ESAMP models this complexity completely without making any simplifying assumptions. This is significant because it adds transparency, flexibility, and provenance to the curation of datasets for machine learning (ML) or other data analysis techniques. When the underlying data is modeled without assumptions, the user is able to write a SQL query with a certain set of constraints and assumptions to generate a dataset. If the researcher finds that the data is imbalanced or comes across another problem with their first dataset, they can easily edit the query to generate a new, improved dataset.