The connectivity of the human brain is fundamental to understanding the principles of cognitive function, and the mechanisms by which it can go awry. To that extent, tools for estimating human brain networks are required for single subject, group level, and cross-study analyses. We have developed an open-source, cloud-enabled, turn-key pipeline that operates on (groups of) raw di usion and structure magnetic resonance imaging data, estimating brain networks (connectomes) across 24 di erent spatial scales, with quality assurance visualizations at each stage of processing. Running a harmonized analysis on 10 di erent datasets comprising 2,295 subjects and 2,861 scans reveals that the connectomes across datasets are similar on coarse scales, but quantitatively di erent on fine scales. Our framework therefore illustrates that while general principles of human brain organization may be preserved across experiments, obtaining reliable p-values and clinical biomarkers from connectomics will require further harmonization e orts.