CORD-19: The COVID-19 Open Research Dataset
Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Michael Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey A. Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex D. Wade, Kuansan Wang, Nancy Xin Ru Wang, Christopher Wilhelm, Boya Xie, Douglas M. Raymond, Daniel S. Weld, Oren Etzioni, Sebastian Kohlmeier
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.- Anthology ID:
- 2020.nlpcovid19-acl.1
- Volume:
- Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Karin Verspoor, Kevin Bretonnel Cohen, Mark Dredze, Emilio Ferrara, Jonathan May, Robert Munro, Cecile Paris, Byron Wallace
- Venue:
- NLP-COVID19
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2020.nlpcovid19-acl.1
- DOI:
- Bibkey:
- Cite (ACL):
- Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Michael Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey A. Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, et al.. 2020. CORD-19: The COVID-19 Open Research Dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
- Cite (Informal):
- CORD-19: The COVID-19 Open Research Dataset (Wang et al., NLP-COVID19 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.nlpcovid19-acl.1.pdf
- Code
- allenai/cord19 + additional community code
- Data
- CORD-19, S2ORC, TREC-COVID
Export citation
@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",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Dredze, Mark and
Ferrara, Emilio and
May, Jonathan and
Munro, Robert and
Paris, Cecile and
Wallace, Byron",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings %T CORD-19: The COVID-19 Open Research Dataset %A Wang, Lucy Lu %A Lo, Kyle %A Chandrasekhar, Yoganand %A Reas, Russell %A Yang, Jiangjiang %A Burdick, Doug %A Eide, Darrin %A Funk, Kathryn %A Katsis, Yannis %A Kinney, Rodney Michael %A Li, Yunyao %A Liu, Ziyang %A Merrill, William %A Mooney, Paul %A Murdick, Dewey A. %A Rishi, Devvret %A Sheehan, Jerry %A Shen, Zhihong %A Stilson, Brandon %A Wade, Alex D. %A Wang, Kuansan %A Wang, Nancy Xin Ru %A Wilhelm, Christopher %A Xie, Boya %A Raymond, Douglas M. %A Weld, Daniel S. %A Etzioni, Oren %A Kohlmeier, Sebastian %Y Verspoor, Karin %Y Cohen, Kevin Bretonnel %Y Dredze, Mark %Y Ferrara, Emilio %Y May, Jonathan %Y Munro, Robert %Y Paris, Cecile %Y Wallace, Byron %S Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 %D 2020 %8 July %I Association for Computational Linguistics %C Online %F wang-etal-2020-cord %X 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. %U https://aclanthology.org/2020.nlpcovid19-acl.1
Markdown (Informal)
[CORD-19: The COVID-19 Open Research Dataset](https://aclanthology.org/2020.nlpcovid19-acl.1) (Wang et al., NLP-COVID19 2020)
- CORD-19: The COVID-19 Open Research Dataset (Wang et al., NLP-COVID19 2020)
ACL
- Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Michael Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey A. Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, et al.. 2020. CORD-19: The COVID-19 Open Research Dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.