Since the beginning of the 2019-20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies, or even government actions to limit the spread of the disease and treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge in it challenging. In this work, we describe BIP4COVID19, an open dataset compiled to facilitate the coronavirus-related literature exploration, by providing various indicators of scientific impact for the relevant articles. Additionally, we provide a publicly accessible Web interface on top of our data, allowing the exploration of the publications based on the computed indicators.

中文翻译：

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BIP4COVID19：发布与COVID-19相关的文章的影响措施

自2019-20年冠状病毒大流行开始以来，大量相关文章已经发表或在预印本服务器中可用。这些文章以及更早的相关文献，构成了影响当代研究的宝贵知识库，甚至影响了政府限制疾病传播和医生做出治疗决定的行动。但是，这类文章的数量正以迅猛的速度增长，这使得对相关文献的探索和对其中有用知识的识别具有挑战性。在这项工作中，我们描述了BIP4COVID19，这是一个开放数据集，通过为相关文章提供各种科学影响指标来编译，以促进与冠状病毒相关的文献探索。此外，我们在数据之上提供了一个可公开访问的Web界面，