 It is no coincidence that much philosophical work on the relation between causal and predictive knowledge extracted from big data comes from the philosophy of the life sciences, where the absence of axiomatized theories has elicited sophisticated views on the diversity of forms and functions of theory within inferential reasoning. Moreover, biological data are heterogeneous both in their content and in their format; are curated and re-purposed to address the needs of highly disparate and fragmented epistemic communities; and present curators with specific challenges to do with tracking complex, diverse and evolving organismal structures and behaviours, whose relation to an ever-changing environment is hard to pinpoint with any stability (e.g., Shavit &amp; Griesemer 2009). Hence in this domain, some of the core methods and epistemic concerns of experimental research—including exploratory experimentation, sampling and the search for causal mechanisms—remain crucial parts of data-centric inquiry.