PDF(Pubmed)
Abstract：
Following integration of the observed diffraction spots, the process of `data reduction\' initially aims to determine the point-group symmetry of the data and the likely space group. This can be performed with the program POINTLESS. The scaling program then puts all the measurements on a common scale, averages measurements of symmetry-related reflections (using the symmetry determined previously) and produces many statistics that provide the first important measures of data quality. A new scaling program, AIMLESS, implements scaling models similar to those in SCALA but adds some additional analyses. From the analyses, a number of decisions can be made about the quality of the data and whether some measurements should be discarded. The effective `resolution\' of a data set is a difficult and possibly contentious question (particularly with referees of papers) and this is discussed in the light of tests comparing the data-processing statistics with trials of refinement against observed and simulated data, and automated model-building and comparison of maps calculated with different resolution limits. These trials show that adding weak high-resolution data beyond the commonly used limits may make some improvement and does no harm.
摘要：
在对观察到的衍射点进行积分之后，“数据缩减”的过程最初旨在确定数据的点群对称性和可能的空间群。这可以在程序POINTLESS的情况下执行。然后，缩放程序将所有测量值放在一个共同的刻度上，平均对称相关反射的测量（使用之前确定的对称性），并产生许多统计数据，这些统计数据提供了数据质量的第一个重要度量。一个新的缩放程序，AIMLESS,实现与SCALA中类似的缩放模型，但添加了一些其他分析。从分析来看，可以对数据的质量以及是否应该丢弃一些测量做出许多决定。数据集的有效“分辨率”是一个困难的，可能是有争议的问题（特别是对于论文的裁判），这是根据将数据处理统计数据与对观察和模拟数据的改进试验进行比较的测试进行讨论的。以及自动建模和比较不同分辨率限制下计算的地图。这些试验表明，添加超出常用限制的弱高分辨率数据可能会有所改善，并且不会造成任何伤害。
