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        <title>NSF-SciFy: Mining the NSF Awards Database for Scientific Claims</title>
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    <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 an estimated 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.</abstract>
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%0 Conference Proceedings
%T NSF-SciFy: Mining the NSF Awards Database for Scientific Claims
%A Rao, Delip
%A You, Weiqiu
%A Wong, Eric
%A Callison-Burch, Chris
%Y Dong, Yue
%Y Xiao, Wen
%Y Zhang, Haopeng
%Y Zhang, Rui
%Y Ernst, Ori
%Y Wang, Lu
%Y Liu, Fei
%S Proceedings of The 5th New Frontiers in Summarization Workshop
%D 2025
%8 November
%I Association for Computational Linguistics
%C Hybrid
%@ 979-8-89176-337-1
%F rao-etal-2025-nsf
%X 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 an estimated 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.
%U https://aclanthology.org/2025.newsum-main.13/
%P 183-198

Markdown (Informal)

[NSF-SciFy: Mining the NSF Awards Database for Scientific Claims](https://aclanthology.org/2025.newsum-main.13/) (Rao et al., NewSum 2025)

• NSF-SciFy: Mining the NSF Awards Database for Scientific Claims (Rao et al., NewSum 2025)

ACL

• Delip Rao, Weiqiu You, Eric Wong, and Chris Callison-Burch. 2025. NSF-SciFy: Mining the NSF Awards Database for Scientific Claims. In Proceedings of The 5th New Frontiers in Summarization Workshop, pages 183–198, Hybrid. Association for Computational Linguistics.