Despite its rich tradition, there are key limitations to researchers’ ability to make generalizable inferences about state policy innovation and diffusion. This paper introduces new data and methods to move from empirical analyses of single policies to the analysis of comprehensive populations of policies and rigorously inferred diffusion networks. We have gathered policy adoption data appropriate for estimating policy innovativeness and tracing diffusion ties in a targeted manner (e.g., by policy domain, time period, or policy type) and extended the development of methods necessary to accurately and efficiently infer those ties. Our state policy innovation and diffusion (SPID) database includes 728 different policies coded by topic area. We provide an overview of this new dataset and illustrate two key uses: (i) static and dynamic innovativeness measures and (ii) latent diffusion networks that capture common pathways of diffusion between states across policies. The scope of the data allows us to compare patterns in both across policy topic areas. We conclude that these new resources will enable researchers to empirically investigate classes of questions that were difficult or impossible to study previously, but whose roots go back to the origins of the political science policy innovation and diffusion literature.