Mapping coronavirus research: quantitative and visualization approaches
Bibliographic record
Abstract
Purpose The present study aims to measure the global research landscape on coronavirus indexed in the Web of Science from 1989 to 2020. The study examines growth rates, authorship trends, institutional productivity, collaborative networks and prominent authors, institutions and countries. Design/methodology/approach The research literature on coronavirus published globally and indexed in the Web of Science core collection was retrieved using the term “Coronavirus” and its related and synonymous terms (e.g. COVID-19, SARS-COV, SARS-COV-2 and severe acute respiratory syndrome coronavirus) as per the Medical List of Subject Headings. A total of 5,625 publications were retrieved; however, the study was restricted to articles only (i.e. 4,471), and other document types were excluded. Quantitative and visualization techniques were used for data analysis and interpretation. VOSViewer software was employed to map collaborative networks of authors, institutions and countries. Findings A total of 4,471 articles have been published on coronavirus by 99 countries of the world with the maximum contribution from the USA, followed by the People's Republic of China. The United States, China, Canada, Netherlands and Germany are the front runners in the collaborative network and form strong sub-networks with other countries as well. More than 1,000 institutions collaborate in the field of coronavirus research among 99 contributing countries. The authorship pattern shows that 97.5% of publications are contributed by authors in collaboration in which 77.5% of publications are contributed by four or more than four authors. The range between degree of collaboration (DC) varies from 0.89 in 1993 to 1 in 2000 with an average of 0.96 from 1989 to 2020. The results confirm that the coronavirus research is carried out in teamwork at the individual, institutional and global levels with high magnitude and density of collaboration. The relative growth of the literature has shown inconsistency as a decreasing trend has been observed from 2007 onwards, thereby increasing the doubling time from 4.2 in the first ten years to 17.3 in the last ten years. Research limitations The study is limited to the publications indexed in the Web of Science; the findings cannot be generalized across other databases. Practical implications The results of the study may help medical scientists to identify the progress in COVID-19 research. Besdies, it will help to identify the prolific authors, institutions and countries in the development of research. Social implications The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. It has not only attacked the health of the people but the economy of nations as well. Therefore, it is feasible to know the research landscape of the disease to conquer the problem. Originality/value The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. It has not only attacked the health of the people but also the economy of nations as well. Therefore, it is feasible to know the research landscape of the disease to conquer the problem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.050 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".