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A potential motivation for scientists to deposit their scientific work as preprints is to enhance its citation or social impact. In this study we assessed the citation and altmetric advantage of bioRxiv, a preprint server for the biological sciences. We retrieved metadata of all bioRxiv preprints deposited between November 2013 and December 2017, and matched them to articles that were subsequently published in peer-reviewed journals. Citation data from Scopus and altmetric data from Altmetric.com were used to compare citation and online sharing behavior of bioRxiv preprints, their related journal articles, and nondeposited articles published in the same journals. We found that bioRxiv-deposited journal articles had sizably higher citation and altmetric counts compared to nondeposited articles. Regression analysis reveals that this advantage is not explained by multiple explanatory variables related to the articles’ publication venues and authorship. Further research will be required to establish whether such an effect is causal in nature. bioRxiv preprints themselves are being directly cited in journal articles, regardless of whether the preprint has subsequently been published in a journal. bioRxiv preprints are also shared widely on Twitter and in blogs, but remain relatively scarce in mainstream media and Wikipedia articles, in comparison to peer-reviewed journal articles.

This special issue brings together eight papers from experts of communities which often have been perceived as different once: bibliometrics, scientometrics and informetrics on the one side and information retrieval on the other. The idea of this special issue started at the workshop ''Combining Bibliometrics and Information Retrieval'' held at the 14th International Conference of Scientometrics and Informetrics, Vienna, July 14-19, 2013. Our motivation as guest editors started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides.

Purpose -This paper 2 discusses the new scientific search service Google Scholar (GS). This search engine, intended for searching exclusively scholarly documents, will be described with its most important functionality and then empirically tested. The focus is on an exploratory study which investigates the coverage of scientific serials in GS.Design/methodology/approach -The study is based on queries against different journal lists: international scientific journals from Thomson Scientific (SCI, SSCI, AH), Open Access journals from the DOAJ list and journals of the German social sciences literature database SOLIS as well as the analysis of result data from GS. All data gathering took place in August 2006.Findings -The study shows deficiencies in the coverage and up-to-dateness of the GS index. Furthermore, the study points up which web servers are the most important data providers for this search service and which information sources are highly represented. We can show that there is a relatively large gap in Google Scholar's coverage of German literature as well as weaknesses in the accessibility of Open Access content. Major commercial academic publishers are currently the main data providers.Research limitations/implications -Five different journal lists were analyzed, including approximately 9,500 single titles. The lists are from different fields and of various sizes. This limits comparability. There were also some problems matching the journal titles of the original lists to the journal title data provided by Google Scholar. We were only able to analyze the top 100 Google Scholar hits per journal.Practical implications -We conclude that Google Scholar has some interesting pros (such as citation analysis and free materials) but the service can not be seen as a substitute for the use of special abstracting and indexing databases and library catalogues due to various weaknesses (such as transparency, coverage and up-to-dateness).Originality/value -We do not know of any other study using such a brute force approach and such a large empirical basis. Our study can be considered as using brute force in the sense that we gathered lots of data from Google, then analyzed the data in a macroscopic way.