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

该拟议项目的目标是开发协作能力框架并评估 通过使用GenBank元数据提高科学团队在宏观、中观和微观层面的协作能力 和其他相关数据源。该框架界定了科技(S&T)人力资本， 网络基础设施和科学政策作为协作能力的推动者，其对 协作能力可以通过数据生产和数据对知识的衡量标准来衡量，例如团队规模 以及数据与出版物的比率。作为该项目的主要数据源的GenBank元数据提供了 纵向覆盖(1984-2018)和完整的研究生命周期跟踪，从数据生产到发布，再到 专利申请，为生物医药研究企业创造了前所未有的机遇。这 Project将从GenBank元数据设计和创建数据集，以生成可用于分析的数据，这将是 结合NSF和NIH的统计数据。数据集将用于开发计算模型和 检验协作能力、团队规模和连接性之间的相关性的假设 节点的属性，以及破坏性节点的属性及其对生产力和创新的影响。在……里面 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)以及三角定位的资金 并分析协作能力和策略影响。数据来源和理论 方法弥补了过去研究中使用的以发布为中心的数据源的局限性。 协作网络。主要数据来源来自基础生物医学研究的事实是 这项研究处于前沿，使我们能够更全面地了解联邦投资的影响 以及关于协作能力的政策。我们未来的研究将使用这种纵向的、丰富的数据收集来 继续深入挖掘数据生产和数据到知识生命周期中的协作，尤其是在 与基础和临床生物医学中的关键方面的特定基因、疾病和治疗的关系 研究。

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.