eLife is introducing a new article type—called Tools and Resources—to highlight new experimental techniques, datasets, software tools and other resources.

A new data analysis tool provides a concise way of visualizing neural data that summarizes all the relevant features of the population response in a single figure.

An open-source tool for computational simulations of the human genome has been introduced, enabling the characterization of complex nuclear environments and the interpretation of experimental observations.

Visualization methods display the dynamics of the currents in conductance-based model neurons, and show how their contribution changes in response to perturbation.

Under fluid shear, living cells deform elliptically, align in flow direction, and rotate, from which their frequency-dependent viscoelastic properties can be inferred.

A robust fluorescence microscopy-based data acquisition and analysis framework affords the precise measurement of cell surface receptor affinities toward their cognate ligands and their densities in live cells/tissues.

Mapping spike patterns from a small identified circuit reveals the diversity and complexity of circuit dynamics across animals and in response to perturbations.

An open-source python package for phenotype analyses provides a versatile, modular and user-friendly solution to determine complementary fitness-related traits from large-scale assays of microbial colonies.

A Bayesian model of phase angles illustrates a novel approach to the analysis of phase coherence in frequency-tagged experiments.

Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.