Materials research generates vast amounts of data that 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 these challenges, 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, 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.
Unlike similar systems that define data formats, RDE's core innovation is the "Dataset Template" that 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 visualizations to provide an immediate overview. Multiple templates can be prepared for different materials research themes, allowing for maximum flexibility in data management.
Since its launch in January 2023, RDE has been widely adopted across Japan's materials research community, demonstrating its scalability. The system currently 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. RDE 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. The system's development and methodology are detailed in a paper available at https://doi.org/10.1080/27660400.2025.2597702, providing technical specifications and implementation details for the research community. This development represents a significant step toward addressing the data management challenges that have long hampered progress in materials science research.
