FAIRness of micrometeorological data: New community challenge?

"Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use data and metadata" (FAIR Data Maturity Model Working Group, 2020). In the further text term "FAIRness" will be used to describe how data meet FAIR principles, while FAIRNESS (capital letters) is the COST CA20108 action title.

The long-term need for a FAIR micrometeorological data is strongly boosted by two independent but equally important issues: a) full awareness of the time, effort, and money lost due to the lack of FAIR data in general (it is quantified in EC PwC EU Services report (PwC EU Services, 2018): "…the minimum true cost of not having FAIR data … is 78% of the Horizon 2020 budget per year) and b) urgent need to develop tailored adaptation and mitigation measures in rural and urban areas to reduce negative effects of adverse weather and climate change.

Enhanced standardization and integration between data bases&sets of micrometeorological measurements that are part of research projects or local/regional observational networks established for special purposes (agrometeorology, urban microclimate monitoring) by increasing FAIRness of data create a strong background for future research and modeling studies as well as a European Micrometeorological database.

Addressing identified challenges requires an effective transboundary network of researchers, stakeholders (extension services and environmental agencies, local authorities and ministries, SME), and civil society (specialized and general public) from Europe and beyond to identify and fill knowledge gaps, standardize, optimize and promote new environmental-tailored measurement and control procedures, enhance research effectiveness and improve dissemination. Therefore, FAIRNESS Cost action gathers 94 researchers, scholars, students, and stakeholders from 25 countries and 20 different specializations to work together to achieve goals set together.

Literature

FAIR Data Maturity Model Working Group. (2020). FAIR Data Maturity Model. Specification and Guidelines (1.0). https://doi.org/10.15497/rda00050

PwC EU Services. The cost of not having FAIR research data (2018) DOI 10.2777/02999.