Biomedical Knowledge Production in the Age of Big Data
Swiss Science and Innovation Council SSIC
Introduction. Big data are widely seen as a game-changer for social relations, communication and governance around the world. The emergence of big data also promises to revolutionise the production of knowledge within and beyond academia, by enabling new and more efficient ways to plan, conduct, institutionalise, disseminate and assess research. The ability to link and cross-reference datasets coming from different sources is expected to increase the accuracy and predictive power of scientific findings, and help researchers to identify future directions of inquiry. The availability of vast amounts of data provides an incentive to search for intelligent procedures and tools to store, organise and analyse these data, so as to improve the reliability and transparency of scientific knowledge creation. There are therefore strong incentives for researchers to find ways to adequately manage big data at every stage of the research process. This report examines the extent to which the emergence of big data is transforming research practices and outcomes in biomedicine, and the implications of this transformation for researchers in this area. It is divided into three parts. The first part consists of an introduction to the characterisations of big data currently employed in the scientific literature, and the ways in which these definitions fit broader shifts in the status and use of data for research purposes. The second part reviews the opportunities and challenges arising for biomedical researchers in relation to the management and interpretation of big data, focusing particularly on technical, ethical, financial and institutional concerns – and the extent to which such concerns overlap in scientific practice. The third part reflects on how big data infrastructures and skills can be organised at the national and international levels to support a data-centric approach to research, and identifies five principles underpinning the effective and sustainable use of big data in biomedicine.
Swiss Science and Innovation Council
The research underpinning this report was funded by the European Research Council (grant award 335925), the Leverhulme Trust (award RPG-2013-153), the Australian Research Council (award DP160102989), the U.K. Medical Research Council and Natural Environment Research Council (award MR/K019341/1) and the U.K. Economic and Social Research Council (award ES/P011489/1).
This is the author accepted manuscript. The final version is available from the publisher via the link in this record.