Investigating Research Financially Supported by the Iran National Science Foundation in Scopus and Web of Science Citation Databases
Bibliographic record
Abstract
Scientific productions (publications and patents), as an indicator of the activities of the scientific system, countries, organizations, and research centers, have always been the focus of policymakers and decision-makers at the national and international levels. The main purpose of this study was to study the performance of researchers supported by the Iran National Science Foundation (INSF) in the Scopus and Web of Science (WoS) databases. The present research has been done by the descriptive method and the scientometric approach. The data obtained from the research were analyzed using the HistCite, VOSviewer, and Excel software. The research population consists of scientific publications indexed in the Scopus database with a number of 10397 on June 14, 2021, and in WoS with a number of 11530 documents on May 11, 2021. The findings showed that publications supported by the INSF have an increasing trend and the main formats of scientific publications were research articles, review articles, quick access articles, litigation articles, and book chapters. Among the authors of publications and citations, Massoud Salavati with 325 documents was the most prolific author in Scopus, and Abbas Shafiei with 199 documents was the most prolific author in WoS. Analysis of articles showed that 36.32 percent of articles were published in journals of the first and second quartiles (Q 1 and Q 2), which indicated the scientific quality of the articles. The publications supported by the INSF have had the most national collaborations with the University of Tehran, Tarbiat Modares University, and the Sharif University of Technology in WoS. Research publications supported by the INSF nationally have had the most collaborations with the University of Tehran, Tehran University of Medical Sciences, and Islamic Azad University in papers indexed in Scopus. The publications supported by the INSF at the international level in WoS have had the most collaborations with Iran, the US, Germany, and Canada. The publications supported by the INSF have also had the most international cooperation with Iran, the US, Germany, and Canada in Scopus. Analyzing the subject areas of publications showed that most of its publications in WoS were in the field of multidisciplinary chemistry with 1083 documents, multidisciplinary sciences with 998 documents, and chemistry physics with 837 documents. Investigating the subject category of publications supported by the NSF in Scopus indicated that the field of chemistry with 3022 documents, engineering with 2585 documents, and material science with 2496 documents have the most documents.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.066 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".