In polymer informatics , machine learning (ML) techniques are based on machinereadable representations of a polymer's repeat units, e.g., the Simplified Molecular-Input Line-Entry System (SMILES) . Polymer-SMILES (p-SMILES) is an extension of SMILES in which symbols such as "*" indicate the polymerization points of the repeat units. Alternative representations include the "Hierarchical Editing Language for Macromolecules" (HELM) , the "INternational CHemical Identifier" (InChI) , and CurlySMILES . In general, string representations are limited to homopolymers and are not suitable for capturing the stochastic nature of polymers, such as encoding randomly branched polymers, as is the case for CurlySMILES. More recently, BigSMILES has been applied to represent polymers in string format. In BigSMILES, special characters such as "$" and brackets "⟨" "⟩" indicate the position of head and tail bonds between repeat units. As an advancement, BigSMILES can encode copolymers and enables topological representations of polymeric chains in complex polymers. However, BigSMILES strings provide only a qualitative description of a molecular ensemble . To fully characterize a polymer, a probability and weight must be assigned to each polymer constituent. By providing a standard format for digitalizing data, PolyDAT serves as a quantitative extension of BigSMILES.