Bibliometric Analysis of Worldwide Coronavirus Research based on Web of Science between 1970 and February 2020
Bibliographic record
Abstract
Researchers worldwide are striving hard to find a solution for the coronavirus pandemic and reduce the fatalities from this severe outbreak. The purpose of this article is to evaluate and visualize the published documents about coronavirus research, based on extracted data from Web of Science (WoS) citation database. The study used a bibliometric method and social network analysis. Data were collected using the WoS database on February 23, 2020, with 13252 records being retrieved and used as the study sample. Descriptive statistics were used in the bibliometric method and network analysis. Text Statistics Analyzer and ISI.exe were used to compute the number of authors per document. VOSviewer and UCINET were used respectively for visualization and for measuring the centrality and the density of networks. Study findings indicate the top actors of the scientific society (authors, institutions, countries) that had the most publication on coronavirus. Similarly, the top keywords used by authors were identified. Also, the density and centrality measures of co-authorship networks (degree, closeness, betweenness) for the top 10 authors, institutions, countries, and keywords were identified. The Journal of Virology had the highest number of published papers on coronavirus research. The study revealed that the leading researchers and institutions were mostly from the United States of America, England, China, Germany, Netherlands, France, Canada, Japan, South Korea, and Saudi Arabia.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.108 | 0.135 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".