Knowledge Management

As biology and science in general has moved from small to big data, the knowledge involved in leveraging this data has become more complex and more voluminous. Understanding how to manage and represent big data is a challenge in itself, but one which presents opportunities to discover new knowledge.

Data Integration

Our group is developing novel approaches that allow biological heterogeneous and complex data from a wide variety of sources to be combined. These data can then be mined to generate hypotheses directly or can be used to provide a framework for the development of dynamic models.

We have developed schemes, information models and tools to help organise and standardise the data at the point of collection, for genomic and neuroscience data.

Data Analysis

We have analysed many biological databases using their history and provenance as quality indicator. We have build workflow systems, and visualisation systems to display and explore this form of data.


We have developed novel technology for ontology development, used it to describe complex parts of biology, including karyotypes and telomere activity. We have integrated metadata into knowledge publication systems on the web, exposing their knowledge for machine interpretation. We have contributed to foundational ontologies, and the theoretical underpinnings of formal ontology.