  We present a few recent developments in the field of electron backscatter
diffraction (EBSD). We highlight how open source algorithms and open data
formats can be used to rapidly to develop microstructural insight of materials.
We include use of AstroEBSD for single pixel based EBSD mapping and
conventional orientation mapping; followed by an unsupervised machine learning
approach using principal component analysis and multivariate statistics
combined with a refined template matching method to rapidly index orientation
data with high precision. Next, we compare a diffraction pattern captured using
direct electron detector with a dynamical simulation and project this to create
a high quality experimental "reference diffraction sphere". Finally, we
classify phases using supervised machine learning with transfer learning and a
convolutional neural network.
