Data and model description framework developed for laboratory Mössbauer spectroscopy

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Authors: Zoltán Klencsár, Junhu Wang, Rile Ge, Duorong Liu, Alexandre I. Rykov, Tao Zhang
Cited by: 0
References: 17

Abstract

Abstract To enable the efficient storage, retrieval and reuse of data records deposited in data repository systems regarding traditional laboratory Mössbauer spectroscopy measurements, a corresponding comprehensive framework has been developed, which provides a simple yet versatile approach for describing the measured sample, experimental conditions, spectral data, theoretical models, and associated metadata. Experimental details and common data treatment procedures in laboratory Mössbauer spectroscopy were considered to identify the metadata necessary for a thorough characterization of measurements, facilitating the reproducibility and replicability of the results. A grouping of metadata and related information according to their subject is proposed. Based on these groups, a record structure is defined that is divided into several segments, with each segment containing the metadata of a specific group. The precise description of theories used to analyze Mössbauer spectra is proposed to rely on established encoding systems employed by existing Mössbauer spectral analysis software to represent the fit models they are designed to handle. Nevertheless, to define a standardized theoretical baseline, a simplified representation of common special cases of 57 Fe and 119 Sn Mössbauer spectral fitting models is proposed, ensuring compatibility with a wide range of existing spectral analysis software. Although the developed framework currently focuses on traditional laboratory Mössbauer spectroscopy, it is designed to enable future expansion to closely related methods, such as synchrotron radiation-based Mössbauer spectroscopy.

Fields of Study & Subjects

Crystallography and Radiation Phenomena Research Data Management Practices Scientific Computing and Data Management

Publication details

Journal / source
Interactions
Volume
247
Issue
1
Publication date
12 March 2026
Language
EN