In this work, we focused our efforts on the in-house LEF library-derived data set and the publicly accessible Enamine data sets. We kept the two data sources and models trained on these sets separated due to the difference in their experimental conditions, despite observing very good fluorine chemical shift correlation in the overlap between both data sets. Evaluating whether and under what conditions data sets could be combined is key for extending available data sets. One essential part of this is consistent referencing of 19 F NMR chemical shift between institutions, which has been shown to lower deviations by orders of magnitude. In the absence of a consensus, reporting referencing methods may already facilitate corrections to correlate chemical shift measurements. ML model building could then leverage the availability of more 19 F NMR chemical shift data sets of fluorinated molecules in aqueous solution, for example, from other vendors or academic groups, to expand the applicability domain.