2. Introduction

This document presents a data sharing framework developed to facilitate sharing of data from Naturalistic Driving Studies (NDS), Field Operation Tests (FOT), pilots and living labs, with the aim of increasing re-use of data for research and validation purposes, and scaling of projects results from testing activities on public roads. It offers guidance on the following topics: (chapter 3) data sharing agreements; (4) data and metadata descriptions; (5) data protection; (6) training; (7) support and research services; (8) financial models; and (9) data governance procedures.

Tailored specifically for research and development projects, the framework facilitates the management and evaluation of Connected, Cooperative and Automated Mobility (CCAM) data, where Automated is the key element (with or without Connected and Cooperative capabilities). The scope of the framework is focused, but not limited to, data collected from automated vehicles tested on public roads (thus excluding water, rail or air transport modes). In many cases, a combination of higher and lower automation data is collected and analysed in a project, why we have lower automation in mind while developing the framework.

Out of the seven topics, five delve into administrative facets, while the chapters on data and metadata, as well as data protection, are technically inclined. These principles, though crafted within a CCAM FOT/NDS/pilot/living lab context, have demonstrated applicability across varied research domains, including engineering and life sciences.

The CCAM Data Sharing Framework facilitates data sharing regardless of the size or content of a dataset. The framework is well-suited for large datasets, including both confidential/commercial data and personal data. Sharing large datasets imposes a greater effort in all the above-mentioned areas compared to a dataset with only a few signals and no video.  However, when sharing smaller datasets, some sections of the framework might become less relevant, depending on the specific dataset. Nevertheless, each chapter provides advice and recommendations applicable to a wide range of situations.

This framework primarily addresses the exchange of semi-confidential data and doesn’t centre on open-access data repositories or those accessed by license. Nevertheless, certain elements of the framework can be relevant to data repositories. For instance, the sections on data description or financial models may apply, as might aspects relevant to other CCAM scenarios like data from tests in restricted areas, test tracks, or simulators. The framework’s main goal is to offer guidelines for bi-lateral data exchanges, whether through direct data transfer, remote desktop access, or federated data sharing. The first release of the Data Sharing Framework was developed in FOT-Net Data project (2014–2016), with a primary focus on FOTs and NDSs. It was later updated for GDPR compliance in the CARTRE project (2017–2020). The current version, emerging from the FAME project (2022–2025), expands its reach to CCAM datasets and incorporates federated data sharing, alongside parallel deployment in the C-ITS domain.