Leveraging Rich Data to Understand Scientific Diversity, Impact, and the Potential of Teams

Award:#1856090

Period: 08/15/2019-07/31/2022

The overarching aim of the proposed research is to enable policymakers to make sizeable investments in science that move from “high risk high payoff” to “safe(r) bets high payoff.” To do this, we utilize the full text records of knowledge that has been produced and indexed in the (1) Web of Science, (2) ScienceDirect, and the (3) United States Patent and Trademark Office (USPTO). We look at the teams, and solos, who produced this work and the knowledge domains of the expertise they held individually and as a team. We deploy cutting edge methodologies to look at the mix of expertise areas that were brought to bear on the problem. We look for deep patterns in the way these teams (and solos) are relating, extending, integrating, and juxtaposing the breadth and depth of prior knowledge and then we predict the ultimate impact of the team’s products, along multiple dimensions. Significantly, we leverage the full text of these scientific archives to discover the antecedent, mediator, and moderating conditions under which the products produced by teams ultimately have the greatest impact. This research project is designed to answer five questions with policy implications for interdisciplinary team science:

1. How can we best measure the success, and predict the future success, of interdisciplinary science teams, in order to determine a team’s likely return on investment?
2. How can we predict the success of scientific teams midstream?
3. Which kinds of scientific teams, based on their composition, offer the greatest return?
4. Which scientific investments offer the greatest return: investments in interdisciplinary people or in interdisciplinary teams? Or investments in teams comprised of interdisciplinary people?
5. How can we determine the relative contributions of individuals to science team success?

Research Team