One defining characteristic of materials science data is its abundance of data types, from videos to images to atomic structures . Comparatively, spatiotemporal models, besides the classic examples like RFs and GPs, may also take the form of point processes , state space models , and diffusion processes , which are as yet not used for data integration, but have their respective benefits to representing specific data types. Besides coordinates with concrete physical meaning, one could also consider direct spatial or dynamical models of the latent space in data integration, as it is often more robust to noise and dimensional scaling artifacts, especially for multiple data modalities. This leads to the question of problem mapping from data type to model category and subsequent model specification as the primary challenges.