What kinds of data science and AI research are featured by C2D3?

C2D3 (the Cambridge Centre for Data-Driven Discovery) features research that applies data science, machine learning and AI across very different fields — from health data and agriculture to aviation and materials discovery. Its news page highlights work led or joined by University of Cambridge researchers, often in collaboration with external partners. The examples below are drawn from the centre's own news listings and show the range of applications it promotes.

Health data research and public trust

One featured item reports on a University of Cambridge-led review finding that children and young people broadly support using health data for research that could improve lives — but want strong safeguards, transparency and greater involvement in decisions about their information. The work sits at the intersection of data science and public policy: the technical potential of health data is presented alongside the conditions people expect before that data is used.

AI for agriculture: crop mapping in Senegal

Another featured project describes an AI tool for mapping smallholder crops that has been tested in Senegal. According to the listing, food in Senegal is mostly grown on small-scale, rain-dependent farms, leaving much of the population vulnerable to climate shocks, while satellite crop-mapping technologies designed to monitor climate impacts on farming are largely beyond the country's reach. The research is an example of adapting data-driven methods to low-resource settings rather than assuming standard infrastructure.

Aviation and climate: AI-driven contrail avoidance

A third item covers Operation Blue Skies, described as a landmark trial to test AI-driven contrail avoidance. A UK-based consortium will conduct what the listing calls the world's first airspace trial aimed at reducing condensation trails, which could greatly reduce aviation's global warming impact. This illustrates how C2D3's featured work connects machine learning to operational and environmental decision-making.

AI for materials discovery

A fourth example notes that Cambridge researchers are contributing to the AI Materials Foundry, a global network launched by Cambridge-based start-up CuspAI to accelerate the discovery and design of next-generation materials. It shows the centre highlighting industry-linked, AI-accelerated science alongside academic research.

What the range tells you

Across these examples, the common thread is collaboration: Cambridge researchers working with partners in the UK and abroad, and with start-ups and consortia, on problems where data science and AI are the enabling method rather than the subject. If you want to see more, the C2D3 site also offers a searchable profile directory of over 300 Cambridge researchers and welcomes external parties as well as University staff and students to join its community.