Task specialization across research careers

  1. Nicolas Robinson-Garcia  Is a corresponding author
  2. Rodrigo Costas
  3. Cassidy R Sugimoto
  4. Vincent Larivière
  5. Gabriela F Nane
  1. Delft University of Technology, Netherlands
  2. Leiden University, Netherlands
  3. Indiana University Bloomington, United States
  4. Université de Montréal, Canada

Abstract

Research careers are typically envisioned as a single path in which researchers start being one of a large number of researchers working under the guidance of one or more experienced scientists and, if they are successful, end 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.

Data availability

All data is openly accessible at http://doi.org/10.5281/zenodo.3891055

The following data sets were generated

Article and author information

Author details

  1. Nicolas Robinson-Garcia

    Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands
    For correspondence
    elrobinster@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0585-7359
  2. Rodrigo Costas

    Centre for Science and Technology Studies, Leiden University, Leiden, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7465-6462
  3. Cassidy R Sugimoto

    School of Informatics and Computing, Indiana University Bloomington, Bloomington, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Vincent Larivière

    École de bibliothéconomie et des sciences de l'information, Université de Montréal, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Gabriela F Nane

    Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

Funding

European Commission (707404)

  • Nicolas Robinson-Garcia

South African DST-NRF Centre for Excellence in Scientometrics and Science, Technology and Innovation Policy

  • Rodrigo Costas

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Peter Rodgers, eLife, United Kingdom

Publication history

  1. Received: July 1, 2020
  2. Accepted: October 23, 2020
  3. Accepted Manuscript published: October 28, 2020 (version 1)
  4. Version of Record published: November 6, 2020 (version 2)

Copyright

© 2020, Robinson-Garcia et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Nicolas Robinson-Garcia
  2. Rodrigo Costas
  3. Cassidy R Sugimoto
  4. Vincent Larivière
  5. Gabriela F Nane
(2020)
Task specialization across research careers
eLife 9:e60586.
https://doi.org/10.7554/eLife.60586

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