EXTENDED ABSTRACT: Ihe development of FAIR (Findable, Accessible, Interoperable, and Reusable) data is critical to advancing AI research, particularly in materials science [1]. Efforts to accumulate computer simulation data in a FAIR manner during the early stages of basic research have been successful [2]. However, extending these efforts to experimental data faces challenges due to differences in data quality and recording conditions. The Research Data Express (RDE) system at the National Institute for Materials Science addresses these issues by ensuring FAIRness during data submission. This system has already been highly successful and has become an indispensable tool for preparing FAIR data. The RDE system offers functions for inputting relevant data during the registration of measurement data and for the ETL (Extract, Transform, Load) process of input data. These functions enable advanced customization to meet various experimental needs and efffciently consolidate different types of data. Managing experimental results as FAIR data using the RDE system is expected to save both resources and time. Improved customization guidelines and better integration with infrastructure will further enhance the RDE's role as a crucial gateway for FAIR data in experimental studies, thereby increasing the reproducibility and usefulness of research data.

Keywords: FAIR; research data management; data platform;

REFERENCES:

[1] Schefffer, M., Aeschlimann, M., Albrecht, M. et al. FAIR data enabling new horizons for materials research. Nature 604, 635–642 (2022). https://doi.org/10.1038/s41586-022-04501-x

[2] Armiento, R.(2020). Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design. In: Schütt, K., Chmiela, S., von Lilienfeld, O., Tkatchenko, A., Tsuda, K., Müller, KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham. https://doi.org/10.1007/978-3-030-40245-7_17