Since the beginning of the coronavirus pandemic, a large number of relevant articles have 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 directing 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 challenging. In this work, we describe BIP4COVID19, an open data set that offers a variety of impact measures for coronavirus-related scientific articles. These measures can be exploited for the creation or extension of added-value services aiming to facilitate the exploration of the respective literature, alleviating the aforementioned issue. In the same context, as a use case, we provide a publicly accessible keyword-based search interface for COVID-19-related articles, which leverages our data to rank search results according to the calculated impact indicators.

As the number of published scientific papers continually increases, the ability to assess their impact becomes more valuable than ever. In this work, we focus on the problem of estimating the expected citation-based popularity (or short-term impact) of papers. State-of-the-art methods for this problem attempt to leverage the current citation data of each paper. However, these methods are prone to inaccuracies for recently published papers, which have a limited citation history. In this context, we previously introduced ArtSim , an approach that can be applied on top of any popularity estimation method to improve its accuracy. Its power originates from providing more accurate estimations for the most recently published papers by considering the popularity of similar, older ones. In this work, we present ArtSim+ , an improved ArtSim adaptation that considers an additional type of paper similarity and incorporates a faster configuration procedure, resulting in improved effectiveness and configuration efficiency.