Table 2 Examples of impact of NMRlipids and other overlay databanks in different disciplines

Computational and data scientists

Databanks with programmatic access, training sets for machine learning applications, automatic optimisation of simulation models

Programmatic access to all publicly available MD simulation trajectories, machine learning models predicting properties of complex biomolecular assemblies (Table 1), optimising force field and model parameters from atomistic to continuum scale

Biomedical scientists

Applications of biomolecular modelling in new fields, coupling omics to biomolecular structure and dynamics

Anisotropic water diffusion for pharmacokinetics and MRI imaging applications (Fig. 5), properties of complex cellular structures based on composition analyses from omics

Material scientists

Predictions of complex biomolecular material properties from data-driven and machine learning models

Optimising composition of lipid nanoparticle formulations for desired properties, predictions of bioinspired material properties

Biophysicists

Novel analyses and predictions for properties of biomolecular assemblies and their compositions

Analyses of rare phenomena (for example, Lipid flip-flops and water permeation in Figs. 4 and 5), data-driven analyzes and machine learning models predicting correlations between properties and compositions of biomolecular assemblies (Figs. 2 and 5)

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 of its location