Bibliometric Insights into Medical Data Science Applications in Genomics: Evidence from Kaggle and Dimensions Datasets

Abstract

BackgroundWhile bibliometrics is widely used to map scientific fields, few studies have integrated non-traditional sources like competitive data science platforms to analyze the gap between theory and practice. This study addresses this methodological gap by developing and applying a dual-source bibliometric framework to the interdisciplinary field of medical genomics data science, aiming to map its complete knowledge lifecycle. MethodsWe analyzed two corpora from 2015 to late 2025; academic literature from Dimensions (n=825 publications) and practical challenges from Kaggle (n=15 competitions). The analysis employed co-authorship, co-citation, and keyword co-occurrence network analysis to map social and intellectual structures. Science mapping techniques, including LDA-based thematic analysis with a strategic diagram (Callons map) and Kleinbergs burst detection algorithm, were used to model the fields evolution and identify emerging research fronts. ResultsPublication growth follows a logistic (S-shaped) model (AIC=60.35, R2=0.998), indicating field maturation. The co-authorship network exhibits a high average clustering coefficient (0.946), confirming a "small-world" structure. Thematic analysis identified 10 distinct topics, with "Core Machine Learning Models" acting as the primary motor theme. A key integrative finding is a measurable diffusion lag for novel architectures like Transformers, where their popularization on Kaggle precedes widespread academic adoption (p<0.05 in lead-lag analysis). Furthermore, open data sharing was found to have a statistically significant positive effect on citation impact (p=0.047). ConclusionsThe integration of practical competition data provides a more nuanced view of a fields trajectory, revealing innovation pathways and translational gaps not visible from academic data alone. This dual-source framework serves as a valuable model for future bibliometric studies of rapidly evolving, application-driven scientific domains.