Collaboration Capacity: A Framework for Measuring Data-Intensive Biomedical Research

本拟议项目的目标是制定一个协作能力框架，并评估 通过使用基因库元数据提高科学团队在宏观、中观和微观层面的协作能力 以及其他相关数据源。该框架界定了科技人力资本， 网络基础设施和科学政策作为协作能力的推动因素， 协作能力可以通过数据生产和数据到知识的度量（如团队规模）来衡量 和数据与出版物的比率。GenBank元数据作为本项目的主要数据源， 纵向覆盖（1984-2018）和从数据生产到出版的完整研究生命周期跟踪， 专利申请，创造了前所未有的机会，研究生物医药研究企业。这 该项目将从GenBank元数据中设计和创建数据集，以生成分析就绪的数据，这些数据将 结合NSF和NIH的统计数据。这些数据集将用于开发计算模型， 检验协作能力、团队规模和连通性之间的相关性 以及破坏性节点的属性及其对生产力和创新的影响。在 除了来自NSF和NIH的统计数据外，该项目还将结合科学政策中的联合收割机事件（例如，任务 关于数据共享），公共卫生（例如，疫情和流行的慢性病），并提供资金， 分析协作能力和政策影响。数据来源和理论 这种方法弥补了过去研究中使用的以出版物为中心的数据源的局限性， 合作网络。事实上，主要数据来源来自基础生物医学研究， 这项研究处于前沿，使我们能够对联邦投资的影响获得更全面的见解 以及合作能力的政策。我们未来的研究将利用这种纵向的、丰富的数据收集， 继续深入挖掘数据生产和数据到知识生命周期中的协作，特别是 与特定基因、疾病和治疗的关系是基础和临床生物医学的关键方面， research.

The goal of this proposed project is to develop a collaboration capacity framework and evaluate the collaboration capacity of science teams at macro-, meso-, and micro-levels through using GenBank metadata and other related data sources. The framework defines the Scientifc &Technical (S&T) human capital, cyberinfrastructure, and science policy as the enablers of collaboration capacity, the impact of which on collaboration capacity can be measured by data production and data-to-knowledge metrics such as team size and ratio of data to publications. GenBank metadata as the primary data source for this project offers a longitudinal coverage (1984-2018) and full research lifecycle traces from data production to publication to patent application, creating an unprecedented opportunity to study the biomedical research enterprise. This project will design and create datasets from GenBank metadata to generate analysis-ready data, which will be combined with statistics from NSF and NIH. The datasets will be used to develop computational models and test hypotheses that examine the correlation between collaboration capacity, team size, and connectedness of nodes, as well as the properties of disruptive nodes and their impact on productivity and innovation. In addition to statistics from NSF and NIH, the project will also combine events in science policy (e.g., mandates on data sharing), public health (e.g., outbreaks and prevalent chronic diseases), and funding to triangulate with the datasets and analyze collaboration capacity and policy implications. The data source and theoretical approach compensate for the limitations of publication-centric data sources used in past research on collaboration networks. The fact that the primary data source comes from basic biomedical research situates this study at the cutting-edge and allows us to gain more holistic insights into the impact of federal investment and policy on collaboration capacity. Our future research will use this longitudinal, rich data collection to continue deeper mining of collaboration in data production and data-to-knowledge lifecycle, particularly in relation to specific genes, diseases, and treatments that are key aspects in basic and clinical biomedical research.