1. Understanding Data-Intensive Science Sabina Leonelli Egenis & Department of Sociology and Philosophy s.leonelli@exeter.ac.uk

2. Context: The Rise of Data-Intensive Science and the Difficulties in Making Sense of It New ways to produce, store and disseminate data are affecting how scientists work, think and collaborate: a qualitative shift to ‘data-intensive science’ Despite heated debates on the significance and impact of big data and data management tools (e.g. internet databases), no clear characterization of data-intensive science --- difficult given the distinctive methods, objects, materials, aims and technologies characterizing each of fields involved. Further, no systematic study of how this approach affects existing philosophical views on scientific epistemology, as expressed within contemporary philosophy of science and underlying science policy

3. Towards a Philosophy of Data-Intensive Science Aim: systematically analyze whether and how the epistemology of science is changing in the digital age, through an empirical, comparative study of data-intensive practices and their results across different scientific areas and time periods Method: philosophy of science based on empirical studies of science (‘philosophy of science in practice’) -Historical and sociological research on scientific practices -Collaborations with natural scientists (e.g. plant scientists, bioinformaticians and system biologists)

4. Specific Topics Unsustainability of internet databases for biomedical data in the long term: need for new funding & support mechanisms Huge impact of databases and classification systems on how data are interpreted and re-used: not yet fully recognised by biologists Shifts in division of labour in scientific research: role of computer scientists and database curators? Key role of model organism communities, e.g. Arabidopsis thaliana, in driving future research in biology as well as how biologists think of ‘organisms’

5. Activities and Results Publications in science journals and in philosophy, history and social studies of science; monograph in progress. Forthcoming special issues on the characteristics of data-intensive biology, large-scale scientific research, and translational research. Science policy: -Consultations with funding bodies (e.g. BBSRC and NSF) -Consultations with United Nations and EU on impact of digital technologies on science and society (Global Young Academy)