Louise M. Darling Biomedical Library, University of California, Los Angeles, California, USA.
Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
Clin Transl Sci. 2021 Sep;14(5):1705-1712. doi: 10.1111/cts.13067. Epub 2021 May 31.
Research organizations are becoming more reliant on quantitative approaches to determine how to recruit and promote researchers, allocate funding, and evaluate the impact of prior allocations. Many of these quantitative metrics are based on research publications. Publication metrics are not only important for individual careers, but also affect the progress of science as a whole via their role in the funding award process. Understanding the origin and intended use of popular publication metrics can inform an evaluative strategy that balances the usefulness of publication metrics with the limitations of what they can convey about the productivity and quality of an author, a publication, or a journal. This paper serves as a brief introduction to citation networks like Google Scholar, Web of Science Core Collection, Scopus, Microsoft Academic, and Dimensions. It also explains two of the most popular publication metrics: the h-index and the journal impact factor. The purpose of this paper is to provide practical information on using citation networks to generate publication metrics, and to discuss ideas for contextualizing and juxtaposing metrics, in order to help researchers in translational science and other disciplines document their impact in as favorable a light as may be justified.
研究机构越来越依赖定量方法来确定如何招募和提升研究人员、分配资金以及评估先前分配的影响。这些定量指标中的许多都是基于研究出版物。出版物指标不仅对个人职业发展很重要，而且通过在资金授予过程中发挥作用，影响整个科学的进展。了解流行出版物指标的来源和预期用途，可以为评估策略提供信息，在出版物指标的有用性与它们能够传达的关于作者、出版物或期刊的生产力和质量的局限性之间取得平衡。本文简要介绍了像谷歌学术、科睿唯安 Web of Science 核心合集、爱思唯尔 Scopus、微软学术和 Dimensions 这样的引文网络。它还解释了两个最流行的出版物指标：h 指数和期刊影响因子。本文的目的是提供使用引文网络生成出版物指标的实用信息，并讨论上下文化和并列化指标的想法，以帮助转化科学和其他学科的研究人员以尽可能合理的有利方式记录他们的影响。
