As the global mobility of researchers increases, many of whom are supported by national funding agencies’ mobility schemes, there is growing interest in understanding the impact of this overseas mobility on knowledge production and networking. This study addresses a relatively understudied mobility—the temporary international mobility of PhD students in STEM fields—and its relation to the establishment of research collaborations between mobile PhD students and researchers at the host university and with other researchers overseas. First, we find that 55% of the participants established relevant international collaborations (i.e., with hosting supervisors and/or others at the hosting university), and we explore these collaboration patterns in detail by taking a novel research propagation approach. Second, we identify features of the visiting period that influence the formation of research collaborations abroad, such as the prestige of the host university, the duration of the international mobility period, the cultural distance, and the number of peer PhD students at the host university. Previous research collaborations between the home and host supervisors are also found to play a crucial role in research collaboration development. Age at the time of mobility is not found to be particularly relevant. We find that female PhD students are less able to benefit from collaborative research efforts than male students. These findings advance the knowledge of global research networks and provide important insights for research funding agencies aiming to promote international research mobility at the doctoral level.

Schedule coordination is the primary form of demand management used at busy airports. At its core, slot allocation involves a highly complex combinatorial problem. In response, optimization models have been developed to minimize the displacement of flight schedules from airline requests, subject to physical and administrative constraints. Existing approaches, however, may not result in the best itineraries for passengers. This paper proposes an original passenger-centric approach to airport slot allocation to maximize available itineraries and minimize connecting times. Because of the uncertainty regarding passenger demand, the proposed approach combines predictive analytics to forecast passenger flows in flight networks from historical data and prescriptive analytics to optimize airport slot assignments in view of flight-centric and passenger-centric considerations. The problem is formulated as a mixed-integer nonconvex optimization model. To solve it, we propose an approximation scheme that alternates between flight-scheduling and passenger-accommodation modules and embed it into a large-scale neighborhood search algorithm. Using real-world data from the Singapore Changi and Lisbon Airports, we show that the proposed model and algorithm return solutions in acceptable computational times. Results suggest that slot-allocation outcomes can be made much more consistent with passenger flows at a relatively small cost in terms of flight displacement. Ultimately, this paper provides a new paradigm that can create more attractive flight schedules by bringing together airport-level considerations, airline-level considerations, and, for the first time, passenger-level considerations. Funding: Financial support from the Civil Aviation Authority of Singapore [Project on Airfield Management and Economics] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1165 .