Smart cities critical infrastructures are nowadays one of the core applications areas for digital twins (hence, the definition of digital twins of critical infrastructures, DTCCIs), which are virtual replicas that allow to simulate and analyse the behaviour under different conditions of physical assets as pivotal as transportation, energy, or water systems, thus supporting their effective management. DTCCIs are especially helpful in the decision-making process as they enable operators to identify potential issues and optimise system performances without disrupting the services provided to the public. In such a scenario, the role of data is pivotal as the accuracy and usefulness of DTCCIs strongly depend on data availability and data quality in order to achieve a true digital resilience of critical infrastructures.

Therefore, the promising integration with dataspaces, intended as virtual spaces for managing and aggregating heterogeneous real-time and historical data sources, can further enhance effectiveness and applicability of DTCCIs. Several current initiatives aimed at proposing dedicated architectures (as the International Data Spaces, IDS), collaborative data sharing environments (as the European data strategy), and architectural standards (as the GAIA-X initiative) enabling trusted data exchange in accordance with European data protection guidelines, are nowadays paving the way to adopt dataspaces as a fundamental building block of truly data-driven DTCCIs.

The enabling role of dataspaces is rapidly gaining momentum in situations where centralised cloud-based data storage solutions have to be integrated with highly-distributed edge data processing. Furthermore, data sovereignty requirements in dataspaces for DTCCIs constitute another engaging challenge, as it is crucial to ensure that DTCCI data are secured and protected from unauthorised access, misuse, or deletion, particularly when the right of a country or an organisation to keep the ownership and control over its own data is at stake. Moreover, the data generated by a DTCCI usually need to be shared with other stakeholders, such as national and international regulatory authorities or maintenance contracting companies, and data sovereignty must be ensured without hindering data interoperability, sharing, and usage.

Consequently, high-quality dataspaces capable of integrating and managing extremely variegated data sources, allowing data-interoperability among multiple systems and organisations, and supporting data- intensive processing and analytics are needed, with a specific focus on scalability, privacy, and security requirements.

This workshop aims to bring together researchers and practitioners not only from computer science and data science but also from the industry sector and from governmental bodies. The workshop will represent a fruitful opportunity for discussing the latest research developments and the ongoing challenges in the field, as well as for fostering collaborations and networking.