Identifying Implicit Research Data References in Paper Citations

Koshi Motegi, Koichiro Ito, Shigeki Matsubara


Abstract
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.
Anthology ID:
2026.nslp-1.18
Volume:
Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
Month:
May
Year:
2026
Address:
Palma, Mallorca (Spain)
Editors:
Georg Rehm, Stefan Dietze, Danilo Dessi, Diana Maynard, Sonja Schimmler
Venues:
NSLP | WS
SIG:
Publisher:
ELRA Language Resources Association (ELRA)
Note:
Pages:
186–192
Language:
External URL:
https://lrec.elra.info/lrec2026-ws-nslp-18
DOI:
10.63317/2g9fq97f2h2j
Bibkey:
Cite (ACL):
Koshi Motegi, Koichiro Ito, and Shigeki Matsubara. 2026. Identifying Implicit Research Data References in Paper Citations. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 186–192, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).
Cite (Informal):
Identifying Implicit Research Data References in Paper Citations (Motegi et al., NSLP 2026)
Copy Citation: