CROP: the Crop Research Observation Platform A prototype digital twin of the Growing Underground underground farm operated by Zero Carbon Farms Ltd
Computational social science: Social data bias Investigating social bias in CSS from both a social and computational science perspective
App-based information governance for trustworthy research environments Delivering an open source 'information governance system in a box' to support data protection in trustworthy research environments
Digital twins for high-value engineering applications (DTHIVE) Developing digital twins for engineering applications using modelling, data, and connectivity
Quantitative Urban ANalyTics (QUANT) A land use transportation model simulating the location of employment, population and transport interactions
Impacts of climate change and heat on health Developing models to quantify the impacts of climate change and increased temperatures on human health
Data science education How do we advance pedagogy and policy to develop education in data science and AI?
Data science for engineering structural integrity How can data science be used to improve the assessment of structural integrity across engineering sectors?
AI in resource- and data-constrained environments How can we build effective predictive models on hardware platforms that have limited computational resources and/or when the model training data are by definition always limited in amount?
Media in the digital age What are some of the key opportunities and challenges in the current mediasphere, and how can scientific innovation help?
Omics data generation and analysis group Developing rigorous and robust data science methods for curation, collection and computation of omic data sets
Facilitating responsible participation in data science What guidance do we need to have in place to ensure that stakeholder participation is meaningful, effective, and inclusive?
Trustworthy digital identity How can we understand, evaluate and advance the trustworthiness of digital identity systems?
Game AI How can we advance the state of the art in game AI and apply it to ever more interesting and important problems, in games and beyond?