document: The data acquisition strategies must balance the relevant scales and volumes of the datasets to be used in the physical and statistical modeling. Approaches for extraction of the necessary information must be able to disregard spurious information, so as to develop a working network of models for each active mechanism related to each degradation pathway on the mesoscopic physical level and the data-driven statistical model level. To capture the temporal evolution of the energy material over long time frames, appropriate informatics methods are needed to balance data volume (e.g., simple univariate time-series data streams with high-dimensional volumetric imaging datasets) while considering their respective information contents [68,69]. The raw data and extracted information must be accessible for query and modeling. Similarly, the modeling approaches used to understand and parameterize active mechanisms and phenomena over lifetime fall into the broad categories of micro-, meso- and macroscopic approaches. Laboratory and real-world experimentation, informatics, analytics, and the development of network models for mesoscopic evolution of energy materials over lifetime together constitute the field of degradation science.