The collection of meteorological and oceanographic (met-ocean) data is essential to advance knowledge of the state of the oceans, leading to better-informed decisions. Despite the technological advances and the increase in data collection in recent years, met-ocean data collection is still not trivial as it requires a high effort and cost. In this context, data resulting from commercial activities increasingly complement existing scientific data collections in the vast ocean. Commercial fishing vessels (herein fishing vessels) are an example of observing platforms for met-ocean data collection, providing valuable additional temporal and spatial coverage, particularly in regions often not covered by scientific platforms. These data could contribute to the Global Ocean Observing System (GOOS) with Essential Ocean Variables (EOV) provided that the accessibility and manageability of the created datasets are guaranteed by adhering to the FAIR principles, and reproducible uncertainty is included in the datasets. Like other industrial activities, fisheries sometimes are reluctant to share their data, thus anonymization techniques, as well as data license and access restrictions could help foster collaboration between them and the oceanographic community. The main aim of this article is to guide, from a practical point of view, how to create highly FAIR datasets from fishing vessel met-ocean observations towards establishing fishing vessels as new met-ocean observing platforms. First, the FAIR principles are presented and comprehensively described, providing context for their later implementation. Then, the lifecycle of three datasets is showcased as case studies to illustrate the steps to be followed. It starts from data acquisition and follows with the quality control, processing and validation of the data, which shows good general performance and therefore further reassures the potential of fishing vessels as met-ocean data collection platforms. The next steps contribute to making the datasets as FAIR as possible, by richly documenting them with standardized and convention-based vocabularies, metadata and format. Subsequently, the datasets are submitted to widely used repositories while a persistent identifier is also assigned. Finally, take-home messages and lessons learned are provided in case they are useful for new dataset creators.
