Abstract
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. Finally, we provide a publicly accessible web interface on top of our data, allowing the exploration of the publications based on the computed indicators.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We describe an updated version of the dataset. We applied minor revisions on the text (to improve reading comprehension). We simplified the title of the preprint. We made Figure 1 more compact.
