This approach is powerful for biomarker discovery but can be frustrating for both metabolomics practitioners and their collaborators if important biomarkers are slow or difficult to identify posthoc. For this reason, metabolic profiling laboratories often build in-house method-specific databases, empirical in nature and matching their chosen chromatographic separation conditions, for the characterisation (broad annotation) of their profiling methods. curated and whenever possible (for the majority of small molecules and some lipids) compared to the reference chemical material achieving the highest annotation confidence level of 1 according to MSI. An overview of the small molecules and lipid species described is provided in Figure (following page). The open platform leverages the experience of dedicated metabolomics practitioners, providing both a foundation for cooperation among like-minded experts and a turnkey solution for novice users. The latter is specifically helpful in promoting the application of metabolomics within multidisciplinary research teams as it helps to quickly establish reliable methodology and the means to interrogate the data it produces to derive biochemical insights. We advocate the suitability of the methods presented here for a wide variety of clinical sample types and encourage researchers to adapt new sample types to these methods rather than create new uncharacterised methods. However, their use does not preclude more hypothesis and needdriven expansion via the development of augmenting methods and technologies including those oriented toward more sensitive detection (e.g., for low abundance metabolites) or increased analytical specificity (e.g., chiral separations).