Training and Validation Datasets for Neural Network to Fill in Missing Data in EBSD Maps

This dataset consists of the synthetic electron backscatter diffraction (EBSD) maps generated for the paper, titled "Hybrid Algorithm for Filling in Missing Data in Electron Backscatter Diffraction Maps" by Emmanuel Atindama, Conor Miller-Lynch, Huston Wilhite, Cody Mattice, Günay Doğan, and Prashant Athavale. The EBSD maps were used to train, test, and validate a neural network algorithm to fill in missing data points in a given EBSD map.The dataset includes 8000 maps for training, 1000 maps for testing, 2000 maps for validation. The dataset also includes noise-added versions of the maps, namely, one more map per each clean map.