## Nominal datasets for evaluation of "The Categorical Data Map" This component contains all datasets used in our prototype of the Categorical Data Map. Some of these datasets have been reconstructed from the data visualizations in the publications. --- ### Mushroom Dataset (lincoff.csv) A mushroom dataset with 23 attributes and 8124 category combinations. G. Lincoff and N. A. Society: **National Audubon Society field guide to North American mushrooms**, ser. Audubon Society field guide series. Knopf: Distributed by Random House New York, 1981. --- ### Titanic Dataset (dawson.csv) The well-known titanic dataset from Dawson R. J. M. (1995). R. J. M. Dawson: **The "unusual episode" data revisited**. 1995, http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18. --- ### Secure Storage (hassan.csv) #### Source Sabri Hassan and Günther Pernul: **Efficiently Managing the Security and Costs of Big Data Storage using Visual Analytics**. In *16th International Conference on Information Integration and Web-based Applications & Services*, pp. 180-184, 2014. doi: 10.1145/2684200.2684333. #### Dimension - Value - Sensitivity - Region - Costs #### Item Frequencies Frequency | Tuple ----------|---------------------------------------------- 43 | Non Critical, Unknown, EU (Ireland), Moderate 96 | Non Critical, Low, EU (Ireland), Moderate 64 | Unknown, Unknown, South America (Sao Paulo), Low 20 | Unknown, Unknown, Asia Pacific (Sydney), High 12 | Unknown, Unknown, US West (Northern California), High 8 | Critical, Unknown, Asia Pacific (Sydney), High 98 | Critical, Unknown, US West (Northern California), High 115 | Critical, High, Asia Pacific (Sydney), High 51 | Critical, High, US West (Northern California), High 4 | Critical, Unknown, Asia Pacific (Tokyo), Very High 9 | Critical, Low, Asia Pacific (Tokyo), Very High 127 | Critical, High, Asia Pacific (Tokyo), Very High --- ### Property Sales (koh.csv) #### Source Lian Chee Koh, Aidan Slingsby, Jason Dykes, and Tin Seong Kam: **Developing and Applying a User-Centered Model for the Design and Implementation of Information Visualization Tools**. In *15th International Conference on Information Visualisation*, pp. 90-95, 2011. doi: 10.1109/IV.2011.32 #### Dimensions - Purchaser Currently Living In - Property Type Purchased - Location of Purchased Property --- ### HCI Study (rogers-1.csv and rogers-2.csv) #### Source Kristopher Rogers, Janet Wiles, Scott Heath, Kristyn Hensby, and Jonathon Taufatofua: **Discovering Patterns of Touch: A Case Study for Visualization-Driven Analysis in Human-Robot Interaction**. In: *11th ACM/IEEE International Conference on Human Robot Interaction*, pp. 499-500, 2016, doi: 10.1109/HRI.2016.7451825. #### Dimensions - Participant - Origin - Touch Location --- ### Software Dependency Analysis (yano.csv) #### Source Yuki Yano, Raula Gaikovina Kula, Takashi Ishio, and Katsuro Inoue: **VerXCombo: an interactive data visualization of popular library version combinations**. In *23rd International Conference on Program Comprehension*, pp. 291-294, 2015, doi: 10.1109/ICPC.2015.43. #### Dimensions - common-collections - commons-httpclient - joda-time