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Visualizing a field of research: A methodology of systematic scientometric reviews

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  • Chaomei Chen
  • Min Song

Abstract

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

Suggested Citation

  • Chaomei Chen & Min Song, 2019. "Visualizing a field of research: A methodology of systematic scientometric reviews," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
  • Handle: RePEc:plo:pone00:0223994
    DOI: 10.1371/journal.pone.0223994
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    References listed on IDEAS

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    6. Chaomei Chen & Loet Leydesdorff, 2014. "Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(2), pages 334-351, February.
    7. Richard Klavans & Kevin W. Boyack, 2011. "Using global mapping to create more accurate document‐level maps of research fields," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 1-18, January.
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    10. Michael D. Gordon & Robert K. Lindsay, 1996. "Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(2), pages 116-128, February.
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    Cited by:

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    2. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    3. Basile, Vincenzo & Sorooshian, Shahryar & Pizzichini, Lucia, 2024. "A scientometrics-based journal Management framework: A strategic move," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).

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