A major societal challenge is to develop data science methods to capture the multiple and complex temporalities (e.g. speed, shape, direction, momentum of change) that are necessary to design successful policy planning and decision-making. This project addresses this challenge head-on by testing and developing how to use linked data to capture the multiple time-scales within a system for policy purposes. After all, knowing where and when parts of a system are slow or stuck or changing ‘with’ or ‘without’ other parts of a system is a key factor in being able to adequately model complex future trends. The project is methodological in its focus and aims to explore and test how different network methods might be used to develop new kinds of data science for social policy purposes.
The project is led by Dr Emma Uprichard as part of her Alan Turing Fellowship, which is focused on developing new data science methods for complex policy. Bazil Sansom and Sam Martin are part-time postdoctoral fellows on the project. Bazil brings his expertise in network science and policy-making and Sam brings her digital methods expertise.