Ren Fang, Pandolfi Ronald, Van Campen Douglas, Hexemer Alexander, Mehta Apurva
Stanford Synchrotron Radiation Lightsource , SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.
Advanced Light Source, Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States.
ACS Comb Sci. 2017 Jun 12;19(6):377-385. doi: 10.1021/acscombsci.7b00015. Epub 2017 May 24.
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. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.
对更明亮的光源以及更大、更快的探测器的投资，加快了国家用户设施的数据采集速度。加速的数据采集为新材料的发现提供了许多机会，但也带来了艰巨的挑战。数据采集速率远远超过了当前数据质量评估的速度，导致数据及数据覆盖情况不尽如人意，在极端情况下甚至需要重新采集数据。在此，我们展示了如何通过开发一种使常规数据评估自动且即时的方法来应对这一挑战。通过实时提取和可视化定制属性，突出了大数据集中的数据质量和覆盖范围以及其他科学相关信息。部署这样的方法不仅能提高数据质量，还能通过优先安排具有最高科学影响力的测量，帮助优化昂贵表征资源的使用。我们预计我们的方法将成为一个复杂决策树的起点，该决策树通过自动化实时优化数据质量并最大化科学内容。通过这些在数据收集和分析中集成更多自动化的努力，我们能够真正利用数据采集加速的优势。
