Community-Driven Methods for Open and Reproducible Software Tools for Analyzing 
Datasets from Atom Probe Microscopy

Kühbach, Markus Tobias; London, Andrew J.; Wang, Jing, Jing; Schreiber, Daniel K.; Mendez Martin, Francisca; Ghamarian, Iman; Bilal, Huma; Ceguerra, Anna V.

doi:10.1017/S1431927621012241

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Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy

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Kühbach, Markus Tobias
Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Kühbach, M. T., London, A. J., Wang, Jing, J., Schreiber, D. K., Mendez Martin, F., Ghamarian, I., et al. (2021). Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy. Microscopy and Microanalysis, 1-16. doi:10.1017/S1431927621012241.

Cite as: https://hdl.handle.net/21.11116/0000-0009-6817-9

Abstract

Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results. © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America.