@inproceedings{doneva-etal-2025-preclinie,
    title = "{P}re{C}lin{IE}: An Annotated Corpus for Information Extraction in Preclinical Studies",
    author = {Doneva, Simona  and
      Hubarava, Hanna  and
      H{\"a}rvelid, Pia  and
      Z{\"u}rrer, Wolfgang  and
      Bugajska, Julia  and
      Hild, Bernard  and
      Br{\"u}schweiler, David  and
      Schneider, Gerold  and
      Ellendorff, Tilia  and
      Ineichen, Benjamin},
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.8/",
    pages = "74--87",
    ISBN = "979-8-89176-275-6",
    abstract = "Animal research, sometimes referred to as preclinical research, plays a vital role in bridging the gap between basic science and clinical applications. However, the rapid increase in publications and the complexity of reported findings make it increasingly difficult for researchers to extract and assess relevant information. While automation through natural language processing (NLP) holds great potential for addressing this challenge, progress is hindered by the absence of high-quality, comprehensive annotated resources specific to preclinical studies. To fill this gap, we introduce PreClinIE, a fully open manually annotated dataset. The corpus consists of abstracts and methods sections from 725 publications, annotated for study rigor indicators (e.g., random allocation) and other study characteristics (e.g., species). We describe the data collection and annotation process, outlining the challenges of working with preclinical literature. By providing this resource, we aim to accelerate the development of NLP tools that enhance literature mining in preclinical research."
}