@inproceedings{watanabe-etal-2025-metadata,
    title = "Metadata Generation for Research Data from {URL} Citation Contexts in Scholarly Papers: Task Definition and Dataset Construction",
    author = "Watanabe, Yu  and
      Ito, Koichiro  and
      Matsubara, Shigeki",
    editor = "Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Grezes, Felix  and
      Lockhart, Kelly",
    booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
    month = dec,
    year = "2025",
    address = "Mumbai, India and virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wasp-main.8/",
    pages = "72--79",
    ISBN = "979-8-89176-310-4",
    abstract = "This paper proposes a new research task aimed at automatically generating metadata for research data, such as datasets and code, to accelerate open science. From the perspective of `Findable' in the FAIR data principles, research data is required to be assigned a global unique identifier and described with rich metadata. The proposed task is defined as extracting information about research data (specifically, name, generic mention, and in-text citation) from texts surrounding URLs that serve as identifiers for research data references in scholarly papers. To support this task, we constructed a dataset containing approximately 600 manually annotated citation contexts with URLs of research data from conference papers. To evaluate the task, we conducted a preliminary experiment using the constructed dataset, employing the In-Context Learning method with LLMs as a baseline. The results showed that the performance of LLMs matched that of humans in some cases, demonstrating the feasibility of the task."
}