Yukari Katsura
(National Institute for Materials Science)
;
Tomoya Mato
(Center for Basic Research on Materials, National Institute for Materials Science)
;
Yu Takada
(Center for Basic Research on Materials, National Institute for Materials Science)
;
Eiji Koyama
(Center for Basic Research on Materials, National Institute for Materials Science (NIMS))
;
Dewi Yana
(Center for Basic Research on Materials, National Institute for Materials Science (NIMS))
;
Atsumi Tanaka
(Center for Basic Research on Materials, National Institute for Materials Science (NIMS))
;
Masaya Kumagai
(Sakura Internet Research Center, Sakura Internet Inc.)
Description:
(abstract)We developed two LLM-assisted tools to accelerate data collection from materials science publications for the Starrydata database. The first tool, Starrydata Auto-Suggestion, generates concise English descriptions from abstracts and methods that conform to our database schema, integrated into the Starrydata2 platform using lightweight models. The second tool is a dual-component system: Auto-Summary GPT processes PDF files to generate comprehensive JSON output capturing all figures, tables, and samples, while Auto-Summary Viewer transforms this into interactive tables for efficient curator review. These tools enhance curation efficiency and advance automated scientific database construction.
Rights:
Keyword: Materials informatics, materials database, data curation, literature data mining, automated data extraction, automated knowledge extraction, large language model
Date published: 2025-12-31
Publisher: Informa UK Limited
Journal:
Funding:
Manuscript type: Publisher's version (Version of record)
MDR DOI:
First published URL: https://doi.org/10.1080/27660400.2025.2590811
Related item:
Other identifier(s):
Contact agent:
Updated at: 2026-01-20 09:35:27 +0900
Published on MDR: 2026-01-20 12:23:04 +0900
| Filename | Size | |||
|---|---|---|---|---|
| Filename |
Development of LLM-assisted data curation tools for the Starrydata materials science database.pdf
(Thumbnail)
application/pdf |
Size | 12.5 MB | Detail |