During the 20th century, the atmospheric science community became one of the major "big data" sources, and micrometeorological measurements are an important part of it. This trend is still going strong, supported by networks of so-called "unconventional" measurements and SMART technologies (citizen weather networks and meteorological instruments on different mobile devices, for example). The potential weakness of data coming from such diverse sources is the lack of findability, accessibility, interoperability, and reusability (FAIR). The opportunity for improvement is "to define a minimal set of related but independent and separable guiding principles and practices, which enable both machines and humans to find access, interoperate and reuse research data and metadata" (PwC EU Services, 2018). Enhanced FAIRness of micrometeorological data strongly supports almost all main pillars of Responsible Research and Innovation (RRI), particularly in STEM and STEM-related fields, leaving room for different levels of accessibility since FAIRness does not imply Open data status. In a case study, an initial FAIRness assessment is performed for micrometeorological data measured in an orchard within the Forecasting and Reporting Service for Plant Protection of the Republic of Serbia (PIS) observational network.