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

协作能力：衡量数据密集型生物医学研究的框架

• 批准号: 9981992
• 负责人: Jeff Hemsley
• 金额: $ 19.82万
• 依托单位: SYRACUSE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9981992
• 关键词: Biomedical Research; Chronic Disease; Clinical; Code; Collaborations; Computer Models; Computers; Data; Data Collection; Data Discovery; Data Reporting; Data Set; Data Sources; Databases; Diffusion; Discipline; Disease; Disease Outbreaks; Effectiveness; Empiricism; Equation; Evaluation; Event; Funding; Gel; Genbank; Genes; Genetic; Goals; Growth; Investments; Knowledge; Legal patent; Measures; Metadata; Mining; Molecular; Mus; Output; Paper; Play; Policies; Policy Research; Production; Productivity; Property; Public Health; Publications; Publishing; Research; Research Personnel; Role; Running; Science; Science Policy; Services; Severe Acute Respiratory Syndrome; Social Policies; Technology; Testing; Time; TimeLine; United States National Institutes of Health; Visualization; West Nile virus; base; cost; cyber infrastructure; data management; data sharing; data submission; data to knowledge; data tools; data warehouse; design; experience; human capital; innovation; insight; policy implication; response; simulation; statistics; theories; tool; web site

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.

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，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。该框架定义了科学技术（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，其影响 协作能力可以通过数据生产和数据到知识的指标（例如团队规模）来衡量 以及数据与出版物的比率。 GenBank 元数据作为该项目的主要数据源提供了 纵向覆盖（1984-2018）和从数据产生到出版的完整研究生命周期追踪 专利申请，为生物医学研究企业创造了前所未有的研究机会。这 项目将根据 GenBank 元数据设计和创建数据集，以生成可供分析的数据，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和 检验协作能力、团队规模和连通性之间相关性的测试假设 节点的数量，以及颠覆性节点的属性及其对生产力和创新的影响。在 除了 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，授权 数据共享）、公共卫生（例如疫情和流行的慢性病）以及三角测量资金 使用数据集并分析协作能力和政策影响。数据来源及理论 该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 协作网络。主要数据来源来自基础生物医学研究这一事实 这项研究处于前沿地位，使我们能够更全面地了解联邦投资的影响 以及协作能力政策。我们未来的研究将利用这种纵向、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，特别是在 与特定基因、疾病和治疗的关系，这些是基础和临床生物医学的关键方面 研究。