In summary, we have meta-analyzed a large amount of permeability data from freely available databases MolMeDB and ChEMBL. Permeability is, among other things, the basis of classifying drug substances into the Biopharmaceutics Classification System (BCS) and this classification forms the basis of product formulation and regulatory approval strategy decisions, hence it is important to have reliable data for permeability. Moreover, permeability as an important pharmacological property is of interest to many researchers who try to create machine learning algorithms for its prediction. The variability between individual measurements, even for the same methods, has shown that efforts should be made to develop robust methods that would enable consistent inter-laboratory values to be measured and stored in FAIR manner, e.g. in MolMeDB database. Next the analysis of individual methods observed their data limits. Meta-analysis in-between the datasets has shown that cell-based methods such as CACO-2, MDCK are comparable with apparent PAMPA, but all of these methods correlate less with calculated physics-based methods (COSMOperm and PerMM) and with single membrane-based BLM/liposomes or intrinsic PAMPA, which are based on molecular permeabilities. This needs to be considered when permeability data are from different methods compared or used in machine-learning approaches. Finally, we have devised a scale with five significant permeability values as a multiplier of 4 log units of cm•s -1 to help a comprehensive understanding of the permeability data within their physical context.