Science Policy Research Report: Funding Team Science

团队科学是两个或两个以上的研究人员相互协作，朝着共同的研究目标努力。随着资助机构继续鼓励和促进团队科学，重要的是要评估和综合我们所知道的团队规模的后果。鉴于财政资源稀缺，一个重要的科学政策问题是如何有效地将资金分配给研究团队。例如，给定一个150万美元的总项目预算，什么时候资助一个由三名研究人员组成的团队，每人50万美元，而不是一个由10名研究人员组成的团队，每人15万美元。一方面，资助更少的研究人员（每个研究人员的资金更多）增加了他们的激励，使协调更容易，但减少了可用的团队专业知识的数量。另一方面，资助更多的研究人员，每个研究人员的钱少，减少了他们的激励，使协调更加困难，但增加了团队专业知识的数量。通过对团队科学的学术文献进行跨学科的回顾，该项目提高了我们对资助不同规模团队的好处和成本的认识。相关的主题也将被探讨，包括团队复杂性的作用（例如，学科数目和机构数目）、团队的生命周期（例如，在团队生命早期提供资金与在团队生命后期提供资金）以及研究发展阶段（例如，为早期研究提供资金与为后期研究提供资金）。为了确保所确定的证据是稳健和可靠的，将使用三个类别来对团队规模的已知情况进行分类：（1）强因果推理（例如，实验室实验和现场实验），（2）适度的因果推理（例如，具有适当控制的纵向研究），和（3）弱因果推理（例如，横向调查和观察性研究）。根据证据，将提出政策建议，以指导资源的有效分配，以团队科学，以促进复杂问题的解决，新的研究发现和技术创新。

Team science is the collaboration of two or more researchers working interdependently towards a common research goal. As funding agencies continue to encourage and promote team science, it is important to evaluate and synthesize what we know about the consequences of team size. Given scare financial resources, an important science policy issue is how to effectively allocate funding to research teams. For example, given an overall project budget of $1.5M, when is it better to fund a team of three researchers at $500,000 each versus a team of 10 researchers at $150,000 each. On the one hand, funding fewer researchers (with more money per researcher) increases their incentives and makes coordination easier, but reduces the amount of team expertise available. On the other hand, funding more researchers, with less money per researcher, decreases their incentives and makes coordination more difficult, but increases the amount of team expertise available. Through an interdisciplinary review of the academic literature on team science, this project advances our knowledge of the benefits and costs of funding teams of varying size. Related topics will be explored as well, including the role of team complexity (e.g., number of disciplines and number of institutions), the life cycle of teams, for example, funding early in the life of the team vs funding later in the life of the team, and the stage of research development, for example, funding early-stage research vs funding late-stage research. To ensure that the evidence identified is robust and reliable, three categories will be used to classify what is known about team size: (1) strong causal inference (e.g., laboratory experiments and field experiments), (2) moderate causal inference (e.g., longitudinal studies with appropriate controls), and (3) weak causal inference (e.g., cross-sectional surveys and observational studies). Based on the evidence, policy recommendations will be made to guide the efficient allocation of resources to team science in order to facilitate complex problem solving, new research discoveries, and technological innovation.