TITLE:
Adaptive Evolution of Data-Driven Life Science Research: A Complex Adaptive Systems Analysis of Bioeconomy Themes
AUTHORS:
Hanmei Jin, Jianhui Li
KEYWORDS:
Data-Driven Research, Life Sciences, Bioeconomy, Complex Adaptive Systems, BERTopic, Thematic Evolution
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.10,
October
28,
2026
ABSTRACT: Large-scale data and algorithmic tools are changing how life science research problems are organized and redirected. Using bioeconomy research as an empirical window, this study examines the adaptive evolution of research attention under data-driven conditions. It analyzes 5429 archived model-input rows from the China National Knowledge Infrastructure and the Web of Science Core Collection between 2014 and October 2024, and reports revised statistics for 4937 retained text-year records after an exact-duplicate check. BERTopic is used to identify thematic structures and trace temporal changes in Chinese- and English-language corpora, and the observed patterns are interpreted through complex adaptive systems theory. The results show distinct but internally concentrated configurations. Chinese research centers on industrial innovation and application-oriented issues, while English-language research centers on the circular bioeconomy. Growth during 2020-2022 is concentrated mainly in these existing cores. Smaller topics display limited, corpus-specific variation. Three descriptive comparison windows show thematic persistence together with localized changes in research attention. The observed patterns are interpreted through operability boundaries, positive-feedback stabilization, and window-period acceleration. The study connects topic-level evidence with changes in the organization of data-driven life science research and identifies institutional priorities for standards, collaboration, and evaluation.