What difference does quantity make? On the epistemology of Big Data in biology

Abstract

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.

Post-archival genomics and the bulk logistics of DNA sequences

“…We see them less as organisational errors, infrastructural failure or ongoing epistemological tensions in biology (Leonelli, 2014) and more as signs of the divergent patterns of circulation of sequence data in post-archival genomics. Recent work in science and technology studies (STS) has described how 'social distance' affects the movement of data, and occasions the production of metadata.…”

“…In science and business, databases epitomise the organisational practices and logics that knit aggregates of people, things and transactions together in vast accumulations. It is no surprise that the growth in biological data infrastructures such as sequence databases has been analysed in terms of the tensions between the economically loaded ambitions of large-scale genomics research and the ongoing dispersed localised practices of genomic scientists, many of whom only partially conform to the demands and rhythms of global technoscience (Leonelli, 2013;Leonelli, 2014). If the global ambitions of genomic data infrastructures exemplify the encounter between capitalist economies and biology, the dispersed localised practices suggest at least some ongoing irreducibility or resistance to the patterns of circulation typically associated with biocapital.…”

Data Labours: How the Sequence Databases GenBank and EMBL-Bank Make Data

“…With reference to model organism databases, Leonelli has drawn attention to how data travel and are "packaged" along their route from phenomenon to research paper (Leonelli, 2009). This travel consists of three stages: de-contextualisation in which data are removed from their original context of production; re-contextualisation in which data are readied for uptake by other communities; and re-use in which data are applied in new research contexts (Leonelli, 2014). Packaging is rarely a smooth activity as components and communities continually evolve or at times dissolve.…”

Ship space to database: emerging infrastructures for studies of the deep subseafloor biosphere

“…New instruments are capable of collecting data at greater volume, variety, and velocity than ever before. Consequences of these developments include the emergence of new infrastructures, changes in epistemologies, and new forms of collaborative work (Kitchin, 2014;Leonelli, 2014).…”