 One debated issue is the extent to which big data will radically change or even revolutionize scientific methods, shifting the focus from hypothesis-driven inquiry to data-driven discoveries (Mazzocchi 2015). Whereas some have seen data-intensive strategies as a theory-free approach (Allen 2001), others have argued that theory plays different, although not less important, roles in data-intensive science (Leonelli 2016). In particular, big data biology presents new opportunities for eliminative inferences and exploratory experiments (Ratti 2015). Moreover, since the aim of bio-ontologies is to provide a standardized language and repository for shared information, choices concerning vocabulary and categorizations become important philosophical topics, including whether standardization is compatible with local data uses (Leonelli 2016). Important philosophical questions include how choices concerning collection, sampling, standardization, visualization, and interpretation of data affect the result of the analysis. Specifically, the results of correlation-based methods such as GWAS are sensitive to sampling procedures and specific statistical thresholds, and the clinical validity and utility of genetic biomarkers is a debated issue (e.g., Hey 2015; Maher 2008; McPherson and Tybjaerg-Hansen 2016).