Academic Search Engines: A Quantitative Outlook

The subtitle says that this book provides a quantitative outlook, but the author also takes a good look at the key qualitative aspects of academic search engines, and does both very well. Having spent much of the past 25 years exploring and reporting what the producers and my mother never told me about databases, I know what an arduous job it is to create a credible quantitative portrait about their real content using the bundled software.

The book presents the analysis of six academic search engines (CiteSeerX, Scirus – terminated by Elsevier a year ago for no good reason, ArnetMiner (AMiner), Microsoft Academic Search (MAS) and Google Scholar (GS) – including two of the relatively new services, Google Scholar Citations and Google Scholar Metrics. There are short analyses of three additional less well-known academic search engines: BASE, Q-Sensei Scholar and WorldWideScience.

The descriptions of the academic search engines are clear and succinct, and are well-enhanced with informative tables, charts and graphs. This cannot be said of the figures of screen shots made of search templates and search result lists, as they are very difficult to read even with a magnifying glass, especially when two screenshots are squeezed onto a page. This is the drawback of the handy 6.5×9 inch page size. As these figures are useful and not just decorations, it would be very helpful to post these on the author’s or publisher’s website. The tables showing the distribution of documents, citations and other indicators by search engines, top-10 countries and broad disciplines are excellent, but they should be extended by the citations/document key indicator, which can be accommodated within the single table pages.

The narrative is well balanced, describing “the good, the bad and the ugly”, as well as the dysfunctional features of each resource. The language is sufficiently clear, but there are some terms and sentences where a thought in Spanish does not come through well in English, such as the term “inverse engineering” for “reverse engineering” or the sentence “the information in a journal article published in an impacted journal is not the same as the information available in a teaching course because the process of the creation of each type of document is itself an indicator”. I have sympathy with this, because in my manuscripts I often make similar mistakes as an ESL person. For a second edition, which I am sure will come, these could be clarified with help of a good copy editor. It hopefully will be enhanced by the profile of the excellent Scimago academic search engine of scholarly journals, created by the cooperation of bibliometricians working at various Spanish universities and research centres. Several of them presented their findings in top quality metrics-focused serials, including El Profesional de la Informacion (EPI) and the Anuario ThinkEPI.

This book contributes to the reputation of Spain’s ever-growing role in bibliometrics, scentometrics and informetrics, and it is very useful for understanding why academic search engines produce such different research indicator scores for the same researchers, journals and institutions.