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Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization

Author

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  • Cody Dunne
  • Ben Shneiderman
  • Robert Gove
  • Judith Klavans
  • Bonnie Dorr

Abstract

Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer (ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation text extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. A three‐phase usability study guided our revisions to ASE and led us to improve the testing methods.

Suggested Citation

  • Cody Dunne & Ben Shneiderman & Robert Gove & Judith Klavans & Bonnie Dorr, 2012. "Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2351-2369, December.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:12:p:2351-2369
    DOI: 10.1002/asi.22652
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    Citations

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    Cited by:

    1. Holly N. Wolcott & Matthew J. Fouch & Elizabeth R. Hsu & Leo G. DiJoseph & Catherine A. Bernaciak & James G. Corrigan & Duane E. Williams, 2016. "Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 807-817, May.
    2. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    3. Marcel Dunaiski & Gillian J. Greene & Bernd Fischer, 2017. "Exploratory search of academic publication and citation data using interactive tag cloud visualizations," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1539-1571, March.
    4. Qing Ping & Chaomei Chen, 2018. "LitStoryTeller+: an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1887-1944, September.

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