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 (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) 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 paper 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 need 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 analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.

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A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a "crisis of reproducibility" to the rise of fraudulent, careless and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out that reproducibility is not only interpreted in different ways, but also serves a variety of epistemic functions depending on the research at hand. Given such variation, I argue that the uncritical pursuit of reproducibility as an overarching epistemic value is misleading and potentially damaging to scientific advancement. Requirements for reproducibility, however they are interpreted, are one of many available means to secure reliable research outcomes. Furthermore, there are cases where the focus on enhancing reproducibility turns out not to foster high-quality research. Scientific communities and Open Science advocates should learn from inferential reasoning from irreproducible data, and promote incentives for all researchers to explicitly and publicly discuss (1) their methodological commitments, (2) the ways in which they learn from mistakes and problems in everyday practice, and (3) the strategies they use to choose which research component of any project needs to be preserved in the long term, and how.

Poor provision of information and communication technologies in low

This article documents how biomedical researchers in the United Kingdom understand and enact the idea of “openness.” This is of particular interest to researchers and science policy worldwide in view of the recent adoption of pioneering policies on Open Science and Open Access by the U.K. government—policies whose impact on and implications for research practice are in need of urgent evaluation, so as to decide on their eventual implementation elsewhere. This study is based on 22 in-depth interviews with U.K. researchers in systems biology, synthetic biology, and bioinformatics, which were conducted between September 2013 and February 2014. Through an analysis of the interview transcripts, we identify seven core themes that characterize researchers’ understanding of openness in science and nine factors that shape the practice of openness in research. Our findings highlight the implications that Open Science policies can have for research processes and outcomes and provide recommendations for enhancing their content, effectiveness, and implementation.