AI-Driven Research Data Management
Abstract
The rapid growth of scientific data from sensors, simulations, genomic research, high-performance computing, and collaborative research platforms has transformed the requirements of modern research data management (RDM). Traditional manual methods for organising, curating, and retrieving data are no longer sufficient to manage the scale and complexity of today's scientific ecosystems. This chapter explores how artificial intelligence technologies such as machine learning, deep learning, natural language processing, knowledge graphs, large language models, federated learning, and explainable AI are reshaping RDM into intelligent and adaptive infrastructures. It examines AI-enabled capabilities including automated data acquisition, semantic enrichment, predictive analytics, workflow automation, and responsible governance. The chapter also highlights applications across genomics, climate science, medicine, physics, social sciences, and materials science while discussing key technical, ethical, and organisational challenges.
