Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their success relies on training data that is available in programmatically accessible and standardized format . For example in structural biology, Protein Data Bank (PDB) has enabled revolutionary tools that can predict protein structures with unprecedented accuracy . However, the lack of smart guidelines and community-consensus best-practices for data sharing are limiting the development of such databanks and AI applications in many fields . This is the case in biomolecular modeling, where vast amount of data is already available for proteins, lipids, nucleic acids, and carbohydrates, but tools that enable applications of these data for data-driven applications are not yet available . Here we propose a cost-effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications: an overlay databank. We demonstrate the practical relevance of such approach for understanding cellular membranes by incorporating available lipid bilayer simulations into the NMRlipids Databank. Importantly, the basic principles of an overlay databank can be applied to simulation data of any other biomolecules (that are becoming available in increasing amounts ), as well as to any other field where the development of AI-based tools is limited by the lack of data access and community best-practices -such as the assignment of NMR spectra . Incentives to share data can be further accelerated by combining overlay databanks with an open collaboration approach (see Ref. 6).