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.

This paper proposes a new research task aimed at automatically generating metadata for research data, such as datasets and code, to accelerate open science. From the perspective of ‘Findable’ in the FAIR data principles, research data is required to be assigned a global unique identifier and described with rich metadata. The proposed task is defined as extracting information about research data (specifically, name, generic mention, and in-text citation) from texts surrounding URLs that serve as identifiers for research data references in scholarly papers. To support this task, we constructed a dataset containing approximately 600 manually annotated citation contexts with URLs of research data from conference papers. To evaluate the task, we conducted a preliminary experiment using the constructed dataset, employing the In-Context Learning method with LLMs as a baseline. The results showed that the performance of LLMs matched that of humans in some cases, demonstrating the feasibility of the task.