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Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography.

Title: Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography.
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Name(s): Jesse, S, author
Chi, M, author
Belianinov, A, author
Beekman, C, author
Kalinin, S V, author
Borisevich, A Y, author
Lupini, A R, author
Type of Resource: text
Genre: Journal Article
Text
Date Issued: 2016-05-23
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. Here, we discuss the application of so-called "big-data" methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. However, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.
Identifier: FSU_pmch_27211523 (IID), 10.1038/srep26348 (DOI), PMC4876439 (PMCID), 27211523 (RID), 27211523 (EID), srep26348 (PII)
Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4876439.
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_pmch_27211523
Owner Institution: FSU
Is Part Of: Scientific reports.
2045-2322
Issue: vol. 6

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Jesse, S., Chi, M., Belianinov, A., Beekman, C., Kalinin, S. V., Borisevich, A. Y., & Lupini, A. R. (2016). Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography. Scientific Reports. Retrieved from http://purl.flvc.org/fsu/fd/FSU_pmch_27211523