OpenAlex is a promising open source of scholarly metadata, and competitor to established proprietary sources, such as the Web of Science and Scopus. As OpenAlex provides its data freely and openly, it permits researchers to perform bibliometric studies that can be reproduced in the community without licensing barriers. However, as OpenAlex is a rapidly evolving source and the data contained within is expanding and also quickly changing, the question naturally arises as to the trustworthiness of its data. In this report, we will study the reference coverage and selected metadata within each database and compare them with each other to help address this open question in bibliometrics. In our large-scale study, we demonstrate that, when restricted to a cleaned dataset of 16.8 million recent publications shared by all three databases, OpenAlex has average source reference numbers and internal coverage rates comparable to both Web of Science and Scopus. We further analyse the metadata in OpenAlex, the Web of Science and Scopus by journal, finding a similarity in the distribution of source reference counts in the Web of Science and Scopus as compared to OpenAlex. We also demonstrate that the comparison of other core metadata covered by OpenAlex shows mixed results when broken down by journal, where OpenAlex captures more ORCID identifiers, fewer abstracts and a similar number of Open Access status indicators per article when compared to both the Web of Science and Scopus.

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.

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.

Purpose – The purpose of this paper is to discuss the new scientific search service Google Scholar (GS). It aims to discuss this search engine, which is intended exclusively for searching scholarly documents, and then empirically test its most important functionality. 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 from 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 out which web servers are the most important data providers for this search service and which information sources are highly represented. The paper 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 analysed, 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. The study was only able to analyse the top 100 Google Scholar hits per journal. Practical implications – The paper concludes that Google Scholar has some interesting pros (such as citation analysis and free materials) but the service cannot 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 – The authors do not know of any other study using such a brute force approach and such a large empirical basis. The study can be considered as using brute force in the sense that it gathered lots of data from Google and then analysed the data in a macroscopic way.

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.