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Automating bibliometric analyses using Taverna scientific workflows: A tutorial on integrating Web Services

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  • Guler, Arzu Tugce
  • Waaijer, Cathelijn J.F.
  • Mohammed, Yassene
  • Palmblad, Magnus

Abstract

Quantitative analysis of the scientific literature is a frequent task in bibliometrics. Several large online resources collect and disseminate bibliographic information, paving the way for broad analyses and statistics. The Europe PubMed Central (PMC) and its Web Services is one of these resources, providing a rich platform to retrieve information and metadata on scientific publications. However, a complete bibliometric analysis that involves gathering information and deriving statistics on an author, topic, or country is laborious when consuming Web Services on the command-line or using low level automation. In contrast, scientific workflow managers can integrate different types of software tools to automate multi-step processes. The Taverna workflow engine is a popular open-source scientific workflow manager, giving easy access to available Web Services. In this tutorial, we demonstrate how to design scientific workflows for bibliometric analyses in Taverna by integrating Europe PubMed Central Web Services and statistical analysis tools. To our knowledge, this is also the first time scientific workflow managers have been used to perform bibliometric analyses using these Web Services.

Suggested Citation

  • Guler, Arzu Tugce & Waaijer, Cathelijn J.F. & Mohammed, Yassene & Palmblad, Magnus, 2016. "Automating bibliometric analyses using Taverna scientific workflows: A tutorial on integrating Web Services," Journal of Informetrics, Elsevier, vol. 10(3), pages 830-841.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:3:p:830-841
    DOI: 10.1016/j.joi.2016.05.002
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    References listed on IDEAS

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    1. 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.
    2. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    3. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    4. Ruedi Aebersold & Matthias Mann, 2003. "Mass spectrometry-based proteomics," Nature, Nature, vol. 422(6928), pages 198-207, March.
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    1. Shuangqing Sheng & Wei Song & Hua Lian & Lei Ning, 2022. "Review of Urban Land Management Based on Bibliometrics," Land, MDPI, vol. 11(11), pages 1-25, November.
    2. Azira Rahim & Ashraff Ruslan & Siti Rohana Daud & Rozana Othman & Nani Shuhada Sehat & Intan Liana Suhaime & Najihah Abdul Rahim & Jumaelya Jogeran, 2025. "Mapping the Research Landscape on University Students’ Mental Health: A Bibliometric Study," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(3s), pages 6492-6505, August.
    3. Giuseppe Giordano & Maria Carmela Catone & Ilaria Primerano, 2025. "The Fake News Phenomenon in the Scientific Debate: Evidence from a Bibliometric Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 177(1), pages 31-52, March.
    4. Moaaz Kabil & Setiawan Priatmoko & Róbert Magda & Lóránt Dénes Dávid, 2021. "Blue Economy and Coastal Tourism: A Comprehensive Visualization Bibliometric Analysis," Sustainability, MDPI, vol. 13(7), pages 1-25, March.

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