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    <abstract>Software repositories have conventionally been used for software development. Recently, they have also served as research data repositories. Research data published in such repositories are frequently accompanied by README files; however, the data frequently lack structured metadata. To address this issue, this paper investigates the feasibility of generating research data metadata from their accompanying README files. First, we analyze the occurrence patterns of metadata-related information in README files. The results of this analysis demonstrated that README files could serve as valuable resources for metadata generation. We then performed an experiment on extracting metadata-related information from README files using large language models (LLMs) and evaluated their performance. The experimental results demonstrated that LLMs could extract metadata-related information with high performance.</abstract>
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%0 Conference Proceedings
%T Generating Research Data Metadata from Their Accompanying README Files
%A Sekido, Kotaro
%A Watanabe, Yu
%A Ito, Koichiro
%A Matsubara, Shigeki
%Y Rehm, Georg
%Y Dietze, Stefan
%Y Dessi, Danilo
%Y Maynard, Diana
%Y Schimmler, Sonja
%S Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F sekido-etal-2026-generating
%X Software repositories have conventionally been used for software development. Recently, they have also served as research data repositories. Research data published in such repositories are frequently accompanied by README files; however, the data frequently lack structured metadata. To address this issue, this paper investigates the feasibility of generating research data metadata from their accompanying README files. First, we analyze the occurrence patterns of metadata-related information in README files. The results of this analysis demonstrated that README files could serve as valuable resources for metadata generation. We then performed an experiment on extracting metadata-related information from README files using large language models (LLMs) and evaluated their performance. The experimental results demonstrated that LLMs could extract metadata-related information with high performance.
%R 10.63317/27oe6uwv2fws
%U https://aclanthology.org/2026.nslp-1.17/
%U https://doi.org/10.63317/27oe6uwv2fws
%P 180-185

Markdown (Informal)

[Generating Research Data Metadata from Their Accompanying README Files](https://aclanthology.org/2026.nslp-1.17/) (Sekido et al., NSLP 2026)

• Generating Research Data Metadata from Their Accompanying README Files (Sekido et al., NSLP 2026)

ACL

• Kotaro Sekido, Yu Watanabe, Koichiro Ito, and Shigeki Matsubara. 2026. Generating Research Data Metadata from Their Accompanying README Files. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 180–185, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).