Onken James, Miklos Andrew C, Aragon Richard
Research Enterprise Analytics, LLC, Rockville, MD, United States.
Division of Data Integration, Modeling, and Analytics, National Institute of General Medical Sciences, Bethesda, MD, United States.
Front Res Metr Anal. 2020 Sep 8;5:5. doi: 10.3389/frma.2020.00005. eCollection 2020.
In recent years, the science of science policy has been facilitated by the greater availability of and access to digital data associated with the science, technology, and innovation enterprise. Historically, most of the studies from which such data are derived have been econometric or "scientometric" in nature, focusing on the development of quantitative data, models, and metrics of the scientific process as well as outputs and outcomes. Broader definitions of research impact, however, necessitate the use of qualitative case-study methods. For many years, U.S. federal science agencies such as the National Institutes of Health have demonstrated the impact of the research they support through tracing studies that document critical events in the development of successful technologies. A significant disadvantage and barrier of such studies is the labor-intensive nature of a case study approach. Currently, however, the same data infrastructures that have been developed to support scientometrics may also facilitate historical tracing studies. In this paper, we describe one approach we used to discover long-term, downstream outcomes of research supported in the late 1970's and early 1980's by the National Institute of General Medical Sciences, a component of the National Institutes of Health.
近年来,科学、技术与创新领域数字数据的获取变得更加容易,这推动了科学政策学的发展。从历史上看,这些数据所源自的大多数研究本质上都是计量经济学或“科学计量学”的,侧重于科学过程以及产出和成果的定量数据、模型和指标的开发。然而,对研究影响的更广泛定义需要使用定性案例研究方法。多年来,美国国立卫生研究院等联邦科学机构通过追踪研究展示了他们所支持研究的影响,这些研究记录了成功技术发展中的关键事件。此类研究的一个重大缺点和障碍是案例研究方法的劳动密集型性质。然而,目前为支持科学计量学而开发的数据基础设施也可能有助于历史追踪研究。在本文中,我们描述了一种方法,用于发现20世纪70年代末和80年代初由国立卫生研究院的一个组成部分国立综合医学科学研究所资助的研究的长期下游成果。