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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1107/S1600577516017756
|2 doi
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|a pubmed24n0891.xml
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|a eng
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1 |
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|a Parsons, Aaron D
|e verfasserin
|4 aut
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1 |
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|a Automatic processing of multimodal tomography datasets
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|c 2017
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 11.07.2017
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|a Date Revised 11.11.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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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|a Journal Article
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|a big data
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|a imaging
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|a mapping
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|a multimodal
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|a tomography
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|a Price, Stephen W T
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wadeson, Nicola
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Basham, Mark
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Beale, Andrew M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ashton, Alun W
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mosselmans, J Frederick W
|e verfasserin
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1 |
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|a Quinn, Paul D
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of synchrotron radiation
|d 1994
|g 24(2017), Pt 1 vom: 01. Jan., Seite 248-256
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|x 1600-5775
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|g volume:24
|g year:2017
|g number:Pt 1
|g day:01
|g month:01
|g pages:248-256
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