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Automatic processing of multimodal tomography datasets

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 quickl...

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Autores principales: Parsons, Aaron D., Price, Stephen W. T., Wadeson, Nicola, Basham, Mark, Beale, Andrew M., Ashton, Alun W., Mosselmans, J. Frederick. W., Quinn, Paul. D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5182025/
https://www.ncbi.nlm.nih.gov/pubmed/28009564
http://dx.doi.org/10.1107/S1600577516017756
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author Parsons, Aaron D.
Price, Stephen W. T.
Wadeson, Nicola
Basham, Mark
Beale, Andrew M.
Ashton, Alun W.
Mosselmans, J. Frederick. W.
Quinn, Paul. D.
author_facet Parsons, Aaron D.
Price, Stephen W. T.
Wadeson, Nicola
Basham, Mark
Beale, Andrew M.
Ashton, Alun W.
Mosselmans, J. Frederick. W.
Quinn, Paul. D.
author_sort Parsons, Aaron D.
collection PubMed
description 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.
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spelling pubmed-51820252017-01-10 Automatic processing of multimodal tomography datasets Parsons, Aaron D. Price, Stephen W. T. Wadeson, Nicola Basham, Mark Beale, Andrew M. Ashton, Alun W. Mosselmans, J. Frederick. W. Quinn, Paul. D. J Synchrotron Radiat Research Papers 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. International Union of Crystallography 2017-01-01 /pmc/articles/PMC5182025/ /pubmed/28009564 http://dx.doi.org/10.1107/S1600577516017756 Text en © Aaron D. Parsons et al. 2017 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Parsons, Aaron D.
Price, Stephen W. T.
Wadeson, Nicola
Basham, Mark
Beale, Andrew M.
Ashton, Alun W.
Mosselmans, J. Frederick. W.
Quinn, Paul. D.
Automatic processing of multimodal tomography datasets
title Automatic processing of multimodal tomography datasets
title_full Automatic processing of multimodal tomography datasets
title_fullStr Automatic processing of multimodal tomography datasets
title_full_unstemmed Automatic processing of multimodal tomography datasets
title_short Automatic processing of multimodal tomography datasets
title_sort automatic processing of multimodal tomography datasets
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5182025/
https://www.ncbi.nlm.nih.gov/pubmed/28009564
http://dx.doi.org/10.1107/S1600577516017756
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