Role of machine and organizational structure in science

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.

Automated summary

This study investigates the contribution of team structure in ML-related projects to scientific knowledge production, finding that interdisciplinary collaboration and the engagement of individuals with expertise in both domain and computer sciences are important for achieving high impact and novel discoveries, particularly when computational and domain approaches are interdependent. The contribution of ML and its implication to team structure depend on the depth of ML.