BIP4COVID19: Impact metrics and indicators for coronavirus related publications

Description

This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on: Τhe CORD-19 dataset released by the team of Semantic Scholar 1 and Τhe curated data provided by the LitCovid hub 2 . These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 628,506 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank 3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking ( https://github.com/diwis/PaperRanking ) library 4 . Influence alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles…

Description from Zenodo, shortened.

Files (5)

• CSVarticles_by_influence.csv63 MB
• CSVarticles_by_tweets.csv63 MB
• CSVarticles_by_popularity.csv63 MB
• CSVarticles_by_influence_alt.csv63 MB
• CSVarticles_by_popularity_alt.csv63 MB

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