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Curating and sharing XAS data - Metadata and Scientific quality control
Abhijeet Gaur1, Sebastian Paripsa2,3, Frank Förste4
1Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT), Engesserstr. 20, Karlsruhe, D-76131, Germany. abhijeet.gaur@kit.edu.
Scientific Data
|August 1, 2026
Summary
Standardizing X-ray Absorption Spectroscopy (XAS) data quality and documentation is crucial for machine learning and reproducibility. This study proposes quality control measures for metadata and spectral data, exemplified by the RefXAS database.
Area of Science:
- Materials Science
- Spectroscopy
- Data Science
Background:
- X-ray Absorption Spectroscopy (XAS) data volume is increasing across facilities.
- Standardization of spectral quality and documentation is needed for machine learning and reference.
- Current practices risk repeating measurements, wasting valuable beamtime.
Purpose of the Study:
- To discuss quality control classification for XAS data, focusing on metadata and scientific quality.
- To establish standard documentation and curation protocols for XAS data.
- To improve data reusability and reproducibility through comprehensive metadata and quality evaluation.
Main Methods:
- Classification of quality control for XAS data into metadata and scientific quality.
- Utilizing the RefXAS database as a use case for developing metadata schema.
- Formulating quality criteria for automated screening and manual curation of uploaded data.
Main Results:
- A comprehensive metadata schema is essential for interpreting XAS spectra and enhancing reusability.
- Quality control involves evaluating both raw spectral data and associated metadata.
- Automated screening followed by manual curation ensures data accuracy and reliability.
Conclusions:
- Standardized metadata and scientific quality control are vital for curating XAS data.
- A well-defined protocol strengthens the distribution of XAS data under FAIR data principles.
- Implementing these measures enhances the value and accessibility of XAS datasets.
