The Small-Angle Scattering Biological Data Bank (SASBDB) has surpassed 5000 deposited data sets, highlighting its role as a central open-access resource for biological small-angle scattering data and its growing impact on method development, benchmarking and data-driven analysis.

A strategy based on the central moment expansion approach is proposed for addressing the resolution discrepancy inherent in cross-sectional measurements obtained via small-angle scattering experiments.

We critically examine mathematical tools to invert the orientation distribution of flowing elongated objects from anisotropic small-angle scattering data. This evaluation aims to advance understanding of the interplay between flow dynamics and object orientation, benefiting fluid dynamics and materials science.

A Monte Carlo based method is used to efficiently analyze small-angle scattering curves of large biomolecular structures. Structural features are obtained from scattering data of large tube- and sheet-like protein assemblies.

It is demonstrated that, when a semiflexible polymer is under external forces, these forces and the bending modulus, along with conformation variables of the polymer, can be extracted from the scattering function using a machine learning inversion method.

The virtual special issue related to the 19th International Small Angle Scattering Conference (SAS2024, Taipei, Taiwan) is introduced. The articles included here were originally published in recent regular issues of Journal of Applied Crystallography and Journal of Synchrotron Radiation. The SAS2024 special issue is available at https://journals.iucr.org/special_issues/2025/sas2024/.

Laboratory small-angle X-ray scattering (SAXS) equipment was employed at the SAXS station at NanoTerasu (Sendai, Japan) for SAXS and wide-angle X-ray diffraction (WAXD) measurements.

ATSAS is a comprehensive software suite for the processing, visualization, analysis and modeling of small-angle scattering data. This article describes developments in the ATSAS-4 release series.

This study employs Kolmogorov–Arnold networks to analyze neutron and X-ray scattering from distorted lamellar phases within a wave field representation, identifying topological defects and structural transitions. It offers insights into defect structures and their impact on material properties.

The influence of optical inhomogeneities of polydisperse particles on measurable structure factors is analysed.

A deep learning approach has been developed for analyzing small-angle scattering data, effectively addressing the potential inversion problem in colloids. The method is validated using both simulation results and experimental spectra of charged silica suspensions and is shown to outperform existing approaches in accuracy and efficiency. This study highlights the potential of deep learning in soft-matter research.

A Bayesian method is proposed for quantitatively selecting a mathematical model of a sample for small-angle scattering. The performance of this method is evaluated through numerical experiments on artificial data for a sample containing a mixture of multiple spherical particles.