Bibliometric Analysis and Visualization of Scientific Publications of Iran University of Medical Sciences during 1980-2020
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.065 | 0.159 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it