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A graphical article-level metric for intuitive comparison of large-scale literatures

Author

Listed:
  • Xiaoxi Ling

    (Dalian University of Technology)

  • Yu Liu

    (Dalian University of Technology)

  • Zhen Huang

    (Dalian University of Technology)

  • Parantu K. Shah

    (Harvard School of Public Health and Dana-Farber Cancer Institute)

  • Cheng Li

    (Peking University)

Abstract

With the advances of all research fields, the volume of scientific literature has grown exponentially over the past decades, and the management and exploration of scientific literature is becoming an increasingly complicated task. It calls for a tool that combines scientific impacts and social focuses to visualize relevant papers from a specific research area and time period, and to find important and interesting papers. Therefore, we propose a graphical article-level metric (gALM), which captures the impact and popularity of papers from scientific and social aspects. These two dimensions are combined and visualized graphically as a circular map. The map is divided into sectors of papers belonging to a publication year, and each block represents a paper’s journal citations by block size and readerships in Mendeley by block color. In this graphical way, gALM provides a more intuitive comparison of large-scale literatures. In addition, we also design an online Web server, Science Navigation Map (SNM), which not only visualizes the gALM but provides it with interactive features. Through an interactive visualization map of article-level metrics on scientific impact and social popularity in Mendeley, users can intuitively make a comparison of papers as well as explore and filter important and relevant papers by these metrics. We take the journal PLoS Biology as an example and visualize all the papers published in PLoS Biology during 2003 and 2014 by SNM. From this map, one can easily and intuitively find basic statistics of papers, such as the most cited papers and the most popular papers in Mendeley during a time period. SNM on the journal PLoS Biology is publicly available at http://www.linkscholar.org/plosbiology/ .

Suggested Citation

  • Xiaoxi Ling & Yu Liu & Zhen Huang & Parantu K. Shah & Cheng Li, 2016. "A graphical article-level metric for intuitive comparison of large-scale literatures," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 41-50, January.
  • Handle: RePEc:spr:scient:v:106:y:2016:i:1:d:10.1007_s11192-015-1782-4
    DOI: 10.1007/s11192-015-1782-4
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    References listed on IDEAS

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    1. Ludo Waltman & Rodrigo Costas, 2014. "F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 433-445, March.
    2. Felice Frankel & Rosalind Reid, 2008. "Big data: Distilling meaning from data," Nature, Nature, vol. 455(7209), pages 30-30, September.
    3. Xuemei Li & Mike Thelwall & Dean Giustini, 2012. "Validating online reference managers for scholarly impact measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 461-471, May.
    4. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    5. Haustein, Stefanie & Siebenlist, Tobias, 2011. "Applying social bookmarking data to evaluate journal usage," Journal of Informetrics, Elsevier, vol. 5(3), pages 446-457.
    6. Stefanie Haustein & Isabella Peters & Cassidy R. Sugimoto & Mike Thelwall & Vincent Larivière, 2014. "Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 656-669, April.
    7. Cameron Neylon & Shirley Wu, 2009. "Article-Level Metrics and the Evolution of Scientific Impact," PLOS Biology, Public Library of Science, vol. 7(11), pages 1-6, November.
    8. Koon-Kiu Yan & Mark Gerstein, 2011. "The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-7, May.
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