Contribution of Iran in COVID-19 studies: a bibliometrics analysis
Bibliographic record
Abstract
Background: Iran is fighting heroically against COVID-19. Due to the importance of scientific publications in better dealing with this stubborn virus, this study was conducted aiming at reviewing COVID-19 publications by Iranian scientists. Methods: We searched for COVID-19 and all its related keywords in the Web of Science (WOS), Scopus and PubMed databases to find documents published by Iranian authors until July 10, 2020. Duplicates documents were excluded, and bibliographic parameters were evaluated. Co-authorship matrix was calculated using Bibexcel, and visualizations were done using VOSviewer. Results: A total of 849 documents from 3450 Iranian researchers (5.5 authors per document) were retrieved from WOS, PubMed, and Scopus and Iran ranked 12th and 13th in WOS and Scopus in terms of the number of publications. The average citation per document was 2.2 with the h-index of 18. Original articles and letters were the most common formats for Iranian publications. The Journal of Military Medicine has published the highest number of documents. Iranian authors have mostly collaborated with researchers from the United States, Italy, the UK, and Canada, respectively. The co-occurrence network for keywords represented five publication clusters in the collection, and the largest clusters were related to epidemiological studies and public health, followed by clinical studies on COVID-19. Conclusion: Iranian researchers have had a significant scientific contribution in various areas of the disease. However, the network of studies has not been sufficiently cohesive, and more coherent collaboration between researchers at the national and international levels should be on the agenda of research policymakers in the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.023 | 0.046 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".