task_categories:summarization size_categories:100K<n<1M language:en region:us
---
task_categories:
- summarization
language:
- en
size_categories:
- 100K<n<1M
---

# CORD 19

## Dataset Description

- **Homepage:** https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge

### Dataset Summary

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 1,000,000 scholarly articles, including over 400,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. 
This is a processed version of the dataset, where we removed some empty entries and formated it to be compatible with the alpaca training. For more details on the data, please refer to the original publicatio. 

### Citation Information

```
@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://www.aclweb.org/anthology/2020.nlpcovid19-acl.1"
}
```