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Visualization of co-readership patterns from an online reference management system

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  • Kraker, Peter
  • Schlögl, Christian
  • Jack, Kris
  • Lindstaedt, Stefanie

Abstract

In this paper, we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating knowledge domain visualizations. First, we investigate the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we use co-readership patterns to map the field of educational technology. The resulting visualization prototype, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.

Suggested Citation

  • Kraker, Peter & Schlögl, Christian & Jack, Kris & Lindstaedt, Stefanie, 2015. "Visualization of co-readership patterns from an online reference management system," Journal of Informetrics, Elsevier, vol. 9(1), pages 169-182.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:1:p:169-182
    DOI: 10.1016/j.joi.2014.12.003
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    1. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Luis Bettencourt & Ryan Chute & Marko A Rodriguez & Lyudmila Balakireva, 2009. "Clickstream Data Yields High-Resolution Maps of Science," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-11, March.
    2. Johan Bollen & Herbert van de Sompel, 2006. "Mapping the structure of science through usage," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 227-258, November.
    3. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    4. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    5. Michael J. Kurtz & Guenther Eichhorn & Alberto Accomazzi & Carolyn Grant & Markus Demleitner & Stephen S. Murray & Nathalie Martimbeau & Barbara Elwell, 2005. "The bibliometric properties of article readership information," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(2), pages 111-128, January.
    6. Haustein, Stefanie & Siebenlist, Tobias, 2011. "Applying social bookmarking data to evaluate journal usage," Journal of Informetrics, Elsevier, vol. 5(3), pages 446-457.
    7. Xavier Polanco & Roche Ivana & Besagni Dominique, 2006. "User science indicators in the Web context and co-usage analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 66(1), pages 171-182, January.
    8. Goslee, Sarah C. & Urban, Dean L., 2007. "The ecodist Package for Dissimilarity-based Analysis of Ecological Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 22(i07).
    9. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    10. Leo Egghe, 2010. "Good properties of similarity measures and their complementarity," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(10), pages 2151-2160, October.
    11. Liang-Chu Chen & Yen-Hsuan Lien, 2011. "Using author co-citation analysis to examine the intellectual structure of e-learning: A MIS perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 867-886, December.
    12. Leo Egghe, 2010. "Good properties of similarity measures and their complementarity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(10), pages 2151-2160, October.
    13. Henry Small, 1999. "Visualizing science by citation mapping," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(9), pages 799-813.
    14. Christian Schloegl & Juan Gorraiz, 2010. "Comparison of citation and usage indicators: the case of oncology journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 567-580, March.
    15. Ming-yueh Tsay & Hong Xu & Chia-wen Wu, 2003. "Journal co-citation analysis of semiconductor literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 57(1), pages 7-25, May.
    16. Per Ahlgren & Bo Jarneving & Ronald Rousseau, 2003. "Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(6), pages 550-560, April.
    17. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    18. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    19. Katherine W. McCain, 1990. "Mapping authors in intellectual space: A technical overview," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 433-443, September.
    20. World Bank, 2014. "World Development Indicators 2014," World Bank Publications - Books, The World Bank Group, number 18237.
    21. Tim Brody & Stevan Harnad & Leslie Carr, 2006. "Earlier Web usage statistics as predictors of later citation impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1060-1072, June.
    22. Howard D. White & Katherine W. McCain, 1998. "Visualizing a discipline: An author co‐citation analysis of information science, 1972–1995," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(4), pages 327-355.
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    1. Mojisola Erdt & Aarthy Nagarajan & Sei-Ching Joanna Sin & Yin-Leng Theng, 2016. "Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1117-1166, November.
    2. Mehrbakhsh Nilashi & Othman Ibrahim & Shamila Sohaei & Hossein Ahmadi & Alireza Almaee, 2016. "Features Influencing Researchers’ Selection of Reference Management Software," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-23, September.

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