Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments (...) generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods. (shrink)

Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.

The idea that research should become more interdisciplinary has become commonplace. According to influential commentators, the unprecedented complexity of problems such as climate change or the social implications of biomedicine demand interdisciplinary efforts integrating both the social and natural sciences. In this context, the question of whether a given knowledge practice is too disciplinary, or interdisciplinary, or not disciplinary enough has become an issue for governments, research policy makers and funding agencies. Interdisciplinarity, in short, has emerged as a key political (...) preoccupation; yet the term tends to obscure as much as illuminate the diverse practices gathered under its rubric. This volume offers a new approach to theorising interdisciplinarity, showing how the boundaries between the social and natural sciences are being reconfigured. It examines the current preoccupation with interdisciplinarity, notably the ascendance of a particular discourse in which it is associated with a transformation in the relations between science, technology and society. Contributors address attempts to promote collaboration between, on the one hand, the natural sciences and engineering and, on the other, the social sciences, arts and humanities. From ethnography in the IT industry to science and technology studies, environmental science to medical humanities, cybernetics to art-science, the collection interrogates how interdisciplinarity has come to be seen as a solution not only to enhancing relations between science and society, but the pursuit of accountability and the need to foster innovation. Interdisciplinarity is essential reading for scholars, students and policy makers across the social sciences, arts and humanities, including anthropology, geography, sociology, science and technology studies and cultural studies, as well as all those engaged in interdisciplinary research. It will have particular relevance for those concerned with the knowledge economy, science policy, environmental politics, applied anthropology, ELSI research, medical humanities, and art-science. (shrink)