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.
Highlights
Following integration of the spots on a set of X-ray diffraction images to produce a list of reflection intensities, a series of operations are performed on the data, usually referred to as ‘data reduction’
This paper describes a new data-scaling program, AIMLESS, and discusses criteria for deciding the ‘resolution’ of a measured data set
The extensive statistics produced by AIMLESS are mostly similar to those produced by SCALA, so the questions and decisions for the user are as discussed in Evans (2011): (i) What is the point group (Laue group)? (ii) What is the space group? (iii) Is there radiation damage and should the most damaged regions of data be excluded? (iv) What resolution cutoff should be applied? (v) Is there a detectable anomalous signal? (vi) Are the data twinned? (vii) Is this data set better than those previously collected?
Summary
Following integration of the spots on a set of X-ray diffraction images to produce a list of reflection intensities, a series of operations are performed on the data, usually referred to as ‘data reduction’ These processes include determination of the point group and if possible the space group, checking for consistent indexing where there are alternatives, putting all of the data on a common scale, deciding whether to reject parts of the collected data or to cut the resolution and estimating the structure amplitude |F| from intensity. Scaling attempts to correct for contributions to the measured intensities arising from experimental conditions which vary during data collection, such as variations in the incident-beam intensity, the volume of the crystal illuminated, absorption in the primary or secondary beam and average radiation damage This is performed by trying to make all symmetry-related or replicated measurements of a reflection intensity equal, i.e. to make the data as internally consistent as possible. In the CCP4 context (Winn et al, 2011), AIMLESS is used immediately after the program POINTLESS, which determines the likely point group and possible space group, as well as optionally combining data from multiple files each containing a ‘sweep’ of data and putting them on a common indexing system if necessary (Evans, 2011). AIMLESS is followed by CTRUNCATE, which calculates the structure amplitudes |F| from the intensities and outputs various intensity statistics mainly to detect twinning
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