Building the data infrastructure for next-generation materials science

Researchers at NIMS have created Research Data Express (RDE) to automate data processing and create AI-ready datasets for materials research.

A highly flexible data system automatically interprets a variety of experimental data, and stores it in a format with enhanced readability for materials informatics.

Materials research generates vast amounts of data, but the information often exists in manufacturer-specific formats and the terminology is inconsistent, making it difficult to aggregate, compare, and reuse. Traditionally, researchers have had to spend considerable time on tedious tasks, such as format conversion, metadata assignment, and characteristics extraction. These extra steps can make researchers reluctant to share data, hindering the advancement of data-driven work. The problem is made even more acute by the field’s increasing reliance on AI-driven materials discovery, which requires high-quality datasets.

To address this problem, researchers at the National Institute for Materials Science (NIMS) have developed Research Data Express (RDE), a highly flexible data management system for materials scientists. Published in Science and Technology of Advanced Materials: Methods, RDE automatically interprets experimental data from raw files and manually inputted measurements. It then restructures and stores this information in a format with enhanced readability.

“RDE significantly reduces the burden of routine data processing for researchers and enhances data find-ability, interoperability, reusability (the FAIR principles), and traceability,” explains Jun Fujima, corresponding author and researcher at NIMS’s Materials Data Platform. “We hope this will promote collaborative, data-driven materials research.”

Many systems of a similar purpose usually “define” the data format. In contrast, the RDE’s core innovation “Dataset Template” defines and directs how data from different types of experiments should be processed. For example, if a researcher uploads spreadsheets of X-ray measurements from different sources, the Dataset Template can be configured to interpret them. The system then automatically performs advanced analyses and creates visualisations to provide an immediate overview. Multiple templates can be prepared for different materials research themes, allowing for maximum flexibility in data management. A custom template can also be easily prepared by individual researchers if necessary. Many templates have already been prepared and shared among users.

“RDE’s unique approach allows researchers to freely define data structures tailored to their instruments, while enabling the system to perform massive data structuring and metadata extraction automatically,” says Fujima.

Since its launch in January 2023, RDE has been widely adopted across Japan’s materials research community, demonstrating its scalability. To date, it has over 5,000 users, with more than 1,900 Dataset Templates for various experimental methods implemented, over 16,000 datasets created, and more than three million data files accumulated. The system serves as a data infrastructure for major national initiatives, including the Materials Research DX Platform initiative promoted by Japan’s Ministry of Education, Culture, Sports, Science and Technology. The NIMS team has released an open-source software toolkit (RDEToolKit) to encourage use of the system within the research community.

About Science and Technology of Advanced Materials: Methods (STAM-M) STAM Methods is an open access sister journal of Science and Technology of Advanced Materials (STAM), and focuses on emergent methods and tools for improving and/or accelerating materials developments, such as methodology, apparatus, instrumentation, modeling, high-through put data collection, materials/process informatics, databases, and programming. https://www.tandfonline.com/STAM-M

This article was issued via Press Release as " Building the data infrastructure for next-generation materials science" dated 18 January, 2026.

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