1 to 10 of 52 Results
Jan 26, 2024 -
The Categorical Data Map - Replication Data
ZIP Archive - 107.1 MB -
MD5: d58f479bdc92b926921f7ef5e7429246
Code of the research prototype for "The Categorical Data Map"
All required source code and data are packaged in `categorical-data-map.zip` and ready for development and deployment.
Software Dependencies: The code in this component has been tested with *Docker version 24.0.5, bu... |
Sep 27, 2022 -
Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"
Tabular Data - 9.0 KB - 6 Variables, 100 Observations - UNF:6:B6Qnhv0vjfsgXsgj4Cw3sA==
This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong... |
Sep 27, 2022 -
Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"
Tabular Data - 18.0 KB - 6 Variables, 200 Observations - UNF:6:m3UG8RKHop20WoVRX86OwQ==
This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong... |
Sep 27, 2022 -
Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"
Tabular Data - 36.0 KB - 6 Variables, 400 Observations - UNF:6:4ssZpvmFWXvwIwNYAEczCQ==
This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong... |
Sep 27, 2022 -
Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"
Tabular Data - 71.9 KB - 6 Variables, 800 Observations - UNF:6:S58mxxJ0M5mXhtwEBd4wNQ==
This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong... |
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. |
Jan 26, 2024 -
The Categorical Data Map - Replication Data
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. |
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... |
Jan 26, 2024 -
The Categorical Data Map - Replication Data
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... |
Jan 26, 2024 -
The Categorical Data Map - Replication Data
MPEG-4 Video - 67.7 MB -
MD5: 4534ed233b64581fcd5a27fed32bd228
An instructional video demonstrating the prototype. |