Sep 23 – 27, 2024
ESRF Auditorium
Europe/Paris timezone

Construction of XASDB

Sep 24, 2024, 6:15 PM
45m
Hybrid event (ESRF Auditorium)

Hybrid event

ESRF Auditorium

EPN Campus ESRF - ILL 71 Av. des Martyrs, 38000 Grenoble
Poster Data Analysis Posters

Speaker

雪琪 宋 (IHEP)

Description

X-ray Absorption Spectroscopy (XAS) is a pivotal technique for material characterization. In the field of XAS, data assessment typically involves comparing the data with reference spectra from previous measurements, placing high demands on the quality of the spectra and measurement data. In order to advance data-driven scientific research, it is urgent to establish a reusable infrastructure for XAS data.
Over the past decade, we have collected a series of standard and well-characterized XAS data on the core beamline B8 at the BSRF. Additionally, in the future, the High Energy Photon Source (HEPS) build in China is estimated to generate a large volume of XAS data. Those massive amount of data provides a solid foundation for the construction and ongoing development of the database.
Foucsing on these problems, we have designed and implemented a database for XAS data. This database aims to integrate multiple resources of XAS data, while supplementing information on the characteristics, sources, and analysis of spectral information. Then, We have established specifications and standards for relevant data formats and provided comprehensive displays of both raw and processed data. Furthermore, we have provided a spectral matching tool capable of identifying the most similar spectra in the database to a given input data.
This database will provide effective foundational support for the opening and sharing of XAS data, enabling users to conveniently access, share, and reuse the required scientific data, thereby enhancing the value of the data and accelerating the productivity of scientific output.
In the future, this database will be available for free access and the researchers can conveniently utilize it for further analysis, such as matching with their own experiments data and developing machine learning models.

Abstract publication I agree that the abstract will be published on the web site

Primary author

雪琪 宋 (IHEP)

Co-authors

Mr 浩东 尧 (IHEP) Mr 海峰 赵 (IHEP)

Presentation materials

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