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Normalizing Google Scholar data for use in research evaluation.

John Mingers1, Martin Meyer1

  • 1Kent Business School, University of Kent, Canterbury, UK.

Scientometrics
|August 8, 2017
PubMed
Summary
This summary is machine-generated.

Bibliometric evaluations using Google Scholar (GS) data are possible with normalization, though manual effort is currently needed. This method shows high validity when compared to Web of Science (WoS) data for journal papers.

Keywords:
Google ScholarNormalizationResearch evaluation

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Area of Science:

  • Bibliometrics and scientometrics
  • Information science
  • Research evaluation

Background:

  • Bibliometric data is increasingly used for research evaluation.
  • Traditional databases like Web of Science (WoS) and Scopus have limited coverage in social sciences and humanities.
  • Google Scholar (GS) offers broader coverage but has data reliability and tool limitations.

Purpose of the Study:

  • To test a Google Scholar (GS) normalization method for bibliometric data.
  • To assess the feasibility of normalizing GS data across different publication types (journal papers, book chapters, conference papers).
  • To compare normalized GS data with established databases like WoS.

Main Methods:

  • Application of a GS normalization method developed by Bornmann et al. (2016).
  • Utilizing a dataset including journal papers, book chapters, and conference papers.
  • Manual data generation and validation for GS data.

Main Results:

  • Google Scholar (GS) data normalization is feasible.
  • The process currently requires significant manual data handling.
  • Normalized GS journal paper data demonstrated high convergent validity when compared to WoS data.

Conclusions:

  • Normalization of Google Scholar (GS) data for bibliometric evaluation is achievable.
  • Further development is needed to reduce manual involvement in data processing.
  • The tested method shows promise for more comprehensive research evaluations, especially in underrepresented fields.