A dataset of mentorship in bioscience with semantic and demographic estimations

How can we study mentorship beyond coauthorship?

This data descriptor introduces a resource linking academic mentorship relationships to publication records, research representations, and demographic estimates. It is designed to support analysis of mentorship and scientific careers.

What the study found

• The dataset enriches Academic Family Tree records with Microsoft Academic Graph publication information.
• It adds semantic representations and estimated demographic variables, with validations described in the paper.

How the study works

The work combines crowdsourced mentorship records, researcher-to-publication matching, text-based research representations, and name-based demographic estimation.

Scope and limitations

• Coverage and validation are strongest in neuroscience and biomedical science.
• Demographic fields are uncertain model estimates, not self-reported identities. Matching errors and missing records can affect analyses.

Abstract

Mentorship in science is crucial for topic choice, career decisions, and the success of mentees and mentors. Typically, researchers who study mentorship use article co-authorship and doctoral dissertation datasets. However, available datasets of this type focus on narrow selections of fields and miss out on early career and non-publication-related interactions. Here, we describe Mentorship, a crowdsourced dataset of 743176 mentorship relationships among 738989 scientists primarily in biosciences that avoids these shortcomings. Our dataset enriches the Academic Family Tree project by adding publication data from the Microsoft Academic Graph and “semantic” representations of research using deep learning content analysis. Because gender and race have become critical dimensions when analyzing mentorship and disparities in science, we also provide estimations of these factors. We perform extensive validations of the profile–publication matching, semantic content, and demographic inferences, which mostly cover neuroscience and biomedical sciences. We anticipate this dataset will spur the study of mentorship in science and deepen our understanding of its role in scientists’ career outcomes.

Abstract from the original work, reproduced under its Creative Commons license. The overview above summarizes the study.

Cite this work

Q. Ke, L. Liang, Y. Ding, S V David, D E Acuna (2022). A dataset of mentorship in bioscience with semantic and demographic estimations. Scientific Data. https://doi.org/10.1038/s41597-022-01578-x

@article{keacuna2022,
  title = {A dataset of mentorship in bioscience with semantic and demographic estimations},
  author = {Ke, Q. and Liang, L. and Ding, Y. and David, S V and Acuna, D E},
  year = {2022},
  publication_date = {2022-08-02},
  journal = {Scientific Data},
  volume = {9},
  number = {467},
  doi = {10.1038/s41597-022-01578-x},
  url = {https://www.nature.com/articles/s41597-022-01578-x}
}