One of the most daunting aspects of using genomic technologies—including microarray, proteomic, metabolomic, and other approaches—is the sheer quantity of data that they produce. With thousands of biologically relevant molecules surveyed across (increasingly) large numbers of samples, interpretation of the data requires the use of computational approaches. And while many researchers thought that storing the data could simply build on our experiences with genome sequencing, it quickly became apparent that if one was to make sense of the results from any analysis, there was a need to store much more complex ancillary data than would be necessary for genome sequence.