SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"

High-dimensional data analysis requires dealing with numerous challenges, such as selecting meaningful dimensions, finding relevant projections, and removing noise. As a result, the extraction of relevant and meaningful information from high-dimensional data is a difficult problem. This project aims at advancing the field of quality-metric-driven data visualisation with the central research question of how to quantify the quality of transformations and mappings of high-dimensional data for visual analytics.

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1 to 10 of 71 Results

models.tar.xz Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy" Compressed Archive - 2.2 GB - MD5: 66da0557ed92a25f06d148d95ea87a2a All the trained models from the experiments. All models can be loaded using the functionality provided by our program available in source-code.zip. The file names indicate the model type and parameters.
preprocessed.tar.xz Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy" Compressed Archive - 24.0 MB - MD5: 598dfe09e99ada611261ad6d51dafb81 This includes all pre-processed datasets from our experiments, including projected datasets, CSV files, and serialised NumPy arrays.
records.tar.xz Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy" Compressed Archive - 828.9 KB - MD5: 8ebc1835aae7854c3973a3a5b05dc7db All records gathered during training are included, such as training and validation loss, as well as the individual loss components.
source-code.zip Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy" ZIP Archive - 22.0 MB - MD5: 87a5dbba03be2d0f7f88a5eb0edc6047 The source code of our approach. It trains neural networks and logs the results.
01_data_raw.csv.zip Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings" ZIP Archive - 199.8 KB - MD5: 40c6c15ed70a3319f57537636ef72871 The raw study data, including for each user and each graph, the user-selected best and worst perspective, characterised by the graph position and rotation and along with the camera (user) position and rotation. Each observation has a unique index that is consitent across the following datasets. There are gaps in these indices as we filtered out the...
02_normalised_perspectives.csv.zip Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings" ZIP Archive - 30.7 KB - MD5: 5c7f24ed16a4fb5a16f1cb0716004c7c For each observation of the raw data, we converted the selected perspective to a view vector on the graph (positioned at [0,0,0] without rotation), making comparison easier.
03_3d_graphs_graphml.zip Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings" ZIP Archive - 128.5 KB - MD5: c95c568f25f00b6da75c6efc52fdcd4b The graphs (with 3D layout) as used in the user study (GraphML format).
04_2d_projections_graphml.zip Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings" ZIP Archive - 5.3 MB - MD5: 3921141fef225b964e15feea89c53185 Contains for each perspective chosen by a user the 2D projection as a GraphML file including the projected 2D node positions.
05_2d_projection_images.zip Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings" ZIP Archive - 2.6 GB - MD5: 6c0dc1fa0ca6cbd5dc9da20ca1dbb091 The 2D projections of all selected perspectives as image (PNG).
06_perspective_distribution_spheres.zip Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings" ZIP Archive - 2.1 MB - MD5: 244dee84ea1532aaecca3dff79301c6c The distribution of selected perspectives (best / worst) visualised as a spherical representation.

models.tar.xz

Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy"

Compressed Archive - 2.2 GB -

All the trained models from the experiments. All models can be loaded using the functionality provided by our program available in source-code.zip. The file names indicate the model type and parameters.

preprocessed.tar.xz

Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy"

Compressed Archive - 24.0 MB -

This includes all pre-processed datasets from our experiments, including projected datasets, CSV files, and serialised NumPy arrays.

records.tar.xz

Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy"

Compressed Archive - 828.9 KB -

All records gathered during training are included, such as training and validation loss, as well as the individual loss components.

source-code.zip

Feb 20, 2026 - Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy"

ZIP Archive - 22.0 MB -

The source code of our approach. It trains neural networks and logs the results.

01_data_raw.csv.zip

Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings"

ZIP Archive - 199.8 KB -

The raw study data, including for each user and each graph, the user-selected best and worst perspective, characterised by the graph position and rotation and along with the camera (user) position and rotation. Each observation has a unique index that is consitent across the following datasets. There are gaps in these indices as we filtered out the...

02_normalised_perspectives.csv.zip

Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings"

ZIP Archive - 30.7 KB -

For each observation of the raw data, we converted the selected perspective to a view vector on the graph (positioned at [0,0,0] without rotation), making comparison easier.

03_3d_graphs_graphml.zip

Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings"

ZIP Archive - 128.5 KB -

The graphs (with 3D layout) as used in the user study (GraphML format).

04_2d_projections_graphml.zip

Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings"

ZIP Archive - 5.3 MB -

Contains for each perspective chosen by a user the 2D projection as a GraphML file including the projected 2D node positions.

05_2d_projection_images.zip

Aug 5, 2025 - Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings"

ZIP Archive - 2.6 GB -

The 2D projections of all selected perspectives as image (PNG).