ARIM-mdx Data System: Towards a Nationwide Data Platform for Materials Science
In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. However, existing integrated systems that handle data from these resources have primarily focused just on smaller-scale cross-institutional or single-domain operations. As a result, they often lack the scalability, efficiency, agility, and interdisciplinarity, needed for handling substantial volumes of data from various researchersIn this paper, we introduce ARIM-mdx data system <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, aiming at a nationwide data platform for materials science in Japan. Currently in its trial phase, the platform has been involving 11 universities and institutes all over Japan, and it is utilized by over 800 researchers from around 140 organizations in academia and industry, being intended to gradually expand its reach. The ARIM-mdx data system, as a pioneering nationwide data platform, has the potential to contribute to the creation of new research communities and accelerate innovations.
Authors
- Kenjiro Taura (ORCID: https://orcid.org/0000-0001-5224-382X)
- Toyotaro Suzumura (ORCID: https://orcid.org/0000-0001-6412-8386)
- Shinichiro Kuroki (ORCID: https://orcid.org/0000-0003-1433-5744)
- Mitsuaki Kawamura (ORCID: https://orcid.org/0000-0003-3261-1968)
- Masato Ohnishi (ORCID: https://orcid.org/0000-0002-4889-4061)
- Yoshio Mita (ORCID: https://orcid.org/0000-0003-3655-1245)
- Atsuo Yamada (ORCID: https://orcid.org/0000-0003-4314-7777)
- Hiroki Sakamoto (ORCID: https://orcid.org/0000-0003-1689-956X)
- Norio Takenaka (ORCID: https://orcid.org/0000-0001-7813-4736)
- Ryou Ishikawa (ORCID: https://orcid.org/0000-0001-5625-3259)
- Masatoshi Hanai
- N. Shibata
- Keizo NAKAMURA
- Satoshi Fujikawa
- Yuichi Ikuhara
- Yukinori Ochiai
- Tetsuo Okane
- Daiju Matsumura
Institutions
- Hiroshima University (JP)
- Japan Atomic Energy Agency (JP)
- Tokyo University of Information Sciences (JP)
- The Institute of Statistical Mathematics (JP)
- Research Organization of Information and Systems (JP)
- The University of Tokyo (JP)
Publication Details
- Published
- 2024-12-15
- DOI
- https://doi.org/10.1109/bigdata62323.2024.10825674
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- Research and Development