Resumen
With the rapid progress of the development of intelligent transport systems over the last 15 years, the need for testing them in the real world and collecting data about their impact became more and more important. We have seen a fast growth in the number of Field Operational Tests (FOT) and Naturalistic Driving Studies (NDS) performed worldwide. The need to better understand the benefits of safety systems and the factors behind the occurrence of incidents and accidents have been a main driving force and the data has therefore been collected through naturalistic driving by volunteer drivers. As the number of different datasets has increased and so also the awareness of the substantial effort and funding needed to run these FOT/NDS, the interest in data sharing has increased worldwide. The availability of a common Data Sharing Framework (DSF) could highly facilitate a larger use of the collected FOT/NDS data. The FOT-Net Data project has developed such a framework, in collaboration with a variety of stakeholders from Europe, the US, Japan and other countries. The seven topics addressed by the DSF are (1) project agreements, (2) data and metadata descriptions, (3) data protection, (4) training, (5) support and research services, (6) financial models and (7) applications procedures. Many of the topics are general and can be used for other types of transport research data as well. There remain challenges to make data sharing possible on a global scale. Some of these are: the project funding schemes, leading to multiple schemas of ownerships of data, and the legal settings in different countries. On a technical level, the documentation of datasets and of the metadata describing the test is not always sufficient. Furthermore, new projects need to be made aware of the importance of inserting the pre-requisites for data sharing into the different project agreements right from the start. This paper describes the content of the DSF with its hands-on recommendations on how to prepare for and perform data sharing of transport research data. It also presents the status of a use case, implementing the DSF into the European project UDRIVE.