Search

The Findable, Accessible, Interoperable and Reusable (FAIR) data principles were created to guide the improvement of research data (Wilkinson et al., 2016). As data curators and educators, we often see individual research groups and researchers establish their own unique data collection process, resulting in poor and inconsistent data documentation. At the conclusion of the project, while the data may be accessible and understood by members within the team, it is often not readily usable to anyone outside of those most closely associated with data collection and analysis. The root cause of this is the difficulty to document the pertinent information required to capture the context in which data was captured, processed, and presented. And even when this is attempted it tends to be static and non-machine actionable. As a result, the project data might be FAIR but it is not visible and the cost of re-use is too high as currently few protocols are machine actionable. The availability of context documentation will help other researchers understand and facilitate the re-use the data. Agri-Food Data Canada operates across multiple projects in different fields and run by different institutions. It is a natural environment to recognize the need of decentralized semantic definitions where each research group can influence, modify, or adjust the definition of the data while maintaining integrity of data objects (e.g., schema, data sets, catalogues) across the ecosystem. This practice paper describes the release of the first version of the Semantic Engine leveraging OCA, an architecture to document schemas optimized for decentralized collaboration and reproducibility. OCA leverages new technologies on self-addressing identifiers and enables content-based authority vs. location-based authority. We present here the first results of the Semantic Engine development and the future application.