Towards a Taxonomy of the Model-Ladenness of Data

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are data-laden and data are model-laden. In this article I elaborate and defend the second, more controversial, component of the symbiosis view and construct a taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than so-called raw data and, hence, beneficially serve the epistemic aims of science.