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  • FAIRness Reference Model

    A shared framework for understanding and implementing FAIR

    A simple, flexible model that helps define and evaluate how research output meets the FAIR principles, making it easier to reuse, understand, and trust.

    What is it?

    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.

    Who is it for?

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

    Why it matters?

    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

    How it works? - Key components or features explained simply
    • 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. 
    Related Sources
    • 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. 

  • Interoperability Reference Architecture

    Connecting research tools through a shared blueprint.

    A practical guide to help different tools and services in research work together smoothly using common formats, standards, and connections.

    What is it?

    A framework that helps research tools, like DMPs platforms, SKGs, and FAIR assessment tools, communicate and work together. It defines how these systems connect, what kind of information they exchange, and where standardisation helps ensure they speak the same language.

    Who is it for?

    Developers, service providers, research infrastructure managers, and policy makers aiming to improve how research platforms interact and exchange data — within institutions and across borders.

    Why it matters?

    Research tools often operate in silos. This makes it hard to reuse data, track results, or automate workflows. The Reference Architecture removes these barriers by offering a clear, standards-based approach to integration. It ensures flexibility, avoids vendor lock-in, and helps institutions future-proof their systems.

    How it works? - Key components of features explained simply

    The architecture builds on the OSTrails PTA (Plan-Track-Assess) Pathways and, at its core, introduces three distinct yet complementary interoperability frameworks (IFs): DMP-IF, SKG-IF, and FAIR-IF. Each framework has been developed using established community standards, while remaining adaptable to the evolving needs of researchers, institutions, and service providers.

    • DMP-IF: Supports dynamic, machine-actionable (ma) DMPs by enabling real-time updates via shared APIs. For example, when a dataset is published, the system can automatically update the relevant DMP. It builds on the RDA DMP Common Standard, enriched with an application profile tailored to funder and community needs.
    • SKG-IF: Facilitates consistent, structured metadata exchange through Scientific Knowledge Graphs. It improves and extends the current RDA SKG-IF Core Data Model with scientific domain-specific instruments and software services using a new mechanism. A dedicated API supports rich querying, semantic filtering, and relationship-based discovery of research outputs.
    • FAIR-IF: Aligns and makes transparent FAIR assessments by standardising how test results are described and shared. Built on DCAT and DQV standards, it introduces a common output model and API structure, enabling tools to compare results and integrate assessment data into other workflows.

    Related sources
    • Research Outputs

    Deliverable 1.4: OSTrails Interoperability Reference Architecture V1

    Introduces the OSTrails reference architecture and three Interoperability Frameworks for DMPs, SKGs, and FAIR Assessment. It outlines interactions between components, clarifying standardised methods while allowing flexible implementation.

    Deliverable 1.5: OSTrails Interoperability Reference Architecture V2

    Coming Soon

    • Other Useful Sources & Documentation

    RDA DMP Common Standart

    It is a metadata application profile to provide basic interoperability between systems producing or consuming machine-actionable data management plans (maDMPS).

    RDA SKG-IF Core Data Model

    Introduces the Scientific/Scholarly Knowledge Graph Interoperability Framework (SKG-IF). It outlines its motivation, relation to key elements, and applications in OSTrails.

  • OSTrails Commons

    Shared resources to achieve interoperability and federation within the EOSC ecosystem.

    A collection of open, reusable resources that enable platforms and services to align with OSTrails' Interoperability Frameworks and Reference Architecture, fostering integration and collaboration across research infrastructures.