Since the early 2000s, the transport and mobility sectors have seen significant growth in Field Operational Tests (FOTs) and Naturalistic Driving Studies (NDSs), worldwide. These studies have focused on testing the feasibility and societal impacts of vehicle-to-X connectivity and advanced driver assistance systems (ADAS), among other things. Over the past decade, the field of Cooperative, Connected, and Automated Mobility (CCAM) has emerged as the leading area of research in the sector. While the initial CCAM tests were conducted on a small scale to ensure safety and test automation technology, the next wave of tests is set to match the previous FOTs in scope.

Arranging these tests is a resource-intensive task that often takes years to complete and requires multiple partners’ involvement. These studies produce valuable datasets that could be useful for other organizations and purposes beyond the original project plan. Consequently, there is a growing interest in data sharing to extract more results from the large data collection efforts.

To facilitate greater use of the collected test data, this document presents a data sharing framework. The framework integrates data sharing pre-requisites into project agreements from the start, suggesting procedures and templates. Using a common framework promotes harmonization across projects and ensures that basic criteria are met regarding data protection and possibilities for reuse.

The challenges, both legal and technical, when sharing data has limited its potential. As a response to that, European companies, authorities and research institutes, have set the focus on federated data sharing. The principle is that by establishing trust between stakeholders within a specific domain or community, giving the data providers control of who has access to which data for which purpose, more data can be shared. The approach has the potential in unlocking relevant data for research purposes and is explored in a CCAM-context in this document.

The proposed framework support organizations setting up new tests, in highlighting important data-related topics, enabling them to focus on the primary content of their research, such as research questions and study design. Furthermore, researchers, who want to reuse already collected datasets or multiple datasets in the same research project, can utilize a standard application procedure, rely on widely accepted training, and plan for the costs associated with using a particular dataset. This document covers key aspects of data management, including detailed data and metadata descriptions, data-protection guidelines, insights on personal data and IPR training, and outlines of support and research services and financial considerations. It’s designed as a comprehensive guide for both new and experienced researchers in the CCAM field, promoting effective data management practices and collaboration.