Materials research faces a significant data management challenge as experimental information often exists in manufacturer-specific formats with inconsistent terminology, making aggregation, comparison, and reuse difficult. Researchers traditionally spend considerable time on tedious tasks like format conversion, metadata assignment, and characteristics extraction, which can discourage data sharing and hinder data-driven work. This problem is particularly acute given the field's increasing reliance on AI-driven materials discovery, which requires high-quality datasets.
To address this problem, researchers at Japan's 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, 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 findability, 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."
The system's core innovation is the "Dataset Template," which defines and directs how data from different types of experiments should be processed. Unlike similar systems that typically define data formats, RDE's templates can be configured to interpret spreadsheets of X-ray measurements from different sources, for example. The system then automatically performs advanced analyses and creates visualizations to provide immediate overviews. Multiple templates can be prepared for different materials research themes, allowing maximum flexibility in data management, and individual researchers can easily prepare custom templates when necessary.
"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 scalability with over 5,000 users, 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 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 system use within the research community. The research paper detailing RDE's development is available at https://doi.org/10.1080/27660400.2025.2597702.
