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.

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

41 to 50 of 52 Results

uniform-400.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 57.0 KB - 9 Variables, 400 Observations - UNF:6:YDkCJkbQEHrZ8rtkXyHDLQ== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 400 records.
uniform-800.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 114.0 KB - 9 Variables, 800 Observations - UNF:6:Xckxodnqrq7XKB7pkEERDQ== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 800 records.
variance-100.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 13.5 KB - 6 Variables, 150 Observations - UNF:6:4FduUnJwRNYGTwDWaUbWVw== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 100 records.
variance-200.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 22.5 KB - 6 Variables, 250 Observations - UNF:6:UEtK771CeC5HA0oYlouNfw== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 200 records.
variance-400.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 40.4 KB - 6 Variables, 450 Observations - UNF:6:9HyN/2jlcVdPuuq52hIwpQ== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 400 records.
variance-800.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 76.4 KB - 6 Variables, 850 Observations - UNF:6:dHHpAC3qiIpFizO+EWIYiA== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 800 records.
dawson.tab Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets" Tabular Data - 81.1 KB - 4 Variables, 2201 Observations - UNF:6:n0hJE5dgUmOgW4TXbPH6jw== The well-known titanic dataset from Dawson R. J. M. (1995) [Daw95]. [Daw95] Dawson R. J. M.:The "unusual episode" data revisited, 1995. http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18.
hassan.tab Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets" Tabular Data - 33.3 KB - 4 Variables, 647 Observations - UNF:6:452w0FICojAEWwAMO7rPdQ== A categorical dataset reconstructed from Hassan et al. [HP14] describing data storage security and cost data. It is manually reconstructed from the Parallel Sets visualization in Figure 2 of the publication. The reconstruction method is described in the Process Metadata. The publ...
koh.tab Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets" Tabular Data - 44.1 KB - 3 Variables, 1077 Observations - UNF:6:jm91tihAXgNNRcyGYtStlA== A categorical dataset reconstructed from Koh et al. [KSDK11] describing property sales information from Singapor. It is manually reconstructed from the Parallel Sets visualization in Figure 4 of the publication by Koh et al. The reconstruction method is described in the Process M...
rogers-1.tab Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets" Tabular Data - 23.8 KB - 3 Variables, 1013 Observations - UNF:6:feip2wKBPgQTWR5JZ3oK8g== The first categorical dataset reconstructed from Rogers et al. describing the results of a HCI study. It is manually reconstructed from the Parallel Sets visualization in Figure 1 (a) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Proce...

uniform-400.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 57.0 KB - 9 Variables, 400 Observations -

This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 400 records.

uniform-800.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 114.0 KB - 9 Variables, 800 Observations -

This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 800 records.

variance-100.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 13.5 KB - 6 Variables, 150 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 100 records.

variance-200.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 22.5 KB - 6 Variables, 250 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 200 records.

variance-400.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 40.4 KB - 6 Variables, 450 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 400 records.

variance-800.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 76.4 KB - 6 Variables, 850 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 800 records.

dawson.tab

Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"

Tabular Data - 81.1 KB - 4 Variables, 2201 Observations -

The well-known titanic dataset from Dawson R. J. M. (1995) [Daw95]. [Daw95] Dawson R. J. M.:The "unusual episode" data revisited, 1995. http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18.

hassan.tab

Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"

Tabular Data - 33.3 KB - 4 Variables, 647 Observations -

A categorical dataset reconstructed from Hassan et al. [HP14] describing data storage security and cost data. It is manually reconstructed from the Parallel Sets visualization in Figure 2 of the publication. The reconstruction method is described in the Process Metadata. The publ...

koh.tab

Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"

Tabular Data - 44.1 KB - 3 Variables, 1077 Observations -

A categorical dataset reconstructed from Koh et al. [KSDK11] describing property sales information from Singapor. It is manually reconstructed from the Parallel Sets visualization in Figure 4 of the publication by Koh et al. The reconstruction method is described in the Process M...

rogers-1.tab

Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"

Tabular Data - 23.8 KB - 3 Variables, 1013 Observations -

The first categorical dataset reconstructed from Rogers et al. describing the results of a HCI study. It is manually reconstructed from the Parallel Sets visualization in Figure 1 (a) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Proce...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications