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Since the publication of the principles, several workflows have been proposed to support the process of making data FAIR (FAIRification). However, to respect the uniqueness of different communities, both the principles and the available workflows have been deliberately designed to remain agnostic in terms of standards, tools, and implementation choices. Consequently, FAIRification needs to be properly planned, and implementation details must be discussed with stakeholders and aligned with FAIRification objectives. To support this need, this paper describes GO-Plan, a method for identifying and refining FAIRification objectives. Leveraging on best practices from requirements and ontology engineering, the method aims at incrementally elaborating the most obvious aspects of the domain (e.g. the initial set of elements to be collected) into complex and comprehensive objectives. 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