I propose a framework that explicates and distinguishes the epistemic roles of data
and models within empirical inquiry through consideration of their use in scientific practice.
After arguing that Suppes’ characterization of data models falls short in this respect, I discuss
a case of data processing within exploratory research in ...
I propose a framework that explicates and distinguishes the epistemic roles of data
and models within empirical inquiry through consideration of their use in scientific practice.
After arguing that Suppes’ characterization of data models falls short in this respect, I discuss
a case of data processing within exploratory research in plant phenotyping and use it to
highlight the difference between practices aimed to make data usable as evidence and
practices aimed to use data to represent a specific phenomenon. I then argue that whether a
set of objects functions as data or models does not depend on intrinsic differences in their
physical properties, level of abstraction or the degree of human intervention involved in
generating them, but rather on their distinctive roles towards identifying and characterizing
the targets of investigation. The paper thus proposes a characterization of data models that
builds on Suppes’ attention to data practices, without however needing to posit a fixed
hierarchy of data and models or a highly exclusionary definition of data models as statistical
constructs.