The Data Theme brings together researchers, knowledge and facilities from our university and research organisations working in collaboration with our partner organisations to design data collection and analytics for orebody characterisation. There are challenges across the whole data spectrum for low-grade deposits, from acquisition to modelling and interpretation, which subsequently affects how efficiently low-grade ores can be mined. The Data Theme includes data acquisition (data collection for meaningful mineral exploration), integration (integrating existing and collected data and knowledge) and models (orebody modelling) empowered by multi-disciplinary data. Existing methods mainly focus on single-source data (such as drilling) and often require substantial human intervention and they are costly and time-consuming but also limited in scope by not providing high accuracy. This Theme, therefore, aims to design an automated engine platform that will utilise diverse multi-disciplinary data inputs to identify, characterise and develop the orebody models for various strategic minerals in subsurface environments. This will leverage data integration, data preprocessing and deep learning models to support real-time, precise subsurface characterisation and adaptive decision-making, allowing for significantly more efficient, targeted extraction techniques that optimise resource recovery while minimising waste and environmental impacts.