Scientific data articles from across Nature Portfolio

Human mobility research intersects with various disciplines, with profound implications for urban planning, transportation engineering, public health, disaster management, and economic analysis. Here, we discuss the urgent need for open and standardized datasets in the field, including current challenges and lessons from other computational science domains, and propose collaborative efforts to enhance the validity and reproducibility of human mobility research.

As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models.

Approaches are needed to accelerate the discovery of transition metal complexes (TMCs), which is challenging owing to their vast chemical space. A large dataset of diverse ligands is now introduced and leveraged in a multiobjective genetic algorithm that enables the efficient optimization of TMCs in chemical spaces containing billions of them.

Dr Zhimei Sun – professor of Materials Science and Engineering at Beihang University – talks to Nature Computational Science about her career trajectory, her research on computational materials science and materials informatics, as well as her advice to young women scientists in these fields.

In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.

GRAPE is a software resource for graph processing, learning and embedding that is orders of magnitude faster than existing state-of-the-art libraries. GRAPE can quickly process real-world graphs with millions of nodes and billions of edges, enabling complex graph analyses and research in graph-based machine learning and in diverse disciplines.