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Automatic processing of multimodal tomography datasets

DOI: 10.1107/S1600577516017756 DOI Help

Authors: Aaron D. Parsons (Diamond Light Source) , Stephen W. T. Price (Diamond Light Source) , Nicola Wadeson (Diamond Light Source) , Mark Basham (Diamond Light Source) , Andrew M. Beale (Research Complex at Harwell; University College London) , Alun W. Ashton (Diamond Light Source) , J. Frederick. W. Mosselmans (Diamond Light Source) , Paul D. Quinn (Diamond Light Source)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Journal Of Synchrotron Radiation , VOL 24 , PAGES 248 - 256

State: Published (Approved)
Published: January 2017

Open Access Open Access

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.

Journal Keywords: big data; multimodal; imaging; mapping; tomography.

Subject Areas: Technique Development, Information and Communication Technology, Materials


Instruments: I18-Microfocus Spectroscopy

Documents:
mo5150.pdf