Quantitative methods, big data and the digital ecosystem

The natural world is an inexhaustible source of challenging data. For instance, geologists seeking to infer the spatial distribution of a mineral or fuel resource through spatial maps, and evolutionary ecologists studying the response of a species to habitat change, are equally concerned with access to high-quality spatio-temporal data, and an optimal means for their analysis. The cultural affinity with Engineering is obvious. Mechanical engineers optimize heat-sink designs based on legacies of spatio-temporal heat-flow data, while video processing experts track the movements of an object in high-volume video data.  All these researchers confront the challenge of massive data, aiming to design optimal decisions amid uncertainty. These are the same priorities informing the current data analytics convergence agenda in a wide range of data-intensive industry contexts. The inspiration flows both ways, with evolutionary algorithms inspired by the behaviour of natural systems providing new tools for engineering. The E3 will greatly enhance collaboration on quantitative methodologies for - and by - the natural sciences. This will lead not just to enhanced observational and data-structuring aptitudes, but to new technologies and innovation flows.