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 the 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.

中文翻译：

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Microsoft Academic Graph 上 COVID-19 文献的前八个月一瞥：主题、引文上下文和不确定性

随着全世界的科学家都在寻找致命大流行背后绝大多数未知的答案，有关 COVID-19 的文献呈指数级增长。以如此快速的速度跟上文学体的步伐，不仅对活跃的研究人员而且对整个社会都构成了重大挑战。尽管已公开提供大量数据资源，但对于有效浏览大量不确定性较高的信息至关重要的分析和综合过程仍然是一个重大瓶颈。我们引入了一种通用方法，可在处理多个粒度级别的研究领域（例如 COVID-19）快速增长的情况时促进数据收集和意义制定过程。该方法将学术出版物中结构和时间模式的分析与主题集中度和不确定性类型的描述相结合，可以提供对未知复杂性的额外见解。我们展示了该方法在 COVID-19 文献研究中的应用。