Iranian Scientometrics; Dataset on universities, professors and articles

Area of Science:

• Scientometrics
• Bibliometrics
• Data Science

Background:

• Academic productivity and impact are key metrics for institutional and national progress.
• Understanding scholarly output requires comprehensive, reliable data on publications and researchers.
• Existing datasets may lack specific regional focus or detailed author-level metrics.

Purpose of the Study:

• To introduce a novel, comprehensive dataset of Iranian academic publications and professorial metrics.
• To provide a foundation for scientometric analysis, network analysis, and institutional benchmarking.
• To facilitate the development of data-driven strategies for enhancing research excellence.

Main Methods:

• Systematic data collection from Google Scholar using Python (Selenium, BeautifulSoup).
• A four-step Data Refinement Process including citation threshold, author-article verification, and temporal filtering (2020-2022).
• Ensured compliance with Google Scholar's Terms of Service through rate-limiting and distributed crawling.

Main Results:

• A dataset containing over 1.5 million records of academic articles with detailed metadata.
• Includes interlinked files on article metadata, professor profiles, and institutional details.
• Focused cohort derived for analysis, emphasizing governmental universities and high-citation professors.

Conclusions:

• The dataset offers a significant resource for exploring academic productivity, collaboration, and institutional performance.
• Enables in-depth analysis for social network analysis, trend identification, and academic benchmarking.
• Supports the development of machine learning models for research output classification and scholarly trend analysis.