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The understanding of complex physical or biological systems nearly alwaysrequires a characterisation of the variability that underpins these processes.In addition, the data used to calibrate such models may also often exhibitconsiderable variability. A recent approach to deal with these issues has beento calibrate populations of models (POMs), that is multiple copies of a singlemathematical model but with different parameter values. To date thiscalibration has been limited to selecting models that produce outputs that fallwithin the ranges of the dataset, ignoring any trends that might be present inthe data. We present here a novel and general methodology for calibrating POMsto the distributions of a set of measured values in a dataset. We demonstratethe benefits of our technique using a dataset from a cardiac atrialelectrophysiology study based on the differences in atrial action potentialreadings between patients exhibiting sinus rhythm (SR) or chronic atrialfibrillation (cAF) and the Courtemanche--Ramirez--Nattel model for human atrialaction potentials. Our approach accurately captures the variability inherent inthe experimental population, and allows us to identify the differencesunderlying stratified data as well as the effects of drug block.

Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology