A guide for making research data more findable, accessible, interoperable, and reusable. It brings together shared expectations so everyone, from researchers to service providers can talk about and assess FAIRness in a common way.

Designed for researchers, data stewards, service providers, and anyone involved in creating, managing, or evaluating research data.

FAIR is interpreted differently in different settings, and current tools does not always agree on how to check if something is FAIR. This model provides clarity and consistency, helping communities set expectations and assess data more meaningfully

• Proposes a basic definition of FAIRness that can apply to any kind of research output. 

• Offers a clear structure to describe how FAIR assessments are done and what the results mean. 

• Helps build shared checklists that reflect what different communities care about. 

• Makes FAIRness easier to measure, compare, and improve. 

• Research Outputs:

Deliverable 1.2: FAIRness Reference Model 

Introduces the first iteration of the OSTrails FAIR Reference Model, defining minimal FAIRness expectations for digital objects and how FAIRness measurements are expressed at metadata and data levels. It presents a schema for expressing FAIRness assessment outputs and benchmarks, establishing the foundation for future refinement and implementation within the OSTrails Implementation Framework.

Deliverable 3.4: Community-based Evaluation Extensions for Compliance Assessment

Defines an Assessment Interoperability Framework (Assessment-IF) to ensure consistent and transparent FAIR assessments of Digital Objects (DOs) across diverse typologies and research disciplines. By collecting requirements from scientific communities and aligning with existing resources such as FAIRsharing, it establishes a generic and extendable approach to harmonise tool behavior.