Hostname: page-component-77c89778f8-gq7q9 Total loading time: 0 Render date: 2024-07-22T01:40:44.637Z Has data issue: false hasContentIssue false

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

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
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