Ecological data is highly diverse due to the complex nature of the systems they describe. Proper documentation and management are often lacking or not designed for data reuse by others, making the data difficult to find, understand, and at risk to be lost. Adopting the FAIR (Findable, Accessible, Interoperable, Reusable) principles into data practices is a way to mitigate these problems. However, the FAIR principles are abstract and not easily understood by domain scientists. Despite a growing body of assessment tools and resources about FAIR, applying it in practice remains challenging as clear implementation guidelines are missing. We aim to fill this gap by translating the FAIR principles into four data components (metadata, storage, standard and structure) that can be successively worked on to enhance the FAIRness and structure of data and provide a general workflow together with a hands-on guide to give practical suggestions on how to improve the reusability of ecological data. For every workflow step, we introduce the rationale behind it and point towards implementation solutions tailored to ecology. Additionally, we introduce an evaluation tool that facilitates the entry to this workflow by guiding to only those steps that are necessary for the evaluated dataset. With the workflow, guide and tool introduced here, we lower the threshold for ecologists to start making ecological data FAIR, which will ensure long-term reusability of valuable data sources. • Translating FAIR into tangible data components makes FAIR workable in practice. • Providing a hands-on guide, workflow and tool to more easily implement FAIR. • Workflow prioritises the most important actions to increase the FAIRness of data. • Guide gives detailed explanation of steps and practical implementation examples. • Tool provides entry point to workflow for components of data that are not FAIR yet.
Jantzen et al. (Sun,) studied this question.