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.