As data repositories make more data openly available it becomes challenging for researchers to find what they need either from a repository or through web search engines. This study attempts to investigate data users’ requirements and the role that data repositories can play in supporting data discoverability by meeting those requirements. We collected 79 data discovery use cases (or data search scenarios), from which we derived nine functional requirements for data repositories through qualitative analysis. We then applied usability heuristic evaluation and expert review methods to identify best practices that data repositories can implement to meet each functional requirement. We propose the following ten recommendations for data repository operators to consider for improving data discoverability and user’s data search experience: 1. Provide a range of query interfaces to accommodate various data search behaviours. 2. Provide multiple access points to find data. 3. Make it easier for researchers to judge relevance, accessibility and reusability of a data collection from a search summary. 4. Make individual metadata records readable and analysable. 5. Enable sharing and downloading of bibliographic references. 6. Expose data usage statistics. 7. Strive for consistency with other repositories. 8. Identify and aggregate metadata records that describe the same data object. 9. Make metadata records easily indexed and searchable by major web search engines. 10. Follow API search standards and community adopted vocabularies for interoperability.
- Data discovery
- Data repository
- FAIR data
- Requirements and recommendations