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. 2021 Jan 7;16(1):e0244839.
doi: 10.1371/journal.pone.0244839. eCollection 2021.

A scientometric overview of CORD-19

Giovanni Colavizza  1 , Rodrigo Costas  2 , Vincent A Traag  2 , Nees Jan van Eck  2 , Thed van Leeuwen  2 , Ludo Waltman  2
Affiliations

A scientometric overview of CORD-19

Giovanni Colavizza et al. PLoS One. .

Abstract

As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has been made available alongside calls to help mine the information it contains and to create tools to search it more effectively. We analyse the delineation of the publications included in CORD-19 from a scientometric perspective. Based on a comparison to the Web of Science database, we find that CORD-19 provides an almost complete coverage of research on COVID-19 and coronaviruses. CORD-19 contains not only research that deals directly with COVID-19 and coronaviruses, but also research on viruses in general. Publications from CORD-19 focus mostly on a few well-defined research areas, in particular: coronaviruses (primarily SARS-CoV, MERS-CoV and SARS-CoV-2); public health and viral epidemics; molecular biology of viruses; influenza and other families of viruses; immunology and antivirals; clinical medicine. CORD-19 publications that appeared in 2020, especially editorials and letters, are disproportionately popular on social media. While we fully endorse the CORD-19 initiative, it is important to be aware that CORD-19 extends beyond research on COVID-19 and coronaviruses.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Publication years of CORD-19 papers.
Fig 2
Fig 2. Top 20 journals and pre-print servers by number of CORD-19 articles.
Fig 3
Fig 3. Annual number of publications in CORD-19 and CORD-19-strict.
Fig 4
Fig 4. Annual number of publications in WoS-strict and the overlap with CORD-19.
Fig 5
Fig 5. Term map highlighting temporal trends in the contents of CORD-19.
Also compare with the topic words in the S1 Appendix.
Fig 6
Fig 6. Intensity of the general topics over time.
(a) Relative topic intensity: This can be interpreted as the mean topic intensity per paper. (b) Absolute topic intensity: This can be interpreted as the number of papers per topic.
Fig 7
Fig 7. Bottom level citation network clustering.
The color of a term reflects the percentage of publications in which the term occurs that belong to a specific cluster. For example, in (a), 39% of the publications that include the term “pandemic” belong to the largest cluster, whereas only 4% of the publications that include the term “protein” belong to this cluster. Compare with Fig A.7 in S1 Appendix. (a) Largest cluster. (b) Second largest cluster. (c) Third largest cluster. (d) Fourth largest cluster. (e) Fifth largest cluster. (f) Sixth largest cluster.
Fig 8
Fig 8. Top level citation network clustering.
The color of a term reflects the percentage of publications in which the term occurs that belong to a specific cluster. For example, in (a), 46% of the publications that include the term “pandemic” belong to the largest cluster, whereas only 6% of the publications that include the term “protein” belong to this cluster. In (b), only 2% of the publications that include the term “pandemic” belong to the second largest cluster, whereas 54% of the publications that include the term “protein” belong to this cluster. Compare with Fig A.6 in S1 Appendix. (a) Largest cluster (b) Second largest cluster.
Fig 9
Fig 9. Immediate altmetric sources: Twitter, blogs and news media.
(a) Twitter. (b) Blogs. (c) News media.
See this image and copyright information in PMC

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Grants and funding

The author(s) received no specific funding for this work.