On-the-fly data assessment for high-throughput x-ray diffraction measurements [electronic resource].
- Washington, D.C. : United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy, 2017. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 377-385 : digital, PDF file
- Additional Creators:
- SLAC National Accelerator Laboratory, United States. Department of Energy. Office of Energy Efficiency and Renewable Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for the discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through the development of an approach that makes routine data assessment automatic and instantaneous. By extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large data sets, is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize the usage of expensive characterization resources by prioritizing measurements of the highest scientific impact. We anticipate our approach will become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. Finally, with these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.
- Published through SciTech Connect., 05/02/2017., ACS Combinatorial Science 19 6 ISSN 2156-8952 AM, and Fang Ren; Ronald Pandolfi; Douglas Van Campen; Alexander Hexemer; Apurva Mehta.
- Funding Information:
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