LAUSR

Data sharing improves epidemiology research, but sharing data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study that has shared its data and any researcher who has received shared data. Temprosa and Moore et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX)) describe how the COnsortium of METabolomics Studies (COMETS) developed and deployed a flexible analytic platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are supporting, rather than the lead, actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then they developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium's analytic requirements. Different specialties will have different specific research needs and will likely continue to use and develop an array of diverse analytic and technical solutions for their projects. COMETS Analytics shows how important and enabling the upstream attention to data standards and data consistency are to producing high-quality metabolomics, consortium-based, and large-scale epidemiology research.