Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases

By Michael Gusenbauer. Scientometrics, 2018

641 citations recorded by the source corpus.

Information on the size of academic search engines and bibliographic databases (ASEBDs) is often outdated or entirely unavailable. Hence, it is difficult to assess the scope of specific databases, such as Google Scholar. While scientometric studies have estimated ASEBD sizes before, the methods employed were able to compare only a few databases. Consequently, there is no up-to-date comparative information on the sizes of popular ASEBDs. This study aims to fill this blind spot by providing a comparative picture of 12 of the most commonly used ASEBDs. In doing so, we build on and refine previous scientometric research by counting query hit data as an indicator of the number of accessible records. Iterative query optimization makes it possible to identify a maximum number of hits for most ASEBDs. The results were validated in terms of their capacity to assess database size by comparing them with official information on database sizes or previous scientometric studies. The queries used here are replicable, so size information can be updated quickly. The findings provide first-time size estimates of ProQuest and EbscoHost and indicate that Google Scholar’s size might have been underestimated so far by more than 50%. By our estimation Google Scholar, with 389 million records, is currently the most comprehensive academic search engine.

Subfields: Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ff5f86f2e54c356cec4b4004398f501776c29ffb

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Citations in the corpus

Gamification in Science Education. A Systematic Review of the Literature (2021, background): “Google Scholar was also selected due to currently be the most comprehensive academic search engine [98] and to include articles published outside educational journals that provide relevant information [99].”
Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources (2020, methodology): “37 In our analysis, we did not assess the quality of the retrieved records, in terms of their fit with a given search intent for example.”
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, methodology): “The search terms were used to search Google Scholar (currently the most comprehensive academic search engine according to recent studies [95,96]) on 24 July 2020 and the number of papers returned by each search are shown in brackets after the search terms.”
Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today's Academic World (2021, background): “Gusenbauer [23,42] and in several subject specific studies [27,43,83,84].”
Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations (2020, neutral): “2019; Gusenbauer 2018).”
NRC Publications Archive Archives des publications du CNRC (neutral): “However, scientometric researchers have estimated that it included about 389 million documents in January 2018 (Gusenbauer, 2019)—making it the world’s largest source of academic information.”
Sample Selection in Systematic Literature Reviews of Management Research (2021, background): “…Google Scholar would be an attractive complement to other multipublisher databases (Gusenbauer & Haddaway, 2020) because Google Scholar currently covers more nonjournal content than the other multipublisher databases named in Table 4 (Bar-Ilan, 2010; Gusenbauer, 2019; Harzing & Alakangas, 2016).”
A Review on Fairness in Machine Learning (2022, background): “Moreover, a recent comparative research [92] assessed that Google Scholar’s size might have been underestimated previously and is currently the most comprehensive academic search engine.”