Predicting the age of researchers using bibliometric data

Academic vs. biological age in research on academic careers: a large-scale study with implications for scientifically developing systems

Biological age is an important sociodemographic factor in studies on academic careers (research productivity, scholarly impact, and collaboration patterns). It is assumed that the academic age, or the time elapsed from the first publication, is a good proxy for biological age. In this study, we analyze the limitations of the proxy in academic career studies, using as an example the entire population of Polish academic scientists and scholars visible in the last decade in global science and holding at least a PhD (N = 20,569). The proxy works well for science, technology, engineering, mathematics, and medicine (STEMM) disciplines; however, for non-STEMM disciplines (particularly for humanities and social sciences), it has a dramatically worse performance. This negative conclusion is particularly important for systems that have only recently visible in global academic journals. The micro-level data suggest a delayed participation of social scientists and humanists in global science networks, with practical implications for predicting biological age from academic age. We calculate correlation coefficients, present contingency analysis of academic career stages with academic positions and age groups, and create a linear multivariate regression model. Our research suggests that in scientifically developing countries, academic age as a proxy for biological age should be used more cautiously than in advanced countries: ideally, it should be used only for STEMM disciplines.

Task specialization across research careers

Research careers are typically envisioned as a single path in which a scientist starts as a member of a team working under the guidance of one or more experienced scientists and, if they are successful, ends with the individual leading their own research group and training future generations of scientists. Here we study the author contribution statements of published research papers in order to explore possible biases and disparities in career trajectories in science. We used Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLoS journals, which included 347,136 distinct authors and their associated contribution statements. This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile scientists across four career stages: junior, early-career, mid-career and late-career. All three of the archetypes we found - leader, specialized, and supporting - were encountered for early-career and mid-career researchers. Junior researchers displayed only two archetypes (specialized, and supporting), as did late-career researchers (leader and supporting). Scientists assigned to the leader and specialized archetypes tended to have longer careers than those assigned to the supporting archetype. We also observed consistent gender bias at all stages: the majority of male scientists belonged to the leader archetype, while the larger proportion of women belonged to the specialized archetype, especially for early-career and mid-career researchers.

The relationship between bioRxiv preprints, citations and altmetrics

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.