Three-dimensional, multimodal synchrotron data for machine learning applications
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features before spatially resolved X-ray diffraction computed tomography was carried out to characterise the topographical distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data are available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used to develop machine learning techniques. Such techniques include the development of super-resolution, multimodal data fusion, and 3D reconstruction algorithms.
Date Issued
2025-02-24
Date Acceptance
2025-02-07
Citation
Scientific Data, 2025, 12
ISSN
2052-4463
Publisher
Nature Portfolio
Journal / Book Title
Scientific Data
Volume
12
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s41597-025-04605-9
Publication Status
Published
Article Number
329
Date Publish Online
2025-02-24