Evidence map›Paper›PMID 36724184›Full record

ReviewPloS one2023

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

John Dagdelen, Amalie Trewartha, Haoyan Huo, Yuxing Fei, Tanjin He, Kevin Cruse, Zheren Wang, Akshay Subramanian, Benjamin Justus, Gerbrand Ceder and 1 more

Abstract readReview
In one paragraph

Review in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

John DagdelenMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.ORCID 0000-0003-2181-4815
Amalie TrewarthaMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Haoyan HuoMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Yuxing FeiWuhan University, Wuhan, Hubei, China.
Tanjin HeMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Kevin CruseMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Zheren WangMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Akshay SubramanianIndian Institute of Technology Roorkee, Roorkee, Uttarakhand, India.
Benjamin JustusDepartment of Materials Science & Engineering, University of California, Berkeley, Berkeley, CA, United States of America.
Gerbrand CederMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Kristin A PerssonDepartment of Materials Science & Engineering, University of California, Berkeley, Berkeley, CA, United States of America.ORCID 0000-0003-2495-5509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19HumansNatural Language ProcessingPandemicsPublications

Identifiers

PMID36724184
PMCPMC9891495

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.