It is a common practice to filter experimental data before adding them to the dataset. For example, the maintainers of the NIST retention index database have been using simple but effective algorithms since the first release of the database . Authors of the RepoRT dataset also implemented their own filtering pipeline to enable quality control for the retention times added to the dataset . They compared experimental values with the predictions obtained using gradient boosting models (10-fold cross-validation) trained on each of the sub-datasets obtained under the same chromatographic conditions.