Leveraging value from data, and particularly in the case of research data, is strongly connected to them following the FAIR principles, i.e. being “Findable, Accessible, Interoperable and Reusable”.
Findability is the basis of all subsequent steps and of value in itself. To achieve it, we need infrastructures that assist their users in discovering relevant data independently from underlying factors like their physical location, the used storage and exposure mechanism, the formats and metadata structures used, etc.
This poses a significant and demanding challenge in the pursuit of enabling cross-disciplinary data-driven research at a global scale.
To enable FAIRness, there is a need for both advanced data discovery services that allow the seamless aggregation of remote research data sources and provide intuitive linking mechanisms between data collections as well as between data and other research outputs.
Furthermore, it is essential to establish and apply strong protocols for describing, publishing, licensing, referencing research data in order to promote their discoverability and facilitate their connections to relevant research initiatives.
Based on proven methodologies on a wide range of case studies, we provide guidance on important decisions that affect research data discoverability, including:
- how to cite Datasets and link to Publications
- how to license Research Data
- what metadata should be collected
- which standards to use
- how to integrate or exploit existing Research Information Management (RIM) and repository systems
and can help you develop and implement strategies for these.
Moreover, we offer technical solutions for:
- building common API’s for data query
- using schema.org to improve dataset description and discoverability by search engines
- tackling granularity and domain-specific / cross-domain issues