3,581 to 3,590 of 3,690 Results
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. |
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 publication does not provide a source for the underlying data.
[HP14] Sab... |
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 Metadata. The publication does not provide a source for the underlying... |
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 Process Metadata. The publication does not provide a source for the underly... |
Tabular Data - 23.1 KB - 3 Variables, 1012 Observations - UNF:6:UPfzwP4EP7X8E9Uk4KxFbQ==
The second 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 (b) of the publication by Rogers et al. [RWH*16] The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underl... |
Tabular Data - 15.7 KB - 2 Variables, 1021 Observations - UNF:6:E70xYkGzgv0fjFoXL7T9uA==
A categorical dataset reconstructed from Schätzle et al. [SDB*19] describing language change in Icelandic. It is manually reconstructed from the Parallel Sets visualization in Figure 3 of the publication. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data.
[SDB*19] Chri... |
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 21, 2022 -
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
ZIP Archive - 4.6 MB -
MD5: 4f82bd8d7e56649dc799dc0153f35189
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Jun 21, 2022 -
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
ZIP Archive - 3.7 MB -
MD5: 0302e172f0cdb8cef6a84f4ecfb31caf
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