Role of machine and organizational structure in science

PLoS One. 2022 Aug 11;17(8):e0272280. doi: 10.1371/journal.pone.0272280. eCollection 2022.

Abstract

The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bibliometrics*
  • Humans
  • Interdisciplinary Studies*

Grants and funding

S.S. received a research grant from Lars Erik Lundberg Foundation (936396, https://www.lundbergsstiftelserna.se) and Watanabe Memorial Foundation for the Advancement of Technology (R2-509, http://www.watanabe-found.or.jp). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.