Quantifying team chemistry in scientific collaboration

TL;DR

This study quantitatively analyzes team chemistry in scientific collaborations by reconstructing extensive publication histories, revealing ability discrepancies, network structures, and key personal traits influencing collaboration success.

Contribution

It introduces a robust method to evaluate team chemistry, uncovering its mechanisms and predicting chemistry between unacquainted scientists using network analysis.

Findings

Ability discrepancies influence team chemistry.

Network modularity predicts chemistry between new collaborators.

Research interest is highly correlated with team chemistry.

Abstract

Team chemistry is the holy grail of understanding collaborative human behavior, yet its quantitative understanding remains inconclusive. To reveal the presence and mechanisms of team chemistry in scientific collaboration, we reconstruct the publication histories of 560,689 individual scientists and 1,026,196 duos of scientists. We identify ability discrepancies between teams and their members, enabling us to evaluate team chemistry in a way that is robust against prior experience of collaboration and inherent randomness. Furthermore, our network analysis uncovers a nontrivial modular structure that allows us to predict team chemistry between scientists who have never collaborated before. Research interest is the highest correlated ingredient of team chemistry among six personal characteristics that have been commonly attributed as the keys to successful collaboration, yet the diversity of the characteristics cannot completely explain team chemistry. Our results may lead to unlocking the hidden potential of collaboration by the matching of well-paired scientists.