; Braun, Matthias; Weiskopf, Daniel; Schreiber, Falk, 2022, ""Research Data Curation in Visualization : Position Paper" (Data)", https://doi.org/10.18419/DARUS-3144, DaRUS, V1, UNF:6:yUhRXAoSoLD387EnHtthFg== [fileUNF]
Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inc... |
Sep 23, 2022 - SFB-TRR 161 A05 "Image/Video Quality Assessment: From Test Databases to Similarity-Aware and Perceptual Dynamic Metrics"
Hosu, Vlad; Lin, Hanhe; Szirányi, Tamas; Saupe, Dietmar, 2022, "KonIQ-10k IQA Database", https://doi.org/10.18419/DARUS-2435, DaRUS, V1, UNF:6:2Pw1H0RwWd4G4kLDsT/bjA== [fileUNF]
KonIQ-10k is, at the time of publication, the largest IQA dataset to date consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 milli... |
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. |
Mehl, Lukas; Beschle, Cedric; Barth, Andrea; Bruhn, Andrés, 2022, "Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation", https://doi.org/10.18419/DARUS-2890, DaRUS, V1
Results of our proposed optical flow method on the Sintel and KITTI datasets. We provide the benchmark results before and after applying our refinement approach. Additionally, we provide a supplementary material to our paper with more details on the minimisation, the numerical solution and additional results. The data is structured as follows: The... |
Aug 24, 2022 - SFB-TRR 161 A02 "Quantifying Visual Computing Systems"
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 : 0.01 0.1 500,000 spheres placed randomly, but usinga fixed seed, in... |
Aug 8, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
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... |
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 paper figures, considering four CVDs. As images for our study we used the... |
Jun 30, 2022 - SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"
Dennig, Frederik L.; Fischer, Maximilian T.; Blumenschein, Michael; Fuchs, Johannes; Keim, Daniel; Dimara, Evanthia, 2022, "Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"", https://doi.org/10.18419/DARUS-2869, DaRUS, V1, UNF:6:mmqXqGYXSM0L6g/xQCjGUg== [fileUNF]
This is the replication data for our research article "ParSetgnostics: Quality Metrics for Parallel Sets." It contains the datasets used to obtain optimized Parallel Sets visualizations. We used the following six datasets for our experiments, which we describe on a per-file basis. All datasets are purely categorical datasets. |
Wang, Yao; Bulling, Andreas, 2022, "Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"", https://doi.org/10.18419/DARUS-2826, DaRUS, V1, UNF:6:AuvgRc09o1rESd63AqlW9Q== [fileUNF]
Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. We propose a question-answering paradigm to study visualisation recallability and present VisRecall -- a novel dataset consisting of 200 information visual... |
Jun 9, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Yan, Jia Jun; Rodrigues, Nils; Shao, Lin; Schreck, Tobias; Weiskopf, Daniel, 2022, "Sample Implementation for the Paper "Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection"", https://doi.org/10.18419/DARUS-2810, DaRUS, V1
Java source code of proof-of-concept tool used for the paper "Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection". Code by M.Sc. student Jia Jun Yan. Supervision and concepts by Nils Rodrigues, Lin Shao, Tobias Schreck, and Daniel Weiskopf. |
