AI4Knowledge: Shaping the Future of Research Data Systems

Research Data Management (RDM) faces mounting challenges in academia due to increasing data volumes, heterogeneity, and the limitations of manual, fragmented processes. These issues hinder data integrity, scalability, and effective resource utilization. This paper investigates how Artificial Intelligence (AI)—particularly Machine Learning (ML) and Deep Learning (DL)—can address these limitations by enabling automation, improving data quality, and supporting decision-making. After outlining current deficits in RDM, we propose AI-based methods such as automated classification, anomaly detection, and predictive analytics for workflow optimization. A real-world use case demonstrates the practical application and measurable benefits of these approaches, showing how AI can transform RDM into a more efficient, intelligent, and adaptive system.