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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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    2. 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.

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