Decoding team and individual impact in science and invention

Abstract

Scientists and inventors increasingly work in teams, raising fundamental questions about the nature of team production and making individual assessment increasingly difficult. Here we present a method for describing individual and team citation impact that both is computationally feasible and can be applied in standard, wide-scale databases. We track individuals across collaboration networks to define an individual citation index and examine outcomes when each individual works alone or in teams. Studying 24 million research articles and 3.9 million US patents, we find a substantial impact advantage of teamwork over solo work. However, this advantage declines as differences between the team members’ individual citation indices grow. Team impact is predicted more by the lower-citation rather than the higher-citation team members, typically centering near the harmonic average of the individual citation indices. Consistent with this finding, teams tend to assemble among individuals with similar citation impact in all fields of science and patenting. In assessing individuals, our index, which accounts for each coauthor, is shown to have substantial advantages over existing measures. First, it more accurately predicts out-of-sample paper and patent outcomes. Second, it more accurately characterizes which scholars are elected to the National Academy of Sciences. Overall, the methodology uncovers universal regularities that inform team organization while also providing a tool for individual evaluation in the team production era.