Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
<|reference_start|>Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications: 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 homogeneous distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data is available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used for the development of machine learning techniques. Such techniques include development of super-resolution, multimodal data fusion, and 3D reconstruction algorithm development.<|reference_end|>
@article{green2024three-dimensional,,
    title={Three-Dimensional, Multimodal Synchrotron Data for Machine Learning
  Applications},
    author={Calum Green, Sharif Ahmed, Shashidhara Marathe, Liam Perera, Alberto
  Leonardi, Killian Gmyrek, Daniele Dini, James Le Houx},
    journal={arXiv preprint arXiv:2409.07322},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07322},
    primaryClass={cs.LG eess.IV}
}