This version of BIP! Finder aims to ease the exploration of COVID-19-related literature by enabling ranking articles based on various impact metrics.
Provided impact measures:
Popularity: Citation-based measure reflecting the current impact.
Influence: Citation-based measure reflecting the total impact.
Reader Attention: The current number of Mendeley readers.
Social Media Attention: The number of tweets related to this article.
*More details on these impact measures can be found here.
Popularity: Citation-based measure reflecting the current impact.
Influence: Citation-based measure reflecting the total impact.
Reader Attention: The current number of Mendeley readers.
Social Media Attention: The number of tweets related to this article.
*More details on these impact measures can be found here.
Score interpretations:
Exceptional score (in top 0.01%).
Substantial score (in top 1%).
Average score (in bottom 99%).
Score not available.
Exceptional score (in top 0.01%).
Substantial score (in top 1%).
Average score (in bottom 99%).
Score not available.
Main data sources:
CORD-19 dataset(1) (list of papers)
LitCovid hub(2) (list of papers)
PMC & PubMed (citations)
Mendeley (number of readers)
COVID-19-TweetIDs(3) (tweets)
CORD-19 dataset(1) (list of papers)
LitCovid hub(2) (list of papers)
PMC & PubMed (citations)
Mendeley (number of readers)
COVID-19-TweetIDs(3) (tweets)
(1) COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-05-12. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-05-17. doi:10.5281/zenodo.3715506
(2) Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2020-05-17)
(3) Currently tweets between Jan. 21st and Mar. 31th have been considered based on v1.8 (to be updated).
Copyright ATHENA RC 2020, Developed by: Thanasis Vergoulis, Ilias Kanellos, Serafeim Chatzopoulos, and Danae Pla Karidi
This service is provided "as is", without any warranties of any kind.