Bibliometric Analysis and Visualization of Scientific Publications of Iran University of Medical Sciences during 1980-2020
Bibliographic record
Abstract
In this research, all the scientific publications of the Iran University of Medical Sciences (IUMS) from 1980 to 2020 are studied using bibliometric analysis and scientific network visualization. This research applied quantitative research using bibliometrics and visualization of scientific publications. The research population included all the scientific publications of IUSM on the Web of Science Core Collection (WOSCC) from 1980 to 2020. Data from the WOSCC were extracted via the advanced search by searching the Iran University of Medical Sciences in the affiliation field. Microsoft Excel and VOSviewer were used for data analysis. First, the frequency distribution of the scientific publications was identified. Then, the level of international collaborations was analyzed. Finally, the citation clusters of researchers' scientific publications and keyword co-occurrence were examined. IUMS had 9950 documents indexed in the WOSCC. Malekzadeh jointly ranked first as the most prolific author. The Iranian Red Crescent Medical Journal, with 207 articles, has the highest number of articles. All highly-cited papers were published in high-level Q1 journals. The highest collaboration rate at a national level was with the Tehran University of Medical Sciences. Internationally, IUMS's researchers had the highest collaboration with authors from the United States, the United Kingdom, Canada, and Australia, respectively. Term clustering demonstrated five main clusters: pharmacological studies, epidemiological studies, general & and internal medicine, meta-analysis and systematic review, and Immunological studies. The methods and techniques of bibliometrics and visualization are optimal for depicting and analyzing the scientific status of researchers, publications, journals, universities, countries, and even the world. The current study can be a model for analyzing bibliometric indices of other universities and research institutes in Iran and elsewhere.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.082 | 0.110 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".