BRIDGE Center Teaming Core

项目摘要：Bridge Center团队核心 人们越来越认识到，我们时代的许多最紧迫的挑战需要协调一致的 跨学科的方法。在过去的二十年里，人们已经获得了关于什么是成功的 跨学科协作看起来像是；然而，创造空间来应用这些已知的成功 跨学科的最佳实践仍然很难实现。将跨学科方法应用于 数据密集型研究仍然令人敬畏--特别是缺乏培训和/或资源 跨学科能力建设。协作的社交方面往往被忽视。另外， 更多关于评估协作的方法以及个人和团队的多元影响 人们对协作效能的特征知之甚少，这是研究的活跃领域。作为一名 由于这些知识和资源上的差距，大型生物医学联盟往往达不到他们的 目标。在我们数十个这样的项目上的经验(并且基于多年的团队科学研究 文献)，失败的原因是缺乏对目标的共同理解，加上模棱两可 治理流程和/或错位的激励机制。专注于人工智能的项目尤其面临风险，因为 他们需要跨越多个学科边界的创新，以支持特定的人工智能应用。为了避免 这些陷阱和增进我们对团队科学的理解，我们将致力于以下目标： 目标1.设计和实施协作协议，以促进Bridge2AI的健全治理。 在这里，我们建议建立基于最佳实践的有效跨学科研究的政策和流程 练习。 目标2.在桥梁社区和其他社区实现包容性、综合性的团队科学。在这里我们 建议提供培训和指导，以满足桥梁社区的具体需求。 目标3.评估跨Bridge2AI的团队合作的效果和影响，并交流吸取的经验教训。 在这里，我们建议收集描述Bridge2AI协作及其有效性的数据，以实现以下目的 完善桥牌社区，推进团队科学科学化。

Project Summary: BRIDGE Center Teaming Core There is a growing recognition that many of the most pressing challenges of our time require a coordinated transdisciplinary approach. Over the last two decades insights have been gained about what successful transdisciplinary collaboration looks like; however, creating space to apply these known successful transdisciplinary best practices is still hard to achieve. Barriers to applying transdisciplinary methods to data-intensive research remain formidable - particularly the lack of resources for training and/or cross-disciplinary capacity-building. The social aspects of a collaboration are often neglected. Additionally, much about the methods for evaluating collaboration and the multivariate effects of individual and team characteristics on collaboration efficacy are poorly understood and are active areas of research. As a consequence of these gaps in knowledge and resources, large biomedical consortia often fall short of their objectives. In our experience on dozens of such projects (and based on years of team science research literature), failure results from a lack of shared understanding about the goals coupled with ambiguous governance processes and/or misaligned incentives. Programs focusing on AI are at particular risk because of their need for innovations across multiple disciplinary boundaries to support specific AI applications. To avoid these pitfalls and advance our understanding of team science we will engage in the following aims: Aim 1. Design and implement Collaborative Agreement to foster sound governance across Bridge2AI. Here we propose to establish policy and process for effective transdisciplinary research based on best practices. Aim 2. Realize inclusive, integrative team science across the BRIDGE community and beyond. Here we propose to provide training and guidance to meet the specific needs of the BRIDGE community. Aim 3. Evaluate efficacy and impact of teaming across Bridge2AI and communicate lessons learned. Here we propose to collect data describing Bridge2AI collaborations and their effectiveness for the purpose of improving the BRIDGE community and advancing the science of team science.