Crystallographic methods and equipment keep evolving fast. Today, there seems to be almost no limit to technical hardware and software questions as well as to limitations on computational resources in crystallographic applications. Instead of collecting more and even more data it now seems to become more and more important to also use all of this data to generate knowledge and meaning from it. Traditionally, only information referring to structure models or charge density models are used. However, each diffraction experiment produces much more data, which much too often is just given away without deeper examination. In particular, every diffraction experiment requires many decisions about data acquisition strategies and measurement parameters, data processing parameters and model refinement parameters. These decisions determine and sometimes also limit the data quality and therefore the model quality. Our goal is to focus on the extreme valuable information hidden in the residuals. The decoding of this information paves the road to higher data quality, higher precision and accuracy of the data, measurement cost reduction and constant increase of data quality by continuous evaluation of data quality. In this way, new scientific insights will become possible and some old may need to be revised.

The foundation of data quality assessment is to have good and reliable data quality descriptors and to know how to apply these and also how not to apply these. The one-day crash course will give some basic information about these topics and a demonstration of our software for the identification of systematic errors in diffraction data.