Quantitative text analysis is a range of computational methods to analyse text data statistically and mathematically. In this Primer, Kristoffer Nielbo et al. introduce the methods, principles and applications of the quantitative text analysis across disciplines.

Deep tissues can be imaged with high resolution and greater contrast by performing fluorescence imaging in the second near-infrared (NIR-II) window. This Primer summarizes how NIR-II fluorescence imaging can be used in animal models, exploring commonly used fluorophores and implementation approaches across a range of scientific and clinical applications.

Analysing single-molecule fluorescence resonance energy transfer (smFRET) enables an unprecedented view of the dynamics and kinetics of biomolecular conformational changes and interactions. In this Primer, Ha et al. discuss technological advances that have led to smFRET and how the method can be adapted to address various research questions in structural and molecular biology.

Biocarbons are carbonaceous solids derived from renewable and sustainable feedstocks through thermochemical conversion at high temperatures. In this Primer, Mohanty et al. discuss feedstock selection, pyrolysis techniques and post-modification strategies, as well as waste reduction and the economic impact of biocarbons.

Graph neural networks are a class of deep learning methods that can model physical systems, generate new molecules and identify drug candidates. This Primer introduces graph neural networks and explores how they are applied across the life and physical sciences.

Structures of surfaces and thin films can be investigated by performing X-ray diffraction under grazing incidence conditions. This Primer explores how grazing incidence X-ray diffraction is used to obtain crystallographic information, including in situ characterization, data collection, analysis and visualization, across a range of applications.