Mol Syst Biol. 2: 2006.0010 The nice thing about standards is that there are so many to choose from. Andrew S Tannenbaum 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. In 1999, as microarrays were establishing themselves as a truly viable technology, the Microarray Gene Expression Data Society (MGED; http://www.mged.org) arranged to define the critical information necessary to effectively analyze a microarray experiment and to describe a means of encoding that information. Through a series of discussions between interested parties, public presentations, and working group meetings, what emerged were the Minimal Information About a Microarray Experiment (MIAME) (Brazma et al , 2001; Ball et al , 2002, 2004) and MAGE‐ML (Spellman et al , 2002), an XML‐based markup language used for describing a microarray experiment. The early success of MIAME and its widespread adoption by scientific journals also exposed some of its weaknesses, including the need to develop domain‐specific extensions of MIAME to capture information about the experimental design and sample characteristics necessary for interpreting data coming, for example, from toxicology experiments (MIAME‐Tox; Sansone et al , 2005) and extensions to other domains such as in situ hybridizations (MISFISHIE, the Minimum Information Specification For In Situ Hybridization …