  A major data pre-processing step for large, multi-site studies is to handle
site effects by harmonizing data, generating a dataset that enables more
powerful analyses and more robust algorithms. There is a wide variety of data
harmonization techniques, but there are few tools that streamline the process
of harmonizing data, comparing across techniques, and benchmarking new
techniques. In this paper, we introduce HArmonization BEnchmarking Tool
(HABET), an open source tool for generating harmonized images and evaluating
the performance of different harmonization algorithms. To demonstrate the
capabilities of HABET, we harmonize diffusion MRI images from the Adolescent
Brain and Cognitive Development (ABCD) study using two different approaches,
and we compare their performance.
