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This article explores the challenges and solutions in data reduction at the European XFEL facility, which produces X-ray pulses. The facility generates a large amount of data during experiments, which presents difficulties in storage and interpretation. The article outlines the data infrastructure and various techniques for data reduction, aiming to maximize scientific value while minimizing strain on the infrastructure. The facility has successfully implemented reduction methods to decrease data storage volume and has assessed the risks associated with data reduction. Data reduction is important for scientific research, benefiting users by improving decision-making and simplifying data analysis, as well as having environmental benefits. The article emphasizes the collaboration between facility experts and users to ensure effective data reduction strategies and excellent scientific results. [Extracted from the article]

The European X-ray Free Electron Laser is a research facility located close to Hamburg, offering X-ray pulses with ultra-high brilliance and femtosecond duration at megahertz repetition rates. The detection systems necessary to unlock the full scientific potential made possible by this machine poses considerable challenges both in terms of data volume and rate, as well as the interpretation of their recorded signal. To provide optimal data quality, expert and detector-specific knowledge not easily accessible to external facility users is essential, and its implementation must cope with the generated volumes. We therefore aim to perform these preparatory processing steps and offer users a dataset suitable for further analysis as the primary data product. This work describes the machinery and workflows providing this data to users in an automatic, configurable and reproducible manner, both online during the experiment, and offline for scientific analysis afterward on the way to publication. [ABSTRACT FROM AUTHOR]

After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography. [ABSTRACT FROM AUTHOR]

Structural studies of challenging targets such as G protein-coupled receptors (GPCRs) have accelerated during the last several years due to the development of new approaches, including small-wedge and serial crystallography. Here, we describe the deposition of seven datasets consisting of X-ray diffraction images acquired from lipidic cubic phase (LCP) grown microcrystals of two human GPCRs, Cysteinyl leukotriene receptors 1 and 2 (CysLT1R and CysLT2R), in complex with various antagonists. Five datasets were collected using small-wedge synchrotron crystallography (SWSX) at the European Synchrotron Radiation Facility with multiple crystals under cryo-conditions. Two datasets were collected using X-ray free electron laser (XFEL) serial femtosecond crystallography (SFX) at the Linac Coherent Light Source, with microcrystals delivered at room temperature into the beam within LCP matrix by a viscous media microextrusion injector. All seven datasets have been deposited in the open-access databases Zenodo and CXIDB. Here, we describe sample preparation and annotate crystallization conditions for each partial and full datasets. We also document full processing pipelines and provide wrapper scripts for SWSX and SFX data processing. Measurement(s) X-ray diffraction data • protein complex • protein structure data • protein crystallization Technology Type(s) small-wedge synchrotron crystallography • x-ray crystallography assay • X-ray free electron laser serial femtosecond crystallography Factor Type(s) type of G-protein-coupled receptor • type of antagonist Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13128758 [ABSTRACT FROM AUTHOR]