Best Practice Recommendations for Improving FAIR Data Maturity in Wind Energy

The RDA Wind Energy Community Standards Working Group has created these new best practice recommendations for improving FAIR data maturity in wind energy in practice. It addresses key challenges in the sector, which are to foster data exchange and re-use as well as to create more value from data by increasing the efficiency of data cleaning and analytics processes. The Working Group first reviewed and evaluated the current data sharing and publishing landscape in wind energy, finding that there is no widely recognised community ontology at the sector level, and alignment with upper-level ontologies is largely absent. A review of existing open wind energy data led us to the conclusion that the platform itself tends to dictate the FAIRness of a published dataset. It was therefore decided to analyse the potential FAIRness of a range of different platforms (divided into catalogues, repositories, knowledge hubs, organisation websites and dashboards) relevant to the wind energy sector. After shortlisting 15 relevant data sources covering these different types, we evaluated potential FAIRness assessment tools and chose to work with the FAIR Data Maturity
Model due to previous positive experience with it. However, the criteria were adapted slightly for our needs, including reducing the evaluation scale to a Boolean “1/0” choice, rather than using the suggested scale. We also added some specific criteria, including the capability to link to defined wind energy specific terms, to access a preview plot of the data and to access a preview of the file structure. The results showed that the repositories all score similarly, mostly between 74% and 83%, the knowledge hubs also score slightly lower (69-71%), with the company websites (43% and 0%) and the dashboards (34-66%) the lowest. A more detailed analysis of the different criteria allowed us to define recommendations for wind energy data publishing platform developers for wind energy data publishers and for the general wind energy community. Finally, the project allowed us to define some lessons learned, both in terms of community building and interdisciplinary collaboration, as well as in the application of the FAIR Data Maturity Model.