Finding a Repository with the Help of Machine-Actionable DMPs: Opportunities and Challenges

Abstract

Finding a suitable repository to deposit research data is a difficult task for researchers since the landscape consists of thousands of repositories and automated tool support is limited. Machine-actionable DMPs can improve the situation since they contain relevant context information in a structured and machine-friendly way and therefore enable automated support in repository recommendation.

This work describes the current practice of repository selection and the available support today. We outline the opportunities and challenges of using machine-actionable DMPs to improve repository recommendation. By linking the use case of repository recommendation to the ten principles for machine-actionable DMPs, we show how this vision can be realized. A filterable and searchable repository registry that provides rich metadata for each indexed repository record is a key element in the architecture described. At the example of repository registries we show that by mapping machine-actionable DMP content and data policy elements to their filter criteria and querying their APIs a ranked list of repositories can be suggested.

 [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]