Different types of research can be accomplished with different levels of detail regarding data management. The FAIR-Principles (findable, accessible, interoperable and reusable)1 provide a meaningful framework for research data management. The level of detail regarding analytic meta-information also varies with size and intent of the research project. Yet information not initially recorded will be hard if not impossible to retrieve as time passes.
For the coding of analytic meta-information different standards can be used to improve computerized information retrieval. While the natural language provides different options for naming one measurand it can hopefully be mapped to one coding in a given standard. Common coding systems for clinical chemistry analysis include SPREC, UCUM, LOINC and to some extend SNOMED-CT.
Upon reaching a certain size research projects are conducted in more heterogeneous variations of laboratory settings. Variations may include different batches of reagent, mechanical changes or repairs to the machinery over time or for multi center studies even varying analytical methods for the same measurand.
1) Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
