While there is intense interest in the epistemic, economic, aesthetic, social and political values associated with ‘big data’, (Savage & Burrows, 2007; Latour et al., 2012; Halfpenny & Proctor, 2010; Hey et al., 2009; Anderson, 2008), there are few examples of how to study ‘big data’ itself as a material-social process (cf McNally, Mackenzie, Hui, Tomomitsu, 2012).

As researchers at the ESRC investment Cesagen, we address this lacuna using NGS as a case study. The dramatic increase in genomics data is usually attributed to the advent of NGS instruments. This is typically conveyed using Moore's Law-style graphics of falling costs and greater speeds of NGS sequencing. However, whilst these renderings make instruments and databases highly visible, they tend to hide the diverse factors and relations that shape the terrain and channel the flow of data between instruments and databases. Hence there is a need, in genomics and more generally, to develop empirical methods to coax the flows of big data into greater relief.

In partnership with genomics, bioinformatics and computer science researchers (see below), we will bring three disparate datasets together in order to produce a socio-technical topography of NGS data flows 2007-2012.