Abstract
Information on the size of academic search engines and bibliographic databases (ASEBDs) is often outdated or entirely unavailable.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.
Highlights
Academic search engines and bibliographic databases (ASEBDs) are the standard place from which to access up-to-date scientific publications
We found that query hit count (QHC) sizes varied significantly from the smallest (CiteSeerX) containing 8,401,126 hits to the largest (Google Scholar) containing 389,000,000 hits
The results show that based on QHC, Google Scholar, WorldWideScience, and ProQuest are by far the largest systems providing scholarly information, with each containing about 300 million records
Summary
Academic search engines and bibliographic databases (ASEBDs) are the standard place from which to access up-to-date scientific publications. These services make an everincreasing stock of scientific knowledge accessible for scientists by filtering the most relevant information. Large crawler-based search engines such as Google Scholar, Microsoft Academic, and Scirus started to make huge volumes of scholarly data readily accessible to anyone at no cost (Ortega 2014). Beside Google Scholar and Microsoft Academic, there are many other larger multidisciplinary search engines, bibliographic databases, and other information services that try to convince academic users of the validity of their unique information offering
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