Decoding team and individual impact in science and invention. Mohammad Ahmadpoor and Benjamin F. Jones. Proceedings of the National Academy of Sciences, July 9, 2019 116 (28) 13885-13890. https://doi.org/10.1073/pnas.1812341116
Significance: Scientists and inventors increasingly work in teams. We track millions of individuals across their collaboration networks to help inform fundamental features of team science and invention and help solve the challenge of assessing individuals in the team production era. We find that in all fields of science and patenting, team impact is weighted toward the lower-impact rather than higher-impact team members, with implications for the output of specific teams and team assembly. In assessing individuals, our index substantially outperforms existing measures, including the h index, when predicting paper and patent outcomes or when characterizing eminent careers. The findings provide guidance to research institutions, science funders, and scientists themselves in predicting team output, forming teams, and evaluating individual impact.
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.
Keywords: team science collaboration prediction team organization
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