Paul Donner

This study introduces an approach to estimate the uncertainty in bibliometric indicator values that is caused by data errors. This approach utilizes Bayesian regression models, estimated from empirical data samples, which are used to predict error-free data. Through direct Monte Carlo simulation—drawing many replicates of predicted data from the estimated regression models for the same input data—probability distributions for indicator values can be obtained which provide the information on their uncertainty due to data errors. [...]

The number of researchers' publications is a widely used proxy measure for scientific output, individual achievement, and performance. Despite well-known criticism from the bibliometric community, the use of bibliometric databases as a basis for measuring publication output is widespread. At the same time, there are established survey instruments that also measure the publication output per researcher. We use survey-bibliometric matching with Scopus publication records to compare the alternative publication counts. A Scopus author ID match could be found for 70 % of the respondent researchers. The number of publications per researcher varies greatly between these data sources. [...]

Here we study how the lengths of the periods from submission to acceptance (review duration) and from acceptance to publication (publication delay) relate to research quality, as operationalized by F1000Prime recommendations, for a large dataset of publications from the life and health sciences. We find a statistically detectable relationship between shorter peer review duration and recommendations, but its effect size is negligibly small.

The citations to open access preprint versions of papers and citations to papers in journals translated to English are not regularly counted in major proprietary citation index databases or in ordinary bibliometric research assessment even though they arguably reflect a true part of a work’s scientific impact. Here we explore the extent of these phenomena using Web of Science data.

This study examines how well an idiosyncratic authorship counting rule for co-authored publications recommended by the German Science Foundation (DFG) for medical schools and widely used in performance-based funding systems aligns with the empirical evidence. The DFG rule and two other co-author credit rules are compared with empirical data of percentage contribution statements of authors of co-authored papers in medicine.

This study investigates whether researchers whose published research record is more thematically broad —covering more, and more semantically distant, topics— are also characterized by specific patterns in their mobility between research organizations and countries and their co-authorship patterns. We study a large sample of productive authors in STEM fields who have been active in Germany. Our results show that specific types of international mobility go together with slightly elevated epistemic breadth. But scientists with larger co-author networks have relatively greater epistemic breadth. [...]

This paper discusses drawbacks of the percentile rank method for citation impact normalization which have hitherto been neglected in the bibliometrics literature. The transformation of citation counts to percentile ranks changes ratio scale data into ordinal scale data, for which the notions of the ratio between two values and of the magnitude of a difference between two values are not defined – a substantial loss of information. This distorts citation data particularly severely because the differences between citation counts adjacent in order in publication sets are greater for more highly cited publications and because highly cited publications are more scarce than non-highly cited ones. [...]

In this study we propose and evaluate a method to automatically identify the journal publications that are related to a Ph.D. thesis using bibliographical data of both items. We build a manually curated ground truth dataset from German cumulative doctoral theses that explicitly list the included publications, which we match with records in the Scopus database. We then test supervised classification methods on the task of identifying the correct associated publications among high numbers of potential candidates using features of the thesis and publication records. The results indicate that this approach results in good match quality in general and with the best results attained by the “random forest” classification algorithm.

This study investigates the potential of citation analysis of Ph.D. theses to obtain valid and useful early career performance indicators at the level of university departments. For German theses from 1996 to 2018 the suitability of citation data from Scopus and Google Books is studied and found to be sufficient to obtain quantitative estimates of early career researchers’ performance at departmental level in terms of scientific recognition and use of their dissertations as reflected in citations. Scopus and Google Books citations complement each other and have little overlap. Individual theses’ citation counts are much higher for those awarded a dissertation award than others. Departmental level estimates of citation impact agree ...

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 L1 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)...

We conduct an independent cluster validation study on published clustering solutions of a research testbed corpus, the Astro dataset of publication records from astronomy and astrophysics. We extend the dataset by collecting external validation data serving as proxies for the latent structure of the corpus. Specifically, we collect (1) grant funding information related to the publications, (2) data on topical special issues, (3) on specific journals’ internal topic classifications and (4) usage data from the main online bibliographic database of the discipline. The latter three types of data are newly introduced for the purpose of clustering validation and the rationale for using them for this task is set out.

By individually associating articles to basic or applied research, it is shown that basic articles are cited more frequently than applied ones. Dividing the subject categories of the Web of Science into a basic and an applied part, the mean field-normalization rate is referred to the applied or basic part depending on the research orientation of the paper analysed. By this approach, a distinct difference of the citations for the applied and basic parts of most subject categories is found. However, differences of the citation scores of applied and basic research organisations are found as well, but are less clear. The explanation is that applied and basic research organisations generally publish a mix of basic and applied articles. [...]

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. [...]