Paul Donner

In this study, we investigated whether citation context information is able to increase the validity of citation impact analyses to measure research quality compared to simple citation counts. We analyzed the statistical relationships of information extracted from structured citation context data in the Web of Science (Clarivate) such as the placement of citations within specific sections of an article with post-publication peer review quality ratings from Faculty Opinions (H1 connect), used as an external validity criterion for research quality. The study is based on publications in medicine and life sciences. [...]

Bei Artikeln in Ko-Autorschaft wird oft um die Autorenreihenfolge gerungen. Einige (ungewöhnliche) Lösungsvorschläge im Überblick.

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. [...]

Author self-citations are a somewhat controversial phenomenon. Some scholars maintain they are a normal, even indispensable, part of scientific referencing practice, while others claim they are frequently an expression of vanity and self-promotion. Citations are the basic data for citation network clustering, an important approach to creating bottom-up, data-driven, global taxonomic systems of research publications. Thus the topical information content of self-citations is of particular interest in this context. [...]

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.

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.

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 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.