| title | NSF-SciFy: Mining the NSF Awards Database for Scientific Claims |
|---|---|
| authors | Delip Rao, Weiqiu You, Eric Wong, Chris Callison-Burch |
| date | 2026-05-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2503.08600 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientific claim verification datasets have been limited in size and scope, NSF-SciFy represents a significant advance with 2.8 million claims from 400,000 abstracts spanning all science and mathematics disciplines. We present two focused subsets: NSF-SciFy-MatSci with 114,000 claims from materials science awards, and NSF-SciFy-20K with 135,000 claims across five NSF directorates. Using zero-shot prompting, we develop a scalable approach for joint extraction of scientific claims and investigation proposals. We demonstrate the dataset's utility through three downstream tasks: non-technical abstract generation, claim extraction, and investigation proposal extraction. Fine-tuning language models on our dataset yields substantial improvements, with relative gains often exceeding 100%, particularly for claim and proposal extraction tasks. Our error analysis reveals that extracted claims exhibit high precision but lower recall, suggesting opportunities for further methodological refinement. NSF-SciFy enables new research directions in large-scale claim verification, scientific discovery tracking, and meta-scientific analysis. Code and data are available at https://github.com/darpa-scify/NSFSciFy. |
cite
@misc{rao2026nsf,
title={NSF-SciFy: Mining the NSF Awards Database for Scientific Claims},
author={Delip Rao and Weiqiu You and Eric Wong and Chris Callison-Burch},
year={2026},
eprint={2503.08600},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2503.08600}
}