CHADA for Electron Backscatter Diffraction (EBSD)

The AddMorePower consortium is dedicated to developing characterization and modelling methods that can meet the unique needs of upcoming power semiconductor technology generations. To achieve effective and efficient research and innovation, a seamless interoperation between materials characterization, materials modelling, and data science is required. This goal can only be accomplished through the use of FAIR and open data practices. AddMorePower employs the CHaracterization DAta (CHADA) and MOdelling DAta (MODA) methodologies to achieve FAIR data management. These systematic description and documentation methods are used for materials characterization data and materials modelling data, respectively. They are based on a common terminology, concepts and relationships defined by the community, providing a holistic approach to combine data from various materials characterization and modelling techniques, which allows analysing and modelling of complex structures. In this document the CHADA for Electron Backscatter Diffraction (EBSD) experiments within the AddMorePower project is described. The structure is based on the final draft of the CWA 17815 created within the framework of the CEN/CENELEC Workshop “Materials characterisation — Terminology and structured documentation”. The draft was submitted on Dec. 16, 2024 to CEN/CENELEC.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-07
DOI
https://doi.org/10.5281/zenodo.16759108
Primary Topic
Machine Learning in Materials Science
Type
article
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article

CHADA for Electron Backscatter Diffraction (EBSD)

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

CHADA for Electron Backscatter Diffraction (EBSD)

article en

Abstract

The AddMorePower consortium is dedicated to developing characterization and modelling methods that can meet the unique needs of upcoming power semiconductor technology generations. To achieve effective and efficient research and innovation, a seamless interoperation between materials characterization, materials modelling, and data science is required. This goal can only be accomplished through the use of FAIR and open data practices. AddMorePower employs the CHaracterization DAta (CHADA) and MOdelling DAta (MODA) methodologies to achieve FAIR data management. These systematic description and documentation methods are used for materials characterization data and materials modelling data, respectively. They are based on a common terminology, concepts and relationships defined by the community, providing a holistic approach to combine data from various materials characterization and modelling techniques, which allows analysing and modelling of complex structures. In this document the CHADA for Electron Backscatter Diffraction (EBSD) experiments within the AddMorePower project is described. The structure is based on the final draft of the CWA 17815 created within the framework of the CEN/CENELEC Workshop “Materials characterisation — Terminology and structured documentation”. The draft was submitted on Dec. 16, 2024 to CEN/CENELEC.

Zenodo (CERN European Organization for Nuclear Research)
empirica - Communication and Technology Research (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 61%
Machine Learning in Materials Science
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