Authors : Sven E. Hug, Martin P. Brändle
This is the first in-depth study on the coverage of Microsoft Academic (MA). The coverage of a verified publication list of a university was analyzed on the level of individual publications in MA, Scopus, and Web of Science (WoS).
Citation counts were analyzed and issues related to data retrieval and data quality were examined. A Perl script was written to retrieve metadata from MA. We find that MA covers journal articles, working papers, and conference items to a substantial extent. MA surpasses Scopus and WoS clearly with respect to book-related document types and conference items but falls slightly behind Scopus with regard to journal articles.
MA shows the same biases as Scopus and WoS with regard to the coverage of the social sciences and humanities, non-English publications, and open-access publications. Rank correlations of citation counts are high between MA and the benchmark databases.
We find that the publication year is correct for 89.5% of all publications and the number of authors for 95.1% of the journal articles. Given the fast and ongoing development of MA, we conclude that MA is on the verge of becoming a bibliometric superpower. However, comprehensive studies on the quality of MA data are still lacking.
URL : https://arxiv.org/abs/1703.05539
Our current societies increasingly rely on electronic repositories of collective knowledge. An archetype of these databases is the Web of Science (WoS) that stores scientific publications. In contrast to several other forms of knowledge — e.g., Wikipedia articles — a scientific paper does not change after its « birth ».
Nonetheless, from the moment a paper is published it exists within the evolving web of other papers, thus, its actual meaning to the reader changes.
To track how scientific ideas (represented by groups of scientific papers) appear and evolve, we apply a novel combination of algorithms explicitly allowing for papers to change their groups. We (i) identify the overlapping clusters of the undirected yearly co-citation networks of the WoS (1975-2008) and (ii) match these yearly clusters (groups) to form group timelines.
After visualizing the longest lived groups of the entire data set we assign topic labels to the groups. We find that in the entire Web of Science multidisciplinarity is clearly over-represented among cutting edge ideas. In addition, we provide detailed examples for papers that (i) change their topic labels and (ii) move between groups.
URL : http://arxiv.org/abs/1605.00509
In the humanities and social sciences, bibliometric methods for the assessment of research performance are (so far) less common. The current study takes a concrete example in an attempt to evaluate a research institute from the area of social sciences and humanities with the help of data from Google Scholar (GS).
In order to use GS for a bibliometric study, we have developed procedures for the normalisation of citation impact, building on the procedures of classical bibliometrics. In order to test the convergent validity of the normalized citation impact scores, we have calculated normalized scores for a subset of the publications based on data from the WoS or Scopus.
Even if scores calculated with the help of GS and WoS/Scopus are not identical for the different publication types (considered here), they are so similar that they result in the same assessment of the institute investigated in this study: For example, the institute’s papers whose journals are covered in WoS are cited at about an average rate (compared with the other papers in the journals).
URL : : https://figshare.com/articles/The_application_of_bibliometrics_to_research_evaluation_in_the_humanities_and_social_sciences_an_exploratory_study_using_normalized_Google_Scholar_data_for_the_publications_of_a_research_institute/1293588