Open-source hyper-dimensional materials analytics using HyperSpy


With modern advances in computer technology, materials characterization techniques such as electron microscopy (EM) are generating vastly increasing amounts of digital experimental data, requiring novel processing strategies and providing challenges for data analysis. Prominent among these challenges is being able to easily and reproducibly develop these new strategies, due to the limitations of existing proprietary software solutions available in the EM community. The open source HyperSpy project address this issue by providing researchers with easy access to data in proprietary formats, reproducible analysis through scripting and “notebook computing”, and access to an ever-growing collection of high-quality scientific data processing libraries in the scientific Python ecosystem, including state of the art machine learning strategies. This talk will introduce the HyperSpy project, demonstrate the capabilities of the software, and provide a number of published examples of how HyperSpy has been used for the processing of large multi-dimensional EM imaging and spectroscopy datasets.

Oct 17, 2021
Joshua Taillon
Materials Data Scientist

A materials research scientist at NIST interested in scientific data curation, AI for materials research, and baking bread.

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