title: Crystallographic Data and Model Quality.
abstract: This article gives a consistent classification of sources of random and systematic errors in crystallographic data, and their influence on the averaged dataset obtained from a diffraction experiment. It discusses the relation between precision and accuracy and the crystallographic indicators used to estimate them, as well as topics like completeness and high-resolution cutoff. These concepts are applied in the context of presenting good practices for data processing with a widely used package, XDS. Recommendations are given for how to minimize the impact of several typical problems, like ice rings and shaded areas. Then, procedures for optimizing the processing parameters are explained. Finally, a simple graphical expression of some basic relations between data error and model error is suggested.
authors.affiliation: Department of Biology, Universität Konstanz, Box 647, Konstanz, 78457, Germany, Kay.Diederichs@uni-konstanz.de.
authors.forename: Kay
authors.initials: K
authors.lastname: Diederichs
mesh_terms: D000465:Algorithms; D018360:Crystallography, X-Ray / Q000379:methods*; D004247:DNA / Q000737:chemistry; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D015233:Models, Statistical; D008967:Molecular Biology / Q000379:methods; D011506:Proteins / Q000737:chemistry; D012313:RNA / Q000737:chemistry; D015203:Reproducibility of Results; D012984:Software
chemical_list: D011506:Proteins; D012313:RNA; D004247:DNA