Exploiting use of big data to prevent and treat disease

The Big Data Institute (BDI) is a state-of-the-art building at the University of Oxford's Old Road Campus which opened in 2017. This interdisciplinary research centre focuses on the analysis of large, complex, heterogeneous data sets for research into the causes and consequences, prevention and treatment of disease. To this end, BDI researchers develop, evaluate and deploy efficient methods for acquiring and analysing information for large clinical research studies. These approaches are invaluable in identifying the associations between lifestyle exposures, genetic variants, infections and health outcomes around the globe. The BDI comprises around 250 researchers (approx. 20 research groups) drawn from a wide range of departments and forms an analytical hub, deeply connected to the wider experimental and clinical community in Oxford and beyond.

The creation of the BDI was an important strategy for the Medical Sciences Division in Oxford for some years. To deliver the vision required the development of an implementation of a tailored plan that enabled the building to be built, the IT infrastructure to be purchased, a new group of principal investigators to be recruited, access to data to be delivered, and to operate in a financially sustainable way. The University secured significant capital and programme funding from research funding partners, foundations and private individuals.


However, without the availability of quality-related (QR) funding the deliverability of the vision would have been compromised. Firstly, QR contributes to the unfunded costs of externally-funded research projects and programmes (where most funders supporting research in the BDI do not fund the full economic costs of research) and so has allowed a financially sustainable operation to be designed and delivered. Secondly, QR is used within the BDI to support critical underpinning and enabling activity essential to the BDI’s activities, namely the curation of the massive, heterogeneous datasets on which its research depends. Whilst funding for research using such datasets is available from funding bodies, funding for the resources (people and infrastructure) to convert raw data into the datasets that can support multiple research endeavours is significantly harder to secure.

QR is therefore an essential element in enabling the use of Big Data methods which are transforming the scale, efficiency and impact of large-scale clinical research.

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