Integrating ethnobotany and quantitative methods: A bibliometric analysis of statistical, mathematical, and data science approaches (2016–2025) - Limba, Syella Zignora; Awantara, I Gede; Riansyah, Boy; Idris, Muh Akbar (Ethnobotany Research and Applications, 2026)
Background: The application of quantitative methods, including statistics, mathematics, and data science, in ethnobotanical research represents an emerging interdisciplinary frontier. While many bibliometric studies focus on specific medicinal plants, the overall methodological evolution of computational tools within ethnobotany remains largely unexplored. This study addresses this gap by analyzing the integration of machine learning, multivariate statistics, and network analysis into ethnobotanical research. Methods: A structured bibliometric analysis was conducted using the Scopus database (2016–2025), following PRISMA guidelines. The search targeted titles, abstracts, and keywords linking ethnobotanical concepts with quantitative approaches. A total of 1,275 documents from 337 sources were analyzed using VOSviewer, applying fractional counting and cluster validation to examine publication trends, collaborations, and thematic evolution. Results: The findings show an annual growth rate of 12.05%, increasing from 83 publications in 2016 to 231 in 2025. Polynomial regression indicates an accelerating trend. The results reveal a shift from descriptive approaches toward algorithm-based methodologies, supported by strong international collaboration (30.43%) and dominant contributions from countries such as India and China. Conclusions: The integration of data science is transforming ethnobotany into a predictive discipline. This development supports evidence-based validation of traditional knowledge and improves bioprospecting. Future research should focus on developing a unified Computational Ethnobotany Ontology to support large-scale data integration. Keywords: Ethnobotany; Quantitative Methods; Statistics; Data Science; Bibliometrics; Traditional Knowledge; Machine Learning; Network Pharmacology