The focus of biomolecular simulations is moving from studies of individual molecules to larger complexes and even whole cells and organelles . Simultaneously, machine-learning-based models for predicting the behaviour of biomolecules and automatic approaches to parametrize models are emerging . The resources delivered by the NMRlipids Databank will support developments in both of these directions. Automatic quality evaluation and ranking of simulations against experimental Analyses of rare phenomena (for example, Lipid flip-flops and water permeation in Figs. and), data-driven analyzes and machine learning models predicting correlations between properties and compositions of biomolecular assemblies (Figs. and) Students and teachers Graphical and programmatic access to biomolecular structure and dynamics Illustration of disorder and dynamics in biomolecules, example data for bioinformatics and data-science courses Reviewers and publishers Tool to facilitate FAIRness of data, transparency and reproducibility All publicly available data can be included in an overlay databank irrespectively on its location Table . Examples of impact of NMRlipids and other overlay databanks in different disciplines. data enable the selection of best simulations for specific applications without laborious manual force field evaluation. This also streamlines automatic parametrization procedures for atomistic and coarse grained simulations by, for example, pinpointing typical failures of force fields and highlighting points of improvement. Such practises for fostering the accuracy of simulations are becoming increasingly important as small errors accumulate when complexity and size of simulated systems are increasing. Examples of impact of NMRlipids and other overlay databanks in different disciplines are listed in Table , yet the scope of applications is expected to further widen with increasing amount of publicly shared data.