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- Regarding the recording of “Research Data” and “Evidence Data”
Understanding the Interrelationships of Studies Using Fine-Grained Information in Papers
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- Nakatoh Tetsuya
- Principal Investigator
- 中村学園大学
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- 廣川 佐千男
- Co-Investigator
- 東京都立産業技術大学院大学
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- 石田 栄美
- Co-Investigator
- 九州大学
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- 鈴木 孝彦
- Co-Investigator
- 九州大学
About This Project
- Japan Grant Number
- JP18K11990 (JGN)
- Funding Program
- Grants-in-Aid for Scientific Research
- Funding Organization
- Japan Society for the Promotion of Science
Kakenhi Information
- Project/Area Number
- 18K11990
- Research Category
- Grant-in-Aid for Scientific Research (C)
- Allocation Type
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- Multi-year Fund
- Review Section / Research Field
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- Basic Section 90020:Library and information science, humanistic and social informatics-related
- Research Institution
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- Nakamura Gakuen College
- Kyushu University
- Project Period (FY)
- 2018-04-01 〜 2024-03-31
- Project Status
- Completed
- Budget Amount*help
- 4,290,000 Yen (Direct Cost: 3,300,000 Yen Indirect Cost: 990,000 Yen)
Research Abstract
We conducted research on evaluation metrics for academic papers. Using machine learning, we demonstrated a relationship between bibliographic information and the number of citations a paper receives. Additionally, we proposed a new metric called "Citation Group Count" and showed its effectiveness. Furthermore, we introduced the Focused Citation Count (FCC) and the Revised Focused Citation Count (RFCC), confirming that they provide higher accuracy in evaluation compared to the traditional Citation Count (CC). We also developed a system to automatically divide papers into sections, define similarities between sections, and automatically identify the citing sections, thereby advancing the development of a system to visualize the relationships between papers in detail.
Details 詳細情報について
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- CRID
- 1040282256979457536
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- Text Lang
- ja
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- Data Source
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- KAKEN
- JaLC
- IRDB