Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing 1

A New method for analyzing the microstructural changes in high-strength aluminum alloys, commonly used in aerospace and automotive industries, that deteriorate over time due to aging. The traditional manual analysis of these changes is replaced with an automated approach that provides more objective and reproducible results. The method involves using dark-field transmission electron microscopy images, generating and evaluating precipitation contours, and converting the results into semantic data structures. The adoption of Jupyter Notebooks and Semantic Web technologies in this process enhances the reproducibility and comparability of the findings, serving as a model for FAIR image and research data management

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