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  5. COVIDScholar: An automated COVID-19 research aggregation and analysis platform

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Article
en
2023

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

0 Datasets

0 Files

en
2023
Vol 18 (2)
Vol. 18
DOI: 10.1371/journal.pone.0281147

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Gerbrand Ceder
Gerbrand Ceder

University of California, Berkeley

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John Dagdelen
Amalie Trewartha
Haoyan Huo
+8 more

Abstract

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community’s COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org , an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.

How to cite this publication

John Dagdelen, Amalie Trewartha, Haoyan Huo, Yuxing Fei, Tanjin He, Kevin Cruse, Zheren Wang, Akshay Subramanian, Benjamin Justus, Gerbrand Ceder, Kristin A. Persson (2023). COVIDScholar: An automated COVID-19 research aggregation and analysis platform. , 18(2), DOI: https://doi.org/10.1371/journal.pone.0281147.

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Publication Details

Type

Article

Year

2023

Authors

11

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1371/journal.pone.0281147

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