Featuring: The CRIPT Polymer Database and BigSmiles Polymer Data Representation. The exponential rise in the production and use of plastics, particularly in single-use applications, has led to a dramatic increase in their environmental prevalence and problems with plastic waste management. One necessary component of the solution to this challenge is developing plastics that degrade more effectively when they are accidentally released into the environment, an unavoidable occurrence at some level in any practical waste handling system. Although biodegradation is believed to be a function of chemical structure and therefore should be amenable to quantitative structure-property methods such as group contribution theory or more recent machine learning approaches, the field is plagued by a lack of data. Herein, we report the adaptation of the clear zone assay from molecular biology to the high-throughput screening of biodegradation that can overcome long test times of standardized methods and enable a large biodegradation data set to explore structure-property relationships. We report the synthesis and biodegradation testing of thousands of different polyesters, polyurethanes, and polyamides and the development of new machine learning models to predict polymer biodegradation based on this data-driven by our BigSMILES line notation. The data is organized into the Community Resource for Innovation in Polymer Technology (CRIPT) platform to make it widely available according to FAIR data standards, demonstrating the utility of these tools for big polymer data projects.