A comprehensive description of the methods used within the DIALS framework for diffraction-geometry refinement using predicted reflection centroids is given. Examples of the advanced features of the software are provided.

The design and usage of Scipion-ED, a graphical user interface program for processing and analysis of three-dimensional electron diffraction (3D ED)/microcrystal electron diffraction (MicroED) data, are presented. A study of the influence of data merging strategies on the ability to resolve unmodelled features of tetragonal lysozyme is included as an illustration of the advantages of Scipion-ED.

Bragg Spot Finder (BSF) is a U-Net-based spotfinder with image preprocessing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. BSF is supported by the Bragg Spot Detection (BSD) benchmark image dataset containing more than 300 images with more than 66 000 spots.

Finback is a biological macromolecular crystallography data collection system that features a user-friendly interactive web-based graphical user interface. The backend is based on the Experimental Physics and Industrial Control System and the frontend has been developed with several modern network technologies including WebSocket, WebGL, WebWorker and WebAssembly.

This article describes the Collaborative Computational Project No. 4 (CCP4). It is intended as a general literature citation for the use of the CCP4 software suite in structure determination.

Serial crystallography (SX) requires efficient processing of numerous diffraction patterns. TORO Indexer is a high-performance indexing solution that operates across various platforms such as GPUs, CPUs and TPUs, offering high processing speed without compromising indexing quality. Its design ensures easy integration into existing software, making it a useful tool for evolving SX techniques with ever-expanding data volumes.

A new program, xia2.multiplex, has been developed to facilitate symmetry analysis, scaling and merging of multi-crystal data sets.

Geometry correction is performed with separation of Euclidean and non-Euclidean movements with respect to the sample, refined against independent target functions derived from the data. This leads to substantial improvements in indexing rates and data quality indicators and refines to convergence.