There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results. •Reproducible white matter diagnostics, generalizable to clinical conditions.•Data collected from multiple scanners into a searchable and computable database.•Data analysis software implemented as platform-independent, reproducible containers.•Strategy to minimize measurement variance across a likely span of clinical scanners.