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Scientific workflows for bibliometrics

Author

Listed:
  • Arzu Tugce Guler

    (Leiden University Medical Center)

  • Cathelijn J. F. Waaijer

    (Leiden University)

  • Magnus Palmblad

    (Leiden University Medical Center)

Abstract

Scientific workflows organize the assembly of specialized software into an overall data flow and are particularly well suited for multi-step analyses using different types of software tools. They are also favorable in terms of reusability, as previously designed workflows could be made publicly available through the myExperiment community and then used in other workflows. We here illustrate how scientific workflows and the Taverna workbench in particular can be used in bibliometrics. We discuss the specific capabilities of Taverna that makes this software a powerful tool in this field, such as automated data import via Web services, data extraction from XML by XPaths, and statistical analysis and visualization with R. The support of the latter is particularly relevant, as it allows integration of a number of recently developed R packages specifically for bibliometrics. Examples are used to illustrate the possibilities of Taverna in the fields of bibliometrics and scientometrics.

Suggested Citation

  • Arzu Tugce Guler & Cathelijn J. F. Waaijer & Magnus Palmblad, 2016. "Scientific workflows for bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 385-398, May.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:2:d:10.1007_s11192-016-1885-6
    DOI: 10.1007/s11192-016-1885-6
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    References listed on IDEAS

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    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    3. Gagolewski, Marek, 2011. "Bibliometric impact assessment with R and the CITAN package," Journal of Informetrics, Elsevier, vol. 5(4), pages 678-692.
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