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As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. The maturation of data-centricdoi:10.1101/2020.06.30.178673 fatcat:up6eozdxyjhlxmkllqa4deewfm