The effect of mentee and mentor gender on scientific productivity of applicants for NIH training fellowships

Several studies have suggested that women in science are less productive than men, and that this gap contributes to their under-representation in the ranks of senior researchers. However, few studies have examined the role of mentoring, and in particular mentor gender, on the productivity of female scientists early in their careers. Such efforts are limited by the difficulties of unambiguously linking mentees to their mentors and measuring the research productivity resulting from those relationships. Here we use our novel author disambiguation solution to investigate the role of self-identified gender in mentorship of 12,932 trainees who either successfully or unsuccessfully applied to the National Institutes of Health for research fellowships between fiscal years 2011 and 2017, applying a multi-dimensional framework to assess productivity. We found that, after normalizing for the funding level of mentors, the productivity of female and male mentees is indistinguishable; it is also independent of the gender of the mentor, other than in measures of clinical impact, where women mentored by women outperform other mentee-mentor dyads.

A new approach and gold standard toward author disambiguation in MEDLINE

Objective Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation. Materials and Methods Following a new corpus design method, publication pairs randomly picked from MEDLINE were evaluated by both crowdsourcing and expert curators. Because the latter showed higher accuracy than crowdsourcing, expert curators were tasked to create a full corpus. The corpus was then used to explore new features that could improve state-of-the-art author disambiguation algorithms that would not have been discoverable with previously existing gold standards. Results We created a gold standard based on 1900 publication pairs that shows close similarity to MEDLINE in terms of chronological distribution and information completeness. A machine-learning algorithm that includes new features related to the ethnic origin of authors showed significant improvements over the current state of the art and demonstrates the necessity of realistic gold standards to further develop effective author disambiguation algorithms. Discussion and Conclusion An unbiased gold standard can give a more accurate picture of the status of author disambiguation research and help in the discovery of new features for machine learning. The principles and methods shown here can be applied to other reference databases beyond MEDLINE. The gold standard and code used for this study are available at the following repository: https://github.com/amorgani/AND/