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A scientometric overview of CORD-19

Colavizza, G.; Costas, R.; Traag, V. A.; van Eck, N. J.; van Leeuwen, T.; Waltman, L.

2020-04-20 scientific communication and education
10.1101/2020.04.20.046144 bioRxiv
Show 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 along-side 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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 6%
26.1%
Similar papers in this journal
2
GigaScience
◐ 212 papers in training set
Top 0.2%
7.7%
Similar papers in this journal
3
Patterns
○ 78 papers in training set
Top 0.2%
6.1%
Similar papers in this journal
4
Heliyon
○ 152 papers in training set
Top 0.5%
5.4%
Similar papers in this journal
5
eLife
● 5828 papers in training set
Top 26%
4.8%
Similar papers in this journal
50% of probability mass above
6
Scientific Reports
○ 3612 papers in training set
Top 26%
4.0%
7
BioData Mining
○ 22 papers in training set
Top 0.1%
2.7%
8
Scientific Data
○ 209 papers in training set
Top 1%
2.3%
9
Database
◐ 61 papers in training set
Top 0.4%
2.1%
10
PLOS Computational Biology
● 1863 papers in training set
Top 13%
2.1%
11
PLOS Digital Health
● 106 papers in training set
Top 3%
2.1%
12
Journal of Biomedical Informatics
○ 47 papers in training set
Top 0.8%
1.7%
13
PeerJ
◐ 308 papers in training set
Top 5%
1.7%
14
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.5%
1.7%
15
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 29%
1.7%
16
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 1%
1.7%
17
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 3%
1.4%
18
Royal Society Open Science
● 214 papers in training set
Top 4%
1.3%
19
Wellcome Open Research
○ 67 papers in training set
Top 0.7%
1.3%
20
eneuro
● 439 papers in training set
Top 6%
1.1%
21
BMC Medical Research Methodology
○ 47 papers in training set
Top 1%
1.1%
22
PLOS Biology
● 486 papers in training set
Top 8%
1.1%
23
Bioinformatics
◐ 1204 papers in training set
Top 8%
1.1%
24
Frontiers in Microbiology
○ 427 papers in training set
Top 7%
1.0%
25
iScience
○ 1154 papers in training set
Top 37%
0.8%
26
Sustainability
○ 10 papers in training set
Top 0.5%
0.8%
27
F1000Research
○ 88 papers in training set
Top 4%
0.8%
28
Vaccines
○ 198 papers in training set
Top 4%
0.6%
29
Frontiers in Public Health
○ 148 papers in training set
Top 7%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.