Résumé
The amount of observational data has increased in part due to the more affordable devices and the open satellite data providers. Diverse organisations and working groups have proposed modelling strategies to structure observational data according to their particular purposes. For instance, the ontology I-ADOPT is used to model the detailed description of experiment variables, whereas the ontology SOSA is used to model sensors, instruments and measurements. However, a vast amount of data lies outside the realm of observed data, as is the case with derived, calculated, inferred, predicted, or simulated data. Although both derived outputs and observations share multiple characteristics (e.g., date, value, units, feature of interest, etc.), to our knowledge, there is a lack of a standard model that considers these similarities when describing derived outputs. To fill this gap, this study explores the various representation requirements and introduces SIMONE, a semantic profile based on the semantics of standards such as SOSA and I-ADOPT. We demonstrate the utility of this modelling strategy with a real-life use case concerning the prediction of species coverage for multiple forest areas in Europe.