Early-career researchers propose seven actions that can be taken by research institutions and departments to improve science training.

Researchers and administrators can integrate reproducible research and open science practices into daily practice at their research institutions by adapting research assessment criteria and program requirements, offering training, and building communities.

An analysis of the career and publication outcomes for 2284 early career researchers trained at EMBL provides insights into the evolving career landscape for life scientists.

A deep neural network trained on semi-synthetic data learns to quickly track and identify neurons in Caenorhabditis elegans.

Rapid, label-free, volumetric, and automated assessment of the immunological synapse dynamics is demonstrated by combining optical diffraction tomography and deep-learning-based segmentation, providing a new option for immunological research.

Prediction of future input explains diverse neural tuning properties in sensory cortex.

An open-source tool based on computational vision and machine learning shows high accuracy and sensitivity in the analysis of ultrasonic vocalizations.

A deep learning-based pipeline was developed for extracting cellular signals flexibly from moving cells in 3D time lapse images, and it outperformed previous methods under different imaging conditions.

A standard for complex mouse behavior that can be successfully reproduced across laboratories, along with open-access data and tools to implement provide a resource for reproducibility through collaborative open-science approaches.

A new method for analyzing continuous psychophysics experiments estimates not only perceptual uncertainty from tracking tasks but also action variability, intrinsic costs, and subjective internal models.