STARRY (STARs that ‘R’ Young) is a twin site EID that will provide training to 2 inexperienced researchers in the development of sophisticated research tools in order to exploit, interpret and analyse astronomical data from state-of-the-art observatories in the field of star formation. The joint training programme is provided by world experts in stellar astrophysics at the University of Leeds, UK and in developing, maintaining and exploiting space science data archives at ISDEFE in Madrid, Spain. The programme is extremely timely in exploiting the astrophysical data that are now becoming available from the European Space Agency’s (ESA) mission GAIA and other, mainly European-led, sky surveys. These new developments allow us to answer a current and significant question in the field of the formation of stars, namely: “do massive stars form in clusters?”. The science question requires a particular, novel, functionality to interpret and analyze these new data, and the programme brings together both the academic sector and the non-academic sector to achieve this. The tools which will be developed in the Work Packages in order to address the current scientific question have the potential to enable future studies in many branches of stellar astrophysics, and beyond. Throughout, emphasis is put on skilling a new generation of researchers to work and communicate cross-borders which is vital for the future competitive health of this sector. In short, the need for the development of new analysis tools is identified by a clear and current research question. Its implementation will have a long term benefit to ISDEFE which will ultimately lead to a competitive commercial advantage for the company across Europe. In addition, a new generation of researchers is provided with a research and transferable skillset designed to exploit the considerable European investment in space.

The Flightpath 2050 report envisages a passenger-centric air transport system thoroughly integrated with other transport modes, with the goal of taking travellers from door to door predictably and efficiently. However, ATM operations have so far lacked a passenger-oriented perspective, with performance objectives not necessarily taking into account the ultimate consequences for the passenger. There is a lack of understanding of the impact of passengers’ behaviour on ATM and vice versa. Research in this area has so far been constrained by the limited availability of behavioural data. The pervasive penetration of smart devices in our daily lives and the emergence of big data analytics open new opportunities to overcome this situation: for the first time, we have large-scale dynamic data allowing us to test hypotheses about travellers’ behaviour. The goal of BigData4ATM is to investigate how these data can be analysed and combined with more traditional demographic, economic and air transport databases to extract relevant information about passengers’ behaviour and use this information to inform ATM decision making processes. The specific objectives of the project are: 1. to integrate and analyse multiple sources of passenger-centric spatio-temporal data (mobile phone records, data from geolocation apps, credit card records, etc.) with the aim of eliciting passengers’ behavioural patterns; 2. to develop new theoretical models translating these behavioural patterns into relevant and actionable indicators for the planning and management of the ATM system; 3. to evaluate the potential applications of the new data sources, data analytics techniques and theoretical models through a number of case studies, including the development of passenger-centric door-to-door delay metrics, the improvement of air traffic forecasting models, the analysis of intra-airport passenger behaviour and its impact on ATM, and the assessment of the socio-economic impact of ATM disruptions.