 The question of how data are defined and identified, however, is crucial for understanding the role of big data in scientific research. Let us now consider two philosophical views—the representational view and the relational view—that are both compatible with the emergence of big data, and yet place emphasis on different aspects of that phenomenon, with significant implications for understanding the role of data within inferential reasoning and, as we shall see in the next section, as evidence. The representational view construes data as reliable representations of reality which are produced via the interaction between humans and the world. The interactions that generate data can take place in any social setting regardless of research purposes. Examples range from a biologist measuring the circumference of a cell in the lab and noting the result in an Excel file, to a teacher counting the number of students in her class and transcribing it in the class register. What counts as data in these interactions are the objects created in the process of description and/or measurement of the world. These objects can be digital (the Excel file) or physical (the class register) and form a footprint of a specific interaction with the natural world. This footprint—“trace” or “mark”, in the words of Ian Hacking (1992) and Hans-Jörg Rheinberger (2011), respectively—constitutes a crucial reference point for analytic study and for the extraction of new insights. This is the reason why data forms a legitimate foundation to empirical knowledge: the production of data is equivalent to “capturing” features of the world that can be used for systematic study. According to the representative approach, data are objects with fixed and unchangeable content, whose meaning, in virtue of being representations of reality, needs to be investigated and revealed step-by-step through adequate inferential methods. The data documenting cell shape can be modelled to test the relevance of shape to the elasticity, permeability and resilience of cells, producing an evidence base to understand cell-to-cell signalling and development. The data produced counting students in class can be aggregated with similar data collected in other schools, producing an evidence base to evaluate the density of students in the area and their school attendance frequency.