This deliverable presents the preliminary version of the NOUS Data Life Cycle Framework (DLCF), developed as part of Task 2.4 under Work Package 2. It outlines the first three phases of the proposed six-phase lifecycle: Data Creation/ Collection/ Acquisition, Data Processing/ Transformation, and Data Storage. The remaining phases will be addressed in the final version of the deliverable. The NOUS DLCF aims to provide a comprehensive, standardized, and secure approach to data management across multiple sectors including mobility, energy, crisis management, and scientific computing. The framework integrates advanced technologies such as Artificial Intelligence (AI), High-Performance Computing (HPC), Blockchain (BC), and Federated Learning (FL) to optimize data handling and ensure compliance with GDPR, ISO 27001, and emerging AI governance regulations. Each use case demonstrates how the DLCF supports sector-specific requirements while maintaining a unified methodology: • Mobility: Real-time object detection through Edge AI and secure blockchain-based audit trails reduce latency and enhance road safety. • Energy: Predictive analytics powered by ML and Quantum Computing enable efficient forecasting, reinforced by federated training and blockchain-secured integrity. • Crisis Management: The CRIMSON platform leverages geospatial analytics and live video feeds, ensuring traceability and compliance via blockchain logging and robust access controls. • Scientific Research: HPC optimization, AI-guided workload balancing, and long-term digital preservation techniques ensure data traceability, reproducibility, and FAIR-compliant reuse. By formalizing each stage of the data lifecycle and incorporating best practices for standardization, security, and traceability, NOUS lays the groundwork for a scalable and interoperable infrastructure. This framework is not only technologically robust but also aligned with European data policy goals, facilitating responsible innovation and future integration across diverse digital ecosystems.
Ronco et al. (Tue,) studied this question.