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Open-Source Tools and Containers for the Production of Large-Scale S/TEM Datasets

Published online by Cambridge University Press:  30 July 2021

Alexander M Rakowski
Affiliation:
LBNL, United States
Joydeep Munshi
Affiliation:
ANL, United States
Benjamin Savitzky
Affiliation:
Lawrence Berkeley National Laboratory, California, United States
Shreyas Cholia
Affiliation:
LBNL, United States
Matthew L Henderson
Affiliation:
LBNL, United States
Maria KY Chan
Affiliation:
ANL, United States
Colin Ophus
Affiliation:
Lawrence Berkeley National Laboratory, California, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Savitzky, B.H., et al. , py4DSTEM: A software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets. arXiv preprint arXiv:2003.09523, 2020.Google Scholar
Guo, Yanming, et al. "Deep learning for visual understanding: A review." Neurocomputing 187 (2016): 27-48.CrossRefGoogle Scholar
Ophus, C., A fast image simulation algorithm for scanning transmission electron microscopy. Advanced Structural and Chemical Imaging, 2017. 3(1): p. 13.CrossRefGoogle ScholarPubMed
Pryor, A., Ophus, C., and Miao, J., A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy. Advanced Structural and Chemical Imaging, 2017. 3(1): p. 15.CrossRefGoogle ScholarPubMed
Schembera, B. Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data. J Supercomput (2021). https://doi.org/10.1007/s11227-020-03602-6CrossRefGoogle Scholar