We use materials science R&D data as an example of an inherently complex data source. We show that modeling the data without making assumptions demands a complicated database architecture, which would be difficult to create without DBgen. We show that modeling the data in this way adds transparency and flexibility to dataset curation. Altogether, we provide evidence that DBgen is a useful tool that greatly reduces the barrier to storing scientific data in accordance with the widely accepted FAIR principles.