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