# Koshi Motegi — Publications
## Generating Research Data Management Guidelines with Generative AI Using Institutional Documents
Koshi Motegi, Shigeki Matsubara
AXIES 2026 Annual Conference, December 2026 (2026)
## Metadata Generation for Research Data Using Generative AI and Repository Registration: Classification of URL Citations in Scholarly Papers and Use of README Files
Ryoma Okada, Koshi Motegi, Shigeki Matsubara
AXIES 2026 Annual Conference, December 2026 (2026)
## What, Why, and How Research Data Are Cited? Investigating Research Data Citation Practices Across Time and Domains
Koshi Motegi, Koichiro Ito, Shigeki Matsubara
The 28th International Conference on Asia-Pacific Digital Libraries \(ICADL 2026\), December 2026 (2026)
## Where Are Research Data Published? Identifying Their Publication URLs in Scholarly Papers
Ryoma Okada, Koshi Motegi, Koichiro Ito, Shigeki Matsubara
The 28th International Conference on Asia-Pacific Digital Libraries \(ICADL 2026\), December 2026 (2026)
## Cross-Format Citation Classification: Examining the Effects of Combining Bibliographic and URL Citations
Koshi Motegi, Koichiro Ito, Shigeki Matsubara
Proceedings of the 26th ACM/IEEE Joint Conference on Digital Libraries \(JCDL 2026\), pp. 1–6, October 2026 (2026)
[DOI: 10.1145/3805696.3846499](https://doi.org/10.1145/3805696.3846499)
[Source](https://dl.acm.org/doi/10.1145/3805696.3846499)
Citations in scholarly papers serve various functions and refer to diverse resources. Methods have been proposed to classify citation functions and the types of cited resources. In scholarly papers, citations take two formats, i.e., citations using citation tags \(bibliographic citations\) and citations using URLs \(URL citations\). To utilize information about citation functions and the types of cited resources, it is important to be able to classify them across both formats; however, previous studies have classified them independently for each format. This study investigated the effect of combining bibliographic and URL citations on the classification of citation functions and the types of cited resources. We constructed classifiers through transfer learning and multitask learning, and evaluated their performance. The results demonstrated that combining the two citation formats improved classification performance across various encoder models and learning approaches. The improvements were particularly large for cited resource type classification of bibliographic citations.
## Identifying Implicit Research Data References in Paper Citations
Koshi Motegi, Koichiro Ito, Shigeki Matsubara
Proceedings of the 3rd International Workshop on Natural Scientific Language Processing \(NSLP 2026\) co-located with LREC 2026, pp. 186–192, May 2026 (2026)
[DOI: 10.63317/2g9fq97f2h2j](https://doi.org/10.63317/2g9fq97f2h2j)
[Source](https://aclanthology.org/2026.nslp-1.18/)
To encourage the public release of research data under open science, it is beneficial to establish mechanisms for evaluating research data based on metrics such as citation counts. In scholarly papers, authors sometimes cite papers that report the creation or release of research data instead of citing the research data themselves. In this paper, as a step toward computing citation counts of research data, we investigate the feasibility of identifying paper citations that refer to research data. We conducted an identification experiment using large language models and evaluated their performance.
## Where Did the Research Data Originate? Acquiring Provenance of Research Data from Scholarly Papers
Koshi Motegi, Koichiro Ito, Shigeki Matsubara
The 27th International Conference on Asia-Pacific Digital Libraries \(ICADL 2025\) (2025)
[DOI: 10.1007/978-981-95-4861-3\_29](https://doi.org/10.1007/978-981-95-4861-3_29)
[Source](https://link.springer.com/chapter/10.1007/978-981-95-4861-3_29)
Prior knowledge of existing methodologies and resources is essential for a comprehensive understanding of research. In scholarly papers, citations help readers identify prior knowledge. However, research data, such as datasets, lack such mechanisms, making it difficult to trace their origins. In this study, we discuss approaches to acquiring information about the origins of research data, i.e., research data provenance. We focused on scholarly papers as a source for acquiring information about research data provenance, as they often include descriptions of how research data were created. Accordingly, we conducted a preliminary experiment on extracting information about research data provenance from scholarly papers using large language models \(LLMs\). The results demonstrated the feasibility of extracting information about research data provenance from scholarly papers.
## Accelerating Open Access to Research Data: Metadata Generation Using Generative AI and Its Deposition in Institutional Repositories
Yu Watanabe, Koshi Motegi, Shigeki Matsubara
AXIES 2025 Annual Conference (2025)
[DOI: 10.24669/axies.2025.0\_702](https://doi.org/10.24669/axies.2025.0_702)
[Source](https://www.jstage.jst.go.jp/article/axies/2025/0/2025_702/_article/-char/ja)
Best Paper Award, AXIES 2025 Annual Conference