Evidence map›Paper›PMID 40494890›Full record

ArticleScientific data2025

Coronavirus research topics, tracking twenty years of research.

Amir Aryani, Jingbo Wang, Luis Salvador-Carulla, Jihoon Woo, Cathy P W Cheung, Zhuochen Wu, Hui Yin, Junhua Xiao, Elisabeth A Lambert, Jason Howitt and 12 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

22 authors.

Amir AryaniSwinburne University of Technology, Melbourne, Australia. aaryani@swin.edu.au.ORCID 0000-0002-4259-9774
Jingbo WangNational Computational Infrastructure, The Australian National University, Canberra, Australia.
Luis Salvador-CarullaUniversity of Canberra, Canberra, Australia.ORCID 0000-0002-5742-9866
Jihoon WooSwinburne University of Technology, Melbourne, Australia.
Cathy P W CheungAustralian National University (ANU), Canberra, Australia.
Zhuochen WuNational Computational Infrastructure, The Australian National University, Canberra, Australia.
Hui YinSwinburne University of Technology, Melbourne, Australia.ORCID 0000-0002-1786-4951
Junhua XiaoSchool of Allied Health, La Trobe University, Melbourne, Australia.
Elisabeth A LambertSwinburne University of Technology, Melbourne, Australia.
Jason HowittSwinburne University of Technology, Melbourne, Australia.
Jean M DavidsonCalifornia Polytechnic State University, San Luis Obispo, US.
Serene YoongSchool of Health and Social Development, Deakin University, Burwood, Australia.
John B DixonSwinburne University of Technology, Melbourne, Australia.
Rachel E ClimieUniversity of Tasmania, Hobart, Australia.
Jose A Salinas-PerezUniversidad Loyola Andalucía, Andalucía, Spain.
Nasser BagheriUniversity of Canberra, Canberra, Australia.
Celine SantiagoVictor Chang Cardiac Research Institute, Darlinghurst, Australia.ORCID 0000-0002-1549-3667
Joanne WilliamsSwinburne University of Technology, Melbourne, Australia.
Nilmini WickramasingheSwinburne University of Technology, Melbourne, Australia.
Leo NgSwinburne University of Technology, Melbourne, Australia.ORCID 0000-0002-9814-0495
Clara C ZwackSwinburne University of Technology, Melbourne, Australia.
Gavin W LambertSwinburne University of Technology, Melbourne, Australia.ORCID 0000-0003-0315-645X

Funding

Department of Education and Training | Australian Research Council (ARC) IC200100022Department of Education and Training | Australian Research Council (ARC) LE200100074
6 · The paper itself

Abstract

Research publications aimed at understanding the various aspects of Coronaviruses, particularly COVID-19, have significantly shaped our knowledge base. While the urgency to monitor COVID-19 in real-time has decreased, the continual influx of new research of monthly articles underscores the importance of systematic review and analysis to deepen our understanding of the pandemic's broad impact. To explore research trends and innovations in this space, we developed a pipeline using natural language processing techniques. This pipeline systematically catalogues and synthesises the vast array of research articles, leading to the creation of a dataset with more than eight hundred thousand articles from July 2002 to May 2024. This paper describes the content of this dataset and provides the necessary information to make this dataset accessible and reusable for future research. Our approach aggregates and organises global research related to Coronaviruses into thematic clusters such as vaccine development, public health strategies, infection mechanisms, mental health issues, and economic consequences. Also, we have leveraged the contribution of health experts to review and revise the dataset.

Indexed as

Biomedical ResearchCoronavirusCOVID-19HumansNatural Language ProcessingSARS-CoV-2

Identifiers

PMID40494890
PMCPMC12152131

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.