Add co-authors Co-authors. https://doi.org/10.1007/s11192-011-0582-8. 2015) that year-by-year queries might lead to slightly lower total QHCs than using wide year ranges. 31, 814–830.

Literature searches are essential to evidence-based respiratory care. Then, the remaining difference of 46 million records ought to stem in part from an expansion of Google Scholar’s database within these 10 months and in part from method difference. In fact, for four search engines in our sample (AMiner, Microsoft Academic, Semantic Scholar, and WorldWideScience), the QHC proved to be inadequate to a greater or lesser degree. Whenever QHC variations occur, the question remains whether they stem from actual variations in available records or mere counting errors by the search system.

2013; Khabsa and Giles 2014; de Winter et al. The ASEBDs in our sample without QHC data (AMiner and Microsoft Academic) provide updated information on their sizes themselves.

About the size of Google Scholar: playing the numbers. 21, 383–411. Search. Comparing test searches in PubMed and Google Scholar. Academic search engines: A quantitative outlook (Chandos information professional series). Mayr, P., & Walter, A.-K. (2007). About SumSearch Website Annals of Pharmacotherapy . In M. de Rijke, A. P. de Vries, C. Zhai, F. de Jong, K. Radinsky, & K. Hofmann (Eds. Scientometrics

Stern is a well-respected, established economist: he has been the chief economist at the World Bank, the second permanent secretary of HM Treasury, and the head of the (UK) Government Economic Service.

2017). PubMed had better precision than Google Scholar for both overall search results (13% vs 0.07%, P < .001) and full-text results (8% vs 0.05%, P < .001).

Orduña-Malea, Ayllón, et al.

It is novel in so far as it improves query-based methods for assessing ASEBDs and establishes those methods as adequate, fast predictors of the sizes of most ASEBDs. Only two QHCs were implausible: Semantic Scholar and WorldWideScience. 32, 102–114. 2014). We investigated scope in terms of what information is actually available to the user, rather than the theoretically indexed knowledge. Hence, the goal of our analysis was to retrieve a maximum quantity of records of a given ASEBD with one single query.

In F. Özcan & G. Koutrika (Eds.

Two of the 12 ASEBDs—AMiner and Microsoft Academic—did not report numbers suitable for query-based size estimation. Further, the most recent comparative data on Google Scholar’s size stems from the work of Delgado López-Cózar et al.

Two years later, Khabsa and Giles (2014) estimated that close to 100 million records were listed.

4). The asterisk (*) was the most successful symbol in retrieving maximum QHCs. Library & Information Science Research, https://doi.org/10.1108/oir-07-2014-0169. The following articles are merged in Scholar. Macro Policies For Climate Change: Free Market Or State Intervention?

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Indeed, even just two years after Google Scholar’s launch in late 2004, Mayr and Walter (2007) took up the challenge to be the first to assess its coverage. A technique for measuring the relative size and overlap of public Web search engines. 12, 1–9. Scholar in the Social Sciences, The Depth and Breadth of Google Scholar: An Empirical Study, Google Scholar and the Library Web Site: The Early Response by ARL Libraries, Putting Google Scholar to the test: a preliminary study, Google Scholar and 100 Percent Availability of Information, Three options for citation tracking: Google Scholar, Scopus and Web of Science, Is Google enough? Building on previous scientometric research, this study introduces an iterative method to compare the sizes of widely used multidisciplinary ASEBDs.

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