# 実験的熱電特性のデータベース化に向けた論文データ収集WebシステムStarry dataの開発

https://mdr.nims.go.jp/datasets/01a3de26-4bdb-4d7c-bfa6-6c1bd6703612

## File

- [粉体粉末冶金協会特集号記事5(著者版).pdf](https://mdr.nims.go.jp/filesets/cb649616-7e71-4a3e-8e25-ba08aa1a57de/download) ([Detail](https://mdr.nims.go.jp/filesets/cb649616-7e71-4a3e-8e25-ba08aa1a57de.md))

## Id

01a3de26-4bdb-4d7c-bfa6-6c1bd6703612

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-09T07:31:54.423111Z

## Updated at

2024-10-10T07:30:55.304483Z

## Published at

2024-10-10T07:30:55.383005Z

## Doi

https://doi.org/10.48505/nims.4832

## First published url

https://doi.org/10.2497/jjspm.64.467

## Date published

2017-08-30

## Recorded date published

2017

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 実験的熱電特性のデータベース化に向けた論文データ収集WebシステムStarry dataの開発
  title_type: original
  lang: ja

## Description

- description: Although numerous papers are published each year, most of the experimental
    data reported in those papers are only available as two-dimensional plot images.
    Data-driven materials science using the machine learning technologies will be
    accelerated by gathering those published experimental data into a database. By
    taking thermoelectric materials as a test case, we attempted to optimize the processes
    of collection of papers, extraction of numeric data from plot images, and sample-based
    data storage into a database. By searching with a keyword “thermoelectric”, we
    obtained a list of 47,936 papers. Among these papers, we selected 18,471 papers
    as possible papers with thermoelectric properties, and succeeded to download 14,835
    full-text PDF files. We developed a web system named “Starry data”, to assist
    the sequential data extraction from the images contained in those PDF files. This
    system also assists materials scientists to annotate experimental samples efficiently,
    to develop a descriptive database that can be used for machine-learning of the
    complex, sample-dependent materials properties.
  description_type: abstract
  lang: und

## Creator

- name: 桂 ゆかり
  role: author
  orcid: https://orcid.org/0000-0002-8905-2995
  organization: National Institute for Materials Science
- name: 熊谷 将也
  role: author
- name: 郡司 咲子
  role: author
- name: 今井 庸二
  role: author
- name: 木村 薫
  role: author

## Contact agent



## Publisher

organization: Japan Society of Powder and Powder Metallurgy

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## Keyword

- subject: materials informatics
  schema: not_defined
- subject: materials database
  schema: not_defined
- subject: data curation
  schema: not_defined
- subject: thermoelectric materials
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

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## Journal

- title: 粉体および粉末冶金
  volume: '64'
  issue: '8'
  start_page: 467
  end_page: 470

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## Related item



## Funding

- identifier: 16K14379
  funder_name: 日本学術振興会
  description: '科学研究費補助金（挑戦的萌芽研究）文献データ収集支援システムの開発による大規模高次元物性データベースの構築 '

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## Fileset

- id: cb649616-7e71-4a3e-8e25-ba08aa1a57de
  filename: 粉体粉末冶金協会特集号記事5(著者版).pdf
  content_type: application/pdf
  size: 704409
  md5: 110e13df976bf45edb6f40964e0235c5

## Thumbnail

fileset_id: cb649616-7e71-4a3e-8e25-ba08aa1a57de
filename: 粉体粉末冶金協会特集号記事5(著者版).pdf