The new generation of data collected by novel observational platforms is fundamental to enhance the data driven comprehension of the ocean. Therefore, we need a consistent data centric approach to codify design, architecture, deployment, data logging and processing of marine robots.

The paper “A framework for FAIR robotic datasets” outlines a first fundamental step towards FAIR (Findability, Accessibility, Interoperability, and Reusability) interdisciplinary observational science. The proposed method focuses on creating coupled datasets, i.e., including the parameters describing the performance of the robotic platform and the environmental data.

The manuscript weaves into marine robotics sciences the long tradition of data treatment proper to observational oceanography.