The FAIR principles are globally accepted guidelines for improved data management practices with the potential to align data spaces on a global scale. In practice, this is only marginally achieved through the different ways in which organizations interpret and implement these principles. The concept of FAIR Digital Objects provides a way to realize a domain-independent abstraction layer that could solve this problem, but its specifications are currently diverse, contradictory, and restricted to semantic models. In this work, we introduce a rigorously formalized data model with a set of assertions using formal expressions to provide a common baseline for the implementation of FAIR Digital Objects. The model defines how these objects enable machine-actionable decisions based on the principles of abstraction, encapsulation, and entity relationship to fulfill FAIR criteria for the digital resources they represent. We provide implementation examples in the context of two use cases and explain how our model can facilitate the (re)use of data across domains. We also compare how our model assertions are met by FAIR Digital Objects as they have been described in other projects. Finally, we discuss our results' adoption criteria, limitations, and perspectives in the big data context. Overall, our work represents an important milestone for various communities working towards globally aligned data spaces through FAIRification.

翻译：FAIR原则是全球公认的改进数据管理实践的指导方针，具有在全球范围内对齐数据空间的潜力。然而在实践中，由于各组织对这些原则的理解和实施方式存在差异，这一目标仅得到有限实现。FAIR数字对象概念为实现领域无关的抽象层提供了一种可能解决方案，但其现有规范存在多样性、矛盾性且多局限于语义模型。本研究通过形式化表达式构建了一套严格形式化的数据模型及断言集，为FAIR数字对象的实施提供了共同基准。该模型定义了这些对象如何基于抽象、封装和实体关系原则，实现机器可操作的决策，从而满足其所代表数字资源的FAIR准则。我们在两个应用场景中提供了实施案例，并阐释了该模型如何促进跨领域数据的（重复）利用。同时，我们对比了其他项目中描述的FAIR数字对象对本模型断言的满足程度。最后，我们在大数据背景下讨论了本研究成果的采纳标准、局限性及发展前景。总体而言，本研究为各领域通过FAIR化实现全球对齐数据空间的努力提供了重要里程碑。