Mapping Global Research On Synthetic Data: A Bibliometric Analysis

Synthetic data has emerged as a transformative solution for enabling privacy-preserving access to sensitive datasets across health, social science, finance, and administrative domains. As governments, research institutions, and data professionals increasingly explore synthetic data to balance openness with confidentiality, a clearer understanding of global research patterns in this rapidly expanding field is essential. This study provides a bibliometric analysis of synthetic data research, mapping its intellectual structure, growth trajectory, collaborative networks, and thematic evolution. Bibliometric analysis quantitatively investigates research productivity, impact, collaboration, thematic structures, and the evolution of knowledge within a specific field of study. Therefore, the study would identify trends, most cited and productive authors and journals, emerging themes and pattern of collaboration in synthetic data research globally over a decade. The study would adopt bibliomenrtic method while bibliometric data such as authors’ name, citation counts, journal and keywords, abstracts etc. would be drawn from publications indexed in the Web of Science, Scopus and OpenAlex which are comprehensive scholarly databases to provide answers to the study’s objectives. For accuracy and consistency in the dataset, bibliometric indicators would be cleaned and standardised. Tools like excel, bibliometrix, VOSviewer and CiteSpace would be used to present the results using both descriptive and visualisation methods. The findings would support data professionals, librarians, policymakers, and research managers in understanding global trends, identifying capacity gaps, and anticipating future directions for responsible synthetic data adoption. Recommendations would be provided for strengthening research support services, enhancing data literacy, and aligning institutional policies with emerging synthetic data practices.