Understanding the microstructure-property relation is at the core of materials science and engineering. As this does not describe a fixed "state" of the material but is rather dependent on time and history, and oncomplex interaction of many different details and aspects, this poses still a significant challenge to the materials community. In this talk we present several examples that elucidate how data mining strategies can be used to extract otherwise inaccessible information from experiment and simulation and how this can be used to shed some light on the microstructure-property relationship in metals and alloys. In particular, our goal is to understand some of the many open questions concerning the underlying structure-property relations in single crystalline metals and High Entropy Alloys (HEAs). Although in-situ Transmission Electron Microscopy (TEM) allows high-resolution studies of the structure and dynamics of moving dislocations and -- in a way -- makes the local obstacle/energy "landscape" directly visible through the geometry of dislocations; a truly three-dimensional analysis and high-throughput data-mining of the resulting images or movies is still not possible. We introduce a novel data-mining approach that is based on spatio-temporal coarse graining of TEM dislocation movies, making ensemble averaging of a large number of snapshots in time possible [1]. Using dislocations as "probes" we investigate the effect of pinning points on the dislocation gliding behavior of CoCrFeMnNi alloy during in-situ TEM straining. Additionally, we use our Deep Learning-based dislocation extraction and 3D reconstruction to analyze the strain avalanche statistics of in-situ TEM recordings and discuss the dependency of the power law exponent based on 3D discrete dislocation dynamics simulations.