Cumulative dissertations are doctoral theses comprised of multiple published articles. For studies of publication activity and citation impact of early career researchers it is important to identify these articles and link them to their associated theses. Using a new benchmark data set, this paper reports on experiments of measuring the bilingual textual similarity between, on the one hand, titles and keywords of doctoral theses, and, on the other hand, articles’ titles and abstracts. The tested methods are cosine similarity and L 1 distance in the Vector Space Model (VSM) as baselines, the language-indifferent methods Latent Semantic Analysis (LSA) and trigram similarity, and the language-aware methods fastText and Random Indexing (RI). LSA and RI, two supervised methods, were trained on a purposively collected bilingual scientific parallel text corpus. The results show that the VSM baselines and the RI method perform best but that the VSM method is unsuitable for cross-language similarity due to its inherent monolingual bias.

A perennial problem in bibliometrics is the appropriate distribution of authorship credit for coauthored publications. Several credit allocation methods and formulas have been introduced, but there has been little empirical validation as to which method best reflects the typical contributions of coauthors. This paper presents a validation of credit allocation methods using a new data set of author-provided percentage contribution figures obtained from the coauthored publications in cumulative PhD theses by authors from three countries that contain contribution statements. The comparison of allocation schemes shows that harmonic counting performs best and arithmetic and geometric counting also perform well, while fractional counting and first author counting perform relatively poorly.

The present study is an evaluation of three frequently used institution name disambiguation systems. The Web of Science normalized institution names and Organization Enhanced system and the Scopus Affiliation ID system are tested against a complete, independent institution disambiguation system for a sample of German public sector research organizations. The independent system is used as the gold standard in the evaluations that we perform. We study the coverage of the disambiguation systems and, in particular, the differences in a number of commonly used bibliometric indicators. The key finding is that for the sample institutions, the studied systems provide bibliometric indicator values that have only a limited accuracy. Our conclusion is that for any use with policy implications, additional data cleaning for disambiguating affiliation data is recommended.