Automatic processing of multimodal tomography datasets.
Automatic processing of multimodal tomography datasets.
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DOI：
10.1107/s1600577516017756
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发表时间：
2017-01-01
影响因子：
2.5
通讯作者：
Quinn PD
中科院分区：
文献类型：
--
作者：
Parsons AD;Price SW;Wadeson N;Basham M;Beale AM;Ashton AW;Mosselmans JF;Quinn PD
多模态化学层析成像是一种必须处理各种大数据集（>100 GB）的技术。 在这里，提出了一种使用基于Python的大数据解决方案来分析此类数据的新方法。随着第四代高亮度同步加速器的发展，将在成像和映射光束线上收集的大量数据将以数量级的方式增加。因此，需要一种简单易行的方法来尽快处理如此大的数据集，以便能够在实验数据收集期间解决核心科学问题。Savu是一个可访问且灵活的大数据处理框架，能够处理多模态和多维科学数据集输出的各种数据和数据量，例如在Diamond Light Source的I18微焦点扫描光束线上进行的化学层析成像实验。
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. 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.