CAS OpenIR  > 中科院上海应用物理研究所2011-2020年
Synchrotron Big Data Science
Wang, CP; Steiner, U; Sepe, A
2018
Source PublicationSMALL
ISSN1613-6810
Volume14Issue:46Pages:-
Subtype期刊论文
AbstractThe rapid development of synchrotrons has massively increased the speed at which experiments can be performed, while new techniques have increased the amount of raw data collected during each experiment. While this has created enormous new opportunities, it has also created tremendous challenges for national facilities and users. With the huge increase in data volume, the manual analysis of data is no longer possible. As a result, only a fraction of the data collected during the time- and money-expensive synchrotron beam-time is analyzed and used to deliver new science. Additionally, the lack of an appropriate data analysis environment limits the realization of experiments that generate a large amount of data in a very short period of time. The current lack of automated data analysis pipelines prevents the fine-tuning of beam-time experiments, further reducing their potential usage. These effects, collectively known as the "data deluge," affect synchrotrons in several different ways including fast data collection, available local storage, data management systems, and curation of the data. This review highlights the Big Data strategies adopted nowadays at synchrotrons, documenting this novel and promising hybridization between science and technology, which promise a dramatic increase in the number of scientific discoveries.
KeywordCONVOLUTIONAL NEURAL-NETWORK POWDER DIFFRACTION FILE PROTEIN DATA-BANK FORMAT NEXUS CRYSTALLOGRAPHY INFORMATION ARCHIVE IMAGES CLASSIFICATION
DOI10.1002/smll.201802291
Indexed BySCI
Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sinap.ac.cn/handle/331007/31129
Collection中科院上海应用物理研究所2011-2020年
Affiliation1.Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai Synchrotron Radiat Facil, Big Data Sci Ctr, Shanghai 201204, Peoples R China
2.Univ Fribourg, Adolphe Merkle Inst, CH-1700 Fribourg, Switzerland
Recommended Citation
GB/T 7714
Wang, CP,Steiner, U,Sepe, A. Synchrotron Big Data Science[J]. SMALL,2018,14(46):-.
APA Wang, CP,Steiner, U,&Sepe, A.(2018).Synchrotron Big Data Science.SMALL,14(46),-.
MLA Wang, CP,et al."Synchrotron Big Data Science".SMALL 14.46(2018):-.
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