@inproceedings{wang-etal-2020-cord,
    title = "{CORD-19}: The {COVID-19} Open Research Dataset",
    author = "Wang, Lucy Lu  and
      Lo, Kyle  and
      Chandrasekhar, Yoganand  and
      Reas, Russell  and
      Yang, Jiangjiang  and
      Burdick, Doug  and
      Eide, Darrin  and
      Funk, Kathryn  and
      Katsis, Yannis  and
      Kinney, Rodney Michael  and
      Li, Yunyao  and
      Liu, Ziyang  and
      Merrill, William  and
      Mooney, Paul  and
      Murdick, Dewey A.  and
      Rishi, Devvret  and
      Sheehan, Jerry  and
      Shen, Zhihong  and
      Stilson, Brandon  and
      Wade, Alex D.  and
      Wang, Kuansan  and
      Wang, Nancy Xin Ru  and
      Wilhelm, Christopher  and
      Xie, Boya  and
      Raymond, Douglas M.  and
      Weld, Daniel S.  and
      Etzioni, Oren  and
      Kohlmeier, Sebastian",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.nlpcovid19-acl.1",
    abstract = "The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.",
}