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240828s2024 xx |||||o 00| ||eng c |
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|a 10.1107/S1600577524007239
|2 doi
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|a pubmed24n1524.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Wang, Chunpeng
|e verfasserin
|4 aut
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| 245 |
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|a Accelerating imaging research at large-scale scientific facilities through scientific computing
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|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 05.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a open access.
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|a To date, computed tomography experiments, carried-out at synchrotron radiation facilities worldwide, pose a tremendous challenge in terms of the breadth and complexity of the experimental datasets produced. Furthermore, near real-time three-dimensional reconstruction capabilities are becoming a crucial requirement in order to perform high-quality and result-informed synchrotron imaging experiments, where a large amount of data is collected and processed within a short time window. To address these challenges, we have developed and deployed a synchrotron computed tomography framework designed to automatically process online the experimental data from the synchrotron imaging beamlines, while leveraging the high-performance computing cluster capabilities to accelerate the real-time feedback to the users on their experimental results. We have, further, integrated it within a modern unified national authentication and data management framework, which we have developed and deployed, spanning the entire data lifecycle of a large-scale scientific facility. In this study, the overall architecture, functional modules and workflow design of our synchrotron computed tomography framework are presented in detail. Moreover, the successful integration of the imaging beamlines at the Shanghai Synchrotron Radiation Facility into our scientific computing framework is also detailed, which, ultimately, resulted in accelerating and fully automating their entire data processing pipelines. In fact, when compared with the original three-dimensional tomography reconstruction approaches, the implementation of our synchrotron computed tomography framework led to an acceleration in the experimental data processing capabilities, while maintaining a high level of integration with all the beamline processing software and systems
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|a Journal Article
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|a automation
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|a imaging
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| 650 |
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|a scientific computing
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| 650 |
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|a synchrotron
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| 650 |
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4 |
|a tomography
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| 700 |
1 |
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|a Li, Xiaoyun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wan, Rongzheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Jige
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ye, Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Ke
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Aiguo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tai, Renzhong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sepe, Alessandro
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of synchrotron radiation
|d 1994
|g 31(2024), Pt 5 vom: 01. Sept., Seite 1317-1326
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|x 1600-5775
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|g volume:31
|g year:2024
|g number:Pt 5
|g day:01
|g month:09
|g pages:1317-1326
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|u http://dx.doi.org/10.1107/S1600577524007239
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