The Epistemology of Data-Intensive Science (DATA SCIENCE)
The Epistemology of Data-Intensive Science
Date du début: 1 mars 2014,
Date de fin: 28 févr. 2019
"This project aims to develop a new ‘philosophy of data-intensive science’ that clarifies how research practices are changing in the digital age, and examines how this affects current understandings of scientific epistemology within the philosophy of science and beyond.The scale of scientific data production has massively increased in recent times, raising urgent questions about how scientists are to transform the resulting masses of data into useful knowledge. A technical solution to this problem is offered by technologies for the storage, dissemination and handling of data over the internet, including online databases that enable scientists to retrieve and analyse vast amounts of data of potential relevance to their research. These technologies are having a profound effect on what counts as scientific knowledge and on how that knowledge is obtained and used. This is a step change in scientific methods, which scientists refer to as ‘data-intensive’ research.Surprisingly, the characteristics and philosophical implications of this emerging way of doing science have not yet been extensively and systematically analysed. This project aims to fill this gap by combining the analytic apparatus developed by philosophers of science with empirical, qualitative methods used by social scientists to investigate cutting-edge scientific practices. Accordingly, Phase 1 of the project will investigate how the use of online databases is currently affecting research practices and outcomes in two areas: plant science and biomedicine. Phase 2 will then build on these empirical results to analyse how data-intensive methods challenge existing philosophical understandings of the epistemic role of data, theory, experiments and division of labour in science. Through the analysis of how these four key components, the PI will produce a systematic assessment of the implications of the rise of data-intensive research for how science is organised, conducted and assessed."
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