近年来,与科学相关的数字数据的更大可用性和更多访问促进了科学政策的科学,技术,创新企业。历史上,大多数得出这些数据的研究本质上是计量经济学或“科学计量学”的,专注于量化数据的发展,模型,以及科学过程的指标以及产出和结果。更广泛的研究影响定义,然而,有必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证明了他们支持的研究的影响,这些研究记录了成功技术开发中的关键事件。此类研究的一个重要缺点和障碍是案例研究方法的劳动密集型性质。目前,然而,为支持科学计量学而开发的相同数据基础设施也可能促进历史追踪研究。在本文中,我们描述了我们用来发现长期的一种方法,由国家普通医学科学研究所在1970年代末和1980年代初支持的下游研究结果,美国国立卫生研究院的一个组成部分。
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.