<jats:title>ABSTRACT</jats:title><jats:p>Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.</jats:p><jats:sec><jats:title>HIGHLIGHTS</jats:title><jats:list list-type="bullet"><jats:list-item><jats:p>We harmonised measures of WMHs across two studies on healthy ageing</jats:p></jats:list-item><jats:list-item><jats:p>Specific pre-processing strategies can increase comparability across studies</jats:p></jats:list-item><jats:list-item><jats:p>Modelling of biological differences is crucial to provide calibrated measures</jats:p></jats:list-item></jats:list></jats:sec>