Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
Author(s)
Type
Dataset
Abstract
Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)
This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative
electron diffraction and X-ray spectroscopy, in the scanning electron microscope'
https://doi.org/10.1016/j.ultramic.2020.112944
The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.
Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and
.mat results files are contained within. These are organised by Figure number in the paper.
The provided results are divided into two major sections:
(1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.
RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum.
(2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps.
This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative
electron diffraction and X-ray spectroscopy, in the scanning electron microscope'
https://doi.org/10.1016/j.ultramic.2020.112944
The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.
Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and
.mat results files are contained within. These are organised by Figure number in the paper.
The provided results are divided into two major sections:
(1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.
RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum.
(2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps.
Version
1
Date Issued
2020-01-21
Citation
2020
Copyright Statement
https://creativecommons.org/licenses/by/4.0/legalcode
Is Referenced By
Subjects
Machine Learning
PCA
EBSD
EDS
Microscopy