# A dataset of mentorship in science with semantic and demographic estimations

arXiv: https://arxiv.org/abs/2106.06487 · HTML: https://mlsanity.com/paper/2106.06487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through](https://mlsanity.com/paper/2203.15364/) (2022)

## Abstract (arXiv)

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 across 112 fields that avoids these shortcomings. We enrich the scientists' profiles with 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. We anticipate this dataset will spur the study of mentorship in science and deepen our understanding of its role in scientists' career outcomes.