Data spaces

Data spaces refer to structured and managed environments where data from various sources is securely stored, shared, and utilized for AI and robotics applications within smart and sustainable cities. These data spaces are the project's core technology, enabling participants to access and leverage high-quality data for testing, experimentation, and validation of AI technologies.

Data spaces support interoperability, ensuring that data from different sources can be combined and used while complying with regulations such as the GDPR and other EU directives. They provide the necessary infrastructure for managing data in a way that supports ethical considerations, cybersecurity, and the broader goals of creating a more digital and sustainable urban environment.

In CitCom.ai, data spaces are pivotal in accelerating innovation by facilitating collaboration among different stakeholders. They offer a secure and compliant framework for data exchange, ensuring that the AI solutions developed within the project are both reliable and aligned with European standards.

Basically, a data space must be formed, at least, by the following components (Minimum Data Space Reference):

• Trust Anchor (TA): Responsible for managing trust in the data space. It is the manager of the identities of the different elements of the data space and of managing the trust in them. At least one TA shall exist in the data space, managed by the organization in charge of the data space.

• Data Space Connector (DSC): Responsible for managing the communication between the different elements of the data space. It oversees managing authentication, authorization and data access control. There must be at least two DSCs, one per organization, to be able to affirm that a data space exists.

More details

Overview of open-source data spaces connectors: Overview section