Materials Database Group

Change Storage to Knowledge!

2020.12.01 Update

Materials Database Group progresses: 1) Creation and quality control of materials database applicable in the bigdata generation, 2) Classification and integration of the accumulated data, 3) Interactive data-driven by direct connections between laboratories and database, and 4) Reliable data-sharing through objective analyses and authorization systems. We contribute to materials developments and analyses by construction of database advanced in efficiency, quality, and quantity.

Specialized field and Research target

After the World War Ⅱ, the number of scientific journal is continuously increasing. For example, the number of active journals in 1945 which are accessible with Scopus was 1,000, while those at present are roughly 22,000. The growth rate is 10 times per 50 years except for the abrupt growth just after the WWII. Moreover, it is notable that page of each journal also increases 10 times per 50 years. Namely, the scientific document information has increased by ~100 times in the past 50 years. Imagine that the journals share your bookshelves every month!
The scientific journal is one of the most valuable information sources. We must accumulate and put in order the document information with 100 times efficiency than 50 years ago. In the serious situation that the same or higher efficiency is required for the data accumulation in future, we should reconsider the techniques for database formation.

1) Sustainable creation and quality control of materials database

We progress mechanical database-creation, namely text data mining (TDM). TDM should be performed under quality control of the accumulated data.
We also find high quality datasets in scientific societies and create practically usable databases.

2) Classification and integration of the accumulated data

We research techniques of classification and integration of scattered data in the long history of science. Background of the solo data is summarized as metadata, and careful comparison of the metadata should be performed before data integration.

3) Direct connections between laboratories and materials database

We accumulate hot data obtained at laboratories through online services such as 24-hour data analyses and verifications using our database. For this purpose, we develop advanced database creator (artificial intelligence database, AIDB). Researchers who provide hot data may use related data in our database – It is the open science policy.

4) Reliable data-sharing of laboratory data

We establish an authorization system by using objective evaluation using AI. After machine learning, AI suggests a lot of possible structural models to explain the experimental data in spectroscopy. The models are in ranking in their probability. All the possible models and original spectrum are authorized with DOI (digital object identifier) and stored into a database.

Group Leader

Masashi ISHII

Group Members

  • Yukari KATSURA
  • Sae DIEB
  • Hiroyuki OKA
  • Akira SUZUKI
  • We accomplish our mission with;
    Isao KUWAJIMA, Taichi IKEDA, Koji KIMOTO, Kazutaka MITSUISHI, Fumihiko UESUGI, Taichi ABE, Kazuto HIRATA, Koichi SAKAMOTO and Masato KODAKA

Inquiry about this page

Materials Database Group
1-2-1 Sengen, Tsukuba, Ibaraki,
305-0047 JAPAN
E-Mail: change "=" to "@")