Chen Chaomei
College of Computing and Informatics, Drexel University, Philadelphia, PA, United States.
Front Res Metr Anal. 2020 Dec 23;5:607286. doi: 10.3389/frma.2020.607286. eCollection 2020.
As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We introduce a generic method that facilitates the data collection and sense-making process when dealing with a rapidly growing landscape of a research domain such as COVID-19 at multiple levels of granularity. The method integrates the analysis of structural and temporal patterns in scholarly publications with the delineation of thematic concentrations and the types of uncertainties that may offer additional insights into the complexity of the unknown. We demonstrate the application of the method in a study of the COVID-19 literature.
在全球科学家探寻这场致命大流行背后绝大多数未知答案的过程中，有关新冠病毒的文献呈指数级增长。要跟上如此快速发展的文献步伐，不仅对活跃的研究人员，而且对整个社会来说，都构成了重大挑战。尽管众多数据资源已公开可用，但在高度不确定的情况下有效梳理海量信息所必需的分析和综合过程，仍然是一个重大瓶颈。我们引入一种通用方法，该方法有助于在处理像新冠病毒这样处于多个粒度层次的快速发展的研究领域时的数据收集和意义建构过程。该方法将学术出版物中的结构和时间模式分析与主题集中领域的描绘以及可能为未知复杂性提供更多见解的不确定性类型相结合。我们展示了该方法在一项关于新冠病毒文献研究中的应用。
