Dataset: Profiling of Russian and Belarusian Medical Journals in Scopus (2016–2025)

This dataset accompanies the study "Profiling of Russian and Belarusian Medical Journals in Scopus (2016–2025)". It contains the processed data and analytical results used in the bibliometric analysis of 221 medical journals indexed in Scopus. Dataset structure The dataset includes the following files: Core data final_dataset.cvs.csv – Cleaned dataset (1,600 records, journal-year level) after filtering by medical ASJC codes (27xx, 29xx, 30xx) and excluding non-medical journals. expert_list_ru_by_medical.cvs.csv – Expert list of medical journals (873 records) filtered by ASJC codes (27xx, 29xx, 30xx). Source: RASSEP project (27.04.2026). PERECHEN_ROSSIYSKIKH_ZHURNALOV_INDEKSIRUEMYKH_V_BD_SCOPUS_15022026_na_sayt_fin – Full expert list (873 records) of Russian and Belarusian journals in Scopus, as of 15.02.2026. Source: RASSEP project (27.04.2026). Analytical results table_dynamics.csv – Annual dynamics of publication output (P) and SNIP (2016–2025), including mean, median, and total values. table_asjc_dynamics.csv – Dynamics of 58 narrow thematic categories (ASJC), with absolute and relative growth rates. table_journal_metrics_full.csv – Comprehensive metrics for all 221 journals (CAGR, CV_SNIP, correlation, status). table_selfcit.csv – Self-citation analysis for each journal (mean and maximum values). journals_growth_correct.csv – Annual number of active and newly appeared journals. table_asjc_detailed_dynamics.csv – Detailed thematic dynamics with share changes. correlation_data_asjc.csv – Data used for correlation analysis between article growth and SNIP growth by thematic categories. Visualizations active_journals_by_year.png – Bar chart of active journals (2016–2025). new_journals_by_year.png – Bar chart of newly appeared journals by year. Methodology summary Data sources: CWTS Journal Indicators (March 2026) and an expert list of Russian and Belarusian journals in Scopus. Filtering: Journals were selected based on medical ASJC codes (27xx, 29xx, 30xx). Non-medical journals (ASJC 1207) were excluded. Metrics: Publication output (P), normalized impact (SNIP), and self-citation rates. Statistical methods: Descriptive statistics, Welch's t-test, Pearson correlation. Analysis environment: Google Colab (Python) with full reproducibility. Key findings Final sample: 1,600 records, 221 journals, 58 thematic categories. Total publication volume increased 3.9-fold (from 12,242 to 48,014 articles). Median SNIP increased 1.9-fold (from 0.14 to 0.27). Correlation between article growth and SNIP growth by thematic categories: r = -0.020 (p = 0.885). Usage notes The dataset is provided under the CC BY 4.0 license. The full analysis code is available at: [GitHub repository link] When using this dataset, please cite it using the DOI provided by Zenodo. Cite as Maksim Khrustalev (2025). Dataset: Profiling of Russian and Belarusian Medical Journals in Scopus (2016–2025). Zenodo. https://doi.org/10.5281/zenodo.21870434