The growing importance of data spaces, which promote the exchange of data across various domains, has generated considerable attention. The existing body of research on the adoption of data space and federated data sharing has predominantly focused on technological factors. However, there is an increasing acknowledgment of the need for a more holistic perspective. This study presents an exploratory investigation based on the Grounded Theory methodology-based survey, that incorporates interviews with professionals from different sectors. Through this research, the most significant data key factors that drive the data space design as support for an Urban Digital Twin Platform. The findings obtained from this research indicate 4 main characteristics and 11 subcategories that are obtained in compliance with the European reference specifications in data space design. These findings can be used for matching the needs perceived from professionals with the European reference specifications. Regarding the matching results, some final considerations are provided.

Green energy production is typically decentralized, and the ecosystem of production, transmission, and distribution differs significantly from centralized systems. Therefore, ensuring the resilience of green energy infrastructure demands a distinct approach, particularly regarding the security and safety aspects of these CIs. Green Energy CIs have less inherent protection, along with ancillary protection facilities compared to conventional power plants. This underscores the need to leverage AI to enhance the safety and security of green energy infrastructures, providing efficient and cost-effective solutions. This study aims to provide a comprehensive overview of AI implementations for enhancing the security and safety of green energy. Although this study is a work in progress, the present article will specifically delve into the resilience aspects of green energy infrastructures. Given the focus on AI implementation and data-driven solutions, we approach energy systems from a cyber-physical and societal perspective, emphasizing their broader impact on society. The ongoing study has unveiled significant improvements in resilience through the application of AI methods and data-driven models, such as machine learning, deep learning, neural networks, multiagent systems, big data, and data mining. Furthermore, we explore the challenges associated with integrating AI into green energy systems and investigate its various applications. This exploration aims to identify key features that will guide the development of novel approaches to enhancing the resilience of green energy systems through AI-based solutions for security and safety. Finally, results show a significant gap in the safety applications of AI. It received the least attention in the articles While the term ”safety” is frequently mentioned, even when the article’s primary focus is not on safety applications.