Automatic processing of multimodal tomography datasets. (20th December 2016)

• Record Type: Journal Article
• Authors: Parsons, Aaron D.; Price, Stephen W. T.; Wadeson, Nicola; Basham, Mark; Beale, Andrew M.; Ashton, Alun W.; Mosselmans, J. Frederick. W.; Quinn, Paul. D.
• Is Part Of: Journal of synchrotron radiation. Volume 24:Part 1(2017)
• Page Start: 248
• Page End: 256
• Publication Date: 2016-12-20
• Subjects: big data -- multimodal -- imaging -- mapping -- tomography; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577516017756
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : Multimodal chemical tomography is a technique that has to deal with a variety of big (>100 GB) datasets. Here, a novel method for approaching the analysis of such data using a Python‐based big data solution is presented. Abstract : With the development of fourth‐generation high‐brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source.