To fully understand the context, methods, data and conclusions that pertain to an experiment, one must have access to a range of background information. However, the current diversity of experimental designs and analytical techniques complicates the discovery and evaluation of experimental data; furthermore, the increasing rate of production of those data compounds the problem. Community opinion increasingly favors that a regularized set of the available metadata ('data about the data') pertaining to an experiment^{1, 2} be associated with the results, making explicit both the biological and methodological contexts. Many journals and funding agencies now require that authors reporting microarray-based transcriptomics experiments comply with the Minimum Information about a Microarray Experiment (MIAME) checklist³ as a prerequisite for publication^{4, 5, 6, 7}. Similarly, minimum information guidelines for reporting proteomics experiments and describing systems biology models are gaining broader support in their respective database communities^{8, 9}; and progress is being made toward the standardization of the reporting of clinical trials in the medical literature¹⁰. Such minimum information checklists promote transparency in experimental reporting, enhance accessibility to data and support effective quality assessment, increasing the general value of a body of work (and the competitiveness of the originators).