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The presentation was delivered at the pre-meeting of the RDA Interest Group on Agricultural Data (IGAD) in Berlin on 19/3/2018.
The presentation illustrates the findings of a gap analysis study on weather data standards under the lens of the FAIR principles.
Data standards and vocabularies can be assessed against the FAIR principles: after all, the FAIR principles recommend that “(meta)data use vocabularies that follow FAIR principles”.
The GODAN Action project created a map of data standards relevant for food and agriculture and did a first gap analysis focusing on weather data. GODAN Action is a three year project to enable data users, producers and intermediaries to engage effectively with open data and maximize its potential for impact in the agriculture and nutrition sectors.
The criteria used for the gap analysis were organized in four categories: (a) fitness for purpose, (b) adoption, c) usability and (d) openness. The criteria in the latter two categories can be used for an assessment of the standards against the FAIR principles, as they all try to measure to what extent the standards are findable (is it available on the web? Is it annotated?), accessible (is it maintained? Is it referenceable?), interoperable (is it available in more formats? Is it machine-readable? Is it semantic, referenceable, linked?) and reusable (does it have a clear license?).
However, even more important is the extent to which a data standard or vocabulary contributes to making data that adopts it more FAIR. In some contexts and for some users (data providers, intermediaries) the FAIRness of the standards themselves is very relevant, while for other users (service providers, end users) the FAIRness of the produced data is what matters.
While it’s implied that the use of FAIR vocabularies and data standards contributes to the “I” in FAIR, certain vocabularies help data also with the “F” (e.g. vocabularies that have properties for identifiers, vocabularies that have dataset metadata), “A” (e.g. vocabularies that have properties for procotols) and “R” (e.g. vocabularies that describe licensing and provenance).
The presentation gives some examples of these assessments applied to different types of weather data standards.