Utilizing Scholarly Papers for Metadata Generation of Research Data

Abstract

To promote open science, publishing and sharing research data is recommended. To enhance the accessibility of research data, metadata about research data should be assigned; however, manually assigning metadata is costly. On the other hand, scholarly papers describe information about research data, i.e., meta-information, which could potentially be utilized for metadata generation. This paper explores the feasibility of obtaining meta-information by utilizing scholarly papers. We implemented two methods to obtain meta-information from text surrounding URLs that cite research data, and evaluate their performance. The first method extracts meta-information from input texts, and the second method classifies input texts. Both methods are performed using a LLM. The experimental results indicate that meta-information extraction using an LLM has low performance. By contrast, in research data classification, we confirm that the performance of our method improves by providing classification examples.