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Infrastructure for Analysis of Large Microscopy and Microanalysis Data Sets

Published online by Cambridge University Press:  22 July 2022

Jingrui Wei
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States
Carter Francis
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States
Dane Morgan
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States
KJ Schmidt
Affiliation:
University of Chicago, Globus / Argonne National Laboratory – Data Science and Learning Division
Aristana Scourtas
Affiliation:
University of Chicago, Globus / Argonne National Laboratory – Data Science and Learning Division
Ian Foster
Affiliation:
University of Chicago, Globus / Argonne National Laboratory – Data Science and Learning Division
Ben Blaiszik
Affiliation:
University of Chicago, Globus / Argonne National Laboratory – Data Science and Learning Division
Paul M. Voyles*
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States
*
*Corresponding author: [email protected]

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Copyright
Copyright © Microscopy Society of America 2022

References

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Development of pyxem at UW-Madison is supported by the Wisconsin MRSEC (DMR-1720415). Development of Foundry is support by the National Science Foundation (OAC-1931298).Google Scholar