Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users’ wishes. This paper presents the analysis of these 101 use cases collected between 2019 and 2020 to examine data discovery requirements. We categorized the information across 12 topics and eight types of users.

The outcomes of this work point to significant gaps in existing search infrastructure. While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We anticipate that the gaps identified in this work are highly generalizable across existing infrastructures, as well as across domains, and can be used to devise plans to improve those infrastructures and guide the development of a more integrated infrastructure ecosystem, particularly for emerging and nascent systems.

The paper is a major outcome of the GO FAIR Discovery Implementation Network. The Discovery IN is a group of over 30 leading institutions and individuals working on FAIR data discovery. The network aims to improve visibility and discoverability of research data across disciplines, to increase reuse of FAIR data, and to provide open alternatives to closed and proprietary infrastructures for data discovery.

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