Visual Analytics for Scientific Data in NSLS-II
Citation
Xu, W.; Ha, S.; Zhong, W.; Cheng, S.; Yan, H.; Yager, K.G.; Mueller, K.; Huang, X.; Fukuto, M.; Chu, Y. "Visual Analytics for Scientific Data in NSLS-II"
Handbook on Big Data and Machine Learning in the Physical Sciences, Volume 2: Advanced Analysis Solutions for Leading Experimental Techniques, World Scientific 2020,
Chapter 9 ISBN 978–981–120–444–9.
doi: 10.1142/9789811204579_0009Summary
This chapter discusses visualization approaches for synchrotron data.
Abstract
X-ray images obtained from synchrotron beamlines are extreme-scale, high-resolution and high-dynamic-range grayscale data encoding multiple complex properties of the measured materials. They are typically associated with a variety of metadata which increases their inherent complexity. There is a wealth of information embedded in these data but so far scientists lack modern exploration tools to unlock these hidden treasures. To bridge this gap, we devise two visual analytics systems ColorMapND and MultiSciView to facilitate scientists on complex material science experiments. We collaborate with Hard X-ray Nanoprobe (HXN) and Complex Materials Scattering (CMS) groups, and target the analysis tasks for fluorescence images and scattering images, respectively. Our methods are demonstrated to enhance scientific understanding, increase the pace of scientific discovery, and improve the usability of data and data analysis methods.
