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Better models by discarding data?

机译:更好的模型通过丢弃数据吗?

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摘要

In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with 'merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled 'paired-refinement' tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.
机译:在大分子x射线晶体学,典型的数据集有巨大的多样性。被用来计算重复的一致性测量,从而评估数据质量。最近,一个相关的属性CC1/2系数,可以使用目的特征,结果表明:与相比CC1/2具有优越的性能“合并”R值。数据和模型质量的链接。数据集,CC1/2和更多的行为常规指标比较两种情况下的实际重要性:合并数据从不同的水晶和选择性地设置拒绝弱观测或(合并)独一无二的反射从一个数据集。控制测试表明,“paired-refinement”即使丢弃导致较弱的数据改进的R值合并,雅致模型基于这些数据是低质量的。这些结果显示这样的愚蠢数据过滤实践旨在改善合并R值。测试CC1/2数据质量指标这一行为准确地反映哪的选择数据处理策略的结果在高质量的改进模型。系统误差的存在记录和讨论。

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