A design study of personal bibliographic data visualization

Tsai Ling Fung, Jia Kai Chou, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper presents a comparative study on personal visualizations of bibliographic data. We consider three designs for egocentric visualization: node-link diagrams, adjacency matrices, and botanical trees to depict one's academic career in terms of his/her publication records. Case studies are conducted to compare the effectiveness of resulting visualizations for conveying particular aspect of a researcher's bibliographic records. Based on our study, we find that node-link diagrams are better at revealing the overall distribution of certain attributes; adjacency matrices can convey more information with less clutter; and botanical trees are visually attractive and provide the best at a glance characterization of the mapped data, but mapping data to tree features must be carefully done to derive expressive visualization.

Original languageEnglish (US)
Title of host publication2016 IEEE Pacific Visualization Symposium, PacificVis 2016 - Proceedings
EditorsChuck Hansen, Ivan Viola, Xiaoru Yuan
PublisherIEEE Computer Society
Pages244-248
Number of pages5
Volume2016-May
ISBN (Electronic)9781509014514
DOIs
StatePublished - May 4 2016
Event9th IEEE Pacific Visualization Symposium, PacificVis 2016 - Taipei, Taiwan, Province of China
Duration: Apr 19 2016Apr 22 2016

Other

Other9th IEEE Pacific Visualization Symposium, PacificVis 2016
CountryTaiwan, Province of China
CityTaipei
Period4/19/164/22/16

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

Fingerprint Dive into the research topics of 'A design study of personal bibliographic data visualization'. Together they form a unique fingerprint.

  • Cite this

    Fung, T. L., Chou, J. K., & Ma, K-L. (2016). A design study of personal bibliographic data visualization. In C. Hansen, I. Viola, & X. Yuan (Eds.), 2016 IEEE Pacific Visualization Symposium, PacificVis 2016 - Proceedings (Vol. 2016-May, pp. 244-248). [7465279] IEEE Computer Society. https://doi.org/10.1109/PACIFICVIS.2016.7465279