Liquid chromatography mass spectrometry (LC-MS) technologies enable the isolation and measurement of small molecule metabolites and lipids in complex biological samples with a high degree of analytical sensitivity and specificity, making them a cornerstone in global profiling (un/non-targeted) metabolomics. However, the resulting datasets are large and complex, typically containing tens of thousands of variables ranging in measurement quality and exhibiting a high degree of redundancy. Consequently, biological interpretation of global profiling data is fruitless without considerable effort toward transforming analytical data into biochemical knowledge. This represents a substantial barrier to the success of LC-MS-based metabolomics in clinical and translational research. This knowledge can be built incrementally for each LC-MS method by establishing the chemical identity of each variable (or group of variables representing a metabolite's mass spectrum) and routinely assigning those identities to the variables observed in each LC-MS-based metabolomics study. This process can be arduous, however. The de novo annotation of metabolite-derived variables requires careful interpretation of experimental spectral data by experienced specialists, optionally aided by complex computational tools. Furthermore, the procurement (or synthesis) and analysis of reference materials with accompanying wet lab experimentation is expected where possible, and required when reporting high confidence metabolite identification (MetID). Characterising the coverage of LC-MS profiling methods by this process is a mammoth task given the diversity and breadth of analytes present and detectable in biofluids.