Role of machine and organizational structure in science

Moe Kyaw Thu, Shotaro Beppu, Masaru Yarime, Sotaro Shibayama

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere0272280
Number of pages17
JournalPLoS ONE
Issue number8
Publication statusPublished - 2022

Subject classification (UKÄ)

  • Business Administration


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