Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-26T04:49:49.036Z Has data issue: false hasContentIssue false
  • English
  • Français

Reproducible Spectrum and Hyperspectrum Data Analysis Using NeXL

Published online by Cambridge University Press:  02 March 2022

Nicholas W. M. Ritchie Open the ORCID record for Nicholas W. M. Ritchie [Opens in a new window]
Nicholas W. M. Ritchie*
Affiliation:
Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, MD20899-8371, USA
Get access

Abstract

NeXL is a collection of Julia language packages (libraries) for X-ray microanalysis data processing. NeXLCore provides basic atomic and X-ray physics data and models including support for microanalysis-related data types for materials and k-ratios. NeXLMatrixCorrection provides algorithms for matrix correction and iteration. NeXLSpectrum provides utilities and tools for energy-dispersive X-ray spectrum and hyperspectrum analysis including display, manipulation, and fitting. NeXL is integrated with the Julia language infrastructure. NeXL builds on the Gadfly plotting library and the DataFrames tabular data library. When combined with the DrWatson package, NeXL can provide a highly reproducible environment in which to process microanalysis data. Data availability and reproducible data analysis are two keys to scientific reproducibility. Not only should readers of journal articles have access to the data, they should also be able to reproduce the analysis steps that take the data to final results. This paper will both discuss the NeXL framework and provide examples of how it can used for reproducible data analysis.

Keywords

EDShyperspectrumquantificationreproduciblesoftware
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 28 , Issue 2 , April 2022 , pp. 478 - 495
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Armstrong, JT, Donovan, J & Carpenter, P (2013). CALCZAF, TRYZAF and CITZAF: The use of multi-correction-algorithm programs for estimating uncertainties and improving quantitative X-ray analysis of difficult specimens. Microsc Microanal 19, 812–813.CrossRefGoogle Scholar
Bezanson, J, Edelman, A, Karpinski, S & Shah, VB (2017). Julia: A fresh approach to numerical computing. SIAM Rev. 59, 65–98.CrossRefGoogle Scholar
Blöthe, M, Wegorzewski, A, Müller, C, Simon, F, Kuhn, T & Schippers, A (2015). Manganese-cycling microbial communities inside deep-sea manganese nodules. Environ Sci Technol 49, 7692–7700.CrossRefGoogle ScholarPubMed
Bright, DS & Newbury, DE (2004). Maximum pixel spectrum: A new tool for detecting and recovering rare, unanticipated features from spectrum image data cubes. J Microsc 216, 186–193.CrossRefGoogle ScholarPubMed
Cortes, C & Vapnik, V (1995). Support-vector networks. Mach Learn 20, 273–297.CrossRefGoogle Scholar
Datseris, G, Isensee, J, Pech, S & Gál, T (2020). DrWatson: The perfect sidekick for your scientific inquiries. J Open Source Softw 5, 2673. doi:10.21105/joss.02673CrossRefGoogle Scholar
de la Peña, F, Ostasevicius, T, Fauske, VT, Burdet, P, Jokubauskas, P, Nord, M, Sarahan, M, Prestat, E, Johnstone, DN & Taillon, J (2017). Electron microscopy (big and small) data analysis with the open source software package HyperSpy. Microsc Microanal 23, 214–215.CrossRefGoogle Scholar
Dempster, AP, Laird, NM & Rubin, DB (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B (Stat Methodol) 39, 1–22.Google Scholar
Folk, M, Heber, G, Koziol, Q, Pourmal, E & Robinson, D (2011). An overview of the HDF5 technology suite and its applications. Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, pp. 36–47.CrossRefGoogle Scholar
Galbert, F (2007). Measurement of carbon layer thickness with EPMA and the thin film analysis software stratagem. Microsc Microanal 13, 96–97.CrossRefGoogle Scholar
Guess, MJ & Wilson, SB (2002). Introduction to hierarchical clustering. J Clin Neurophysiol 19, 144–151.CrossRefGoogle ScholarPubMed
ISO (2012). ISO 22029:2012 Microbeam analysis – EMSA/MAS standard file format for spectral-data exchange. International Organization for Standardization, Geneva, Switzerland.Google Scholar
Jones, DC, Arthur, B, Nagy, T, Mattriks, , Gowda, S, Godisemo, , Holy, T, Noack, A, Sengupta, A, Darakananda, D, Adam, B, Dunning, I, Leblanc, S, Fischer, K, Chudzicki, D, Piibeleht, M, Huijzer, R, Mellnik, A, Kleinschmidt, D, Breloff, D, Yu, Y, Huchette, J, Innes, MJ, Inkyu, Verzani, J, Pelenitsyn, A, Coalson, C, O'Mara, C & Saba, E (2021). Giovineitalia/gadfly.jl: v1.3.2. https://zenodo.org/record/4621172.Google Scholar
Kotula, PG, Keenan, MR & Michael, JR (2003). Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc Microanal 9, 1–17.CrossRefGoogle ScholarPubMed
Llovet, X & Merlet, C (2010). Electron probe microanalysis of thin films and multilayers using the computer program XFILM. Microsc Microanal 16, 21–32.CrossRefGoogle ScholarPubMed
MacQueen, J (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, eds. Le Cam LM & Neyman J, pp. 281–297.Google Scholar
Moy, A & Fournelle, J (2020). Badgerfilm: An Open Source thin film analysis program. Microsc Microanal 26, 496–498.CrossRefGoogle Scholar
Moy, A & Fournelle, J (2021). ϕ (ρz) distributions in bulk and thin-film samples for EPMA. Part 2: Badgerfilm: A new thin-film analysis program. Microsc Microanal 27, 284–296.CrossRefGoogle ScholarPubMed
Munafò, MR, Nosek, BA, Bishop, DV, Button, KS, Chambers, CD, Du Sert, NP, Simonsohn, U, Wagenmakers, EJ, Ware, JJ & Ioannidis, JP (2017). A manifesto for reproducible science. Nat Hum Behav 1, 1–9.CrossRefGoogle ScholarPubMed
Newbury, DE & Bright, DS (1999). Logarithmic 3-band color encoding: Robust method for display and comparison of compositional maps in electron probe X-ray microanalysis. Microsc Microanal 5, 333–343.CrossRefGoogle ScholarPubMed
Newbury, DE & Ritchie, NWM (2015). Performing elemental microanalysis with high accuracy and high precision by scanning electron microscopy/silicon drift detector energy-dispersive X-ray spectrometry (SEM/SDD-EDS). J Mater Sci 50, 493–518.CrossRefGoogle Scholar
Peng, RD (2011). Reproducible research in computational science. Science 334, 1226–1227.CrossRefGoogle ScholarPubMed
Ritchie, NW (2015). Diluvian clustering: A fast, effective algorithm for clustering compositional and other data. Microsc Microanal 21, 1173–1183.CrossRefGoogle ScholarPubMed
Ritchie, NW, Mengason, MJ & Newbury, DE (2017). Standard bundles simplify standards-based quantification in NIST DTSA-II. Microsc Microanal 23, 220–221.CrossRefGoogle Scholar
Ritchie, NWM & Filip, V (2011). Semantics for high speed automated particle analysis by SEM/EDX. Microsc Microanal 17, 896.CrossRefGoogle Scholar
Schamber, FH (1977). A Modification of the Linear Least-squares Fitting Method Which Provides Continuum Suppression, pp. 241–257. Ann Arbor Science Publishers.Google Scholar
White, L, Togneri, R, Liu, W & Bennamoun, M (2019). Datadeps.jl: Repeatable data setup for reproducible data science. J Open Res Softw 7.CrossRefGoogle Scholar