Most structural models deposited in the Protein Data Bank (PDB) result from experimental X-ray crystallography or single particle cryoEM studies. These methods collect data averaged over tens of thousands to billions of individual copies of the system (containing macromolecules, solvent, ions, small molecules, etc). Each molecule within the system can adopt a different conformation (conformational heterogeneity) and may differ slightly in chemical composition (compositional heterogeneity). However, structural models generally represent the system with a single coordinate set. This simplification overlooks the multiple states present in the experimental data and consequently omits details vital to understanding protein function . The single coordinate set convention originated in the specifications of the legacy PDB format and is perpetuated in the current PDBx/mmCIF standard . However, with the universal adoption of PDBx/mmCIF , we propose to expand our representations to separate these two aspects of heterogeneity within the sample, enabling more precise and accurate structures to train deep learning approaches that model dynamic biomolecular systems. In the cases where deposited models go beyond a single set of coordinates, capturing the underlying experimental ensemble is solved in two primary ways: ensemble models that encode many copies of the system, each with a potentially distinct conformation/composition, in a single PDB deposition or multiconformer models that encode alternate conformational/compositional states only for certain parts of the system (Figure ). Nuclear Magnetic Resonance (NMR) data is typically encoded in ensemble models because it inferentially represents sparse distance and angle restraints . Occasionally, these conventional restraints are augmented with other NMR observables, such as relaxation dispersion measurements, that more directly report on distinct states. There are proposals for the best ways to represent the multiple ensembles necessitated by their inclusion . In contrast, cryoEM and X-ray crystallography density maps provide atomistic detail across the entire system, enabling the precise modeling of alternate states directly from a real space signal. As there is currently no principled way of choosing the most parsimonious ensemble size, encoding cryoEM and X-ray crystallography data in ensemble models can result in an exploding data-to-parameter ratio and overfitting (Figure ) . Moreover, ensemble models are difficult to modify manually (e.g. in Coot or Isolde) . In contrast, multiconformer models can represent all states within a single model, reducing the data-to-parameter ratio and allowing for more facile human visualization and manipulation . A limitation of the current PDBx/mmCIF data structure for ensemble and multiconformer models is that it cannot represent the complex interdependencies of alternative conformational states in the experimental ensemble.