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. 

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.

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

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.

• 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.

By lowering technical and conceptual entry barriers, the Commons enables seamless interaction between tools like DMP platforms, SKGs, and FAIR assessment tools. It fosters coherence, reuse, and sustainability across the research landscape.

• Provides standardised specifications aligned with the OSTrails Interoperability Framework and Reference Architecture. 

• Offers a flexible structure for integrating new and existing tools. 

• Aligns with international models such as the RDA Global Open Research Commons (GORC), ensuring global applicability. 

• Includes a roadmap to guide future development and implementation. 

• Research Outputs

Deliverable 2.5: OSTrails Commons Specifications

The document defines the first iteration of the OSTrails Commons, a set of open specifications to integrate DMP platforms, SKGs, FAIR assessment tools, and other services. It specifies the core concepts, features, and structure of the Commons, aligning with European and global standards to ensure scalability and interoperability.

• Other Useful Sources & Documentation

OSTrails Commons Documentation

OSTrails Goverance

The OSTrails Commons is a structured collection of open, reusable specifications designed to facilitate interoperability within the OSTrails ecosystem. Its governance ensures sustainable management, adoption, and evolution. The governance framework incorporates structured leadership, participation policies, and sustainability strategies aligned with global best practices, including the Global Open Research Commons (GORC). 

Milstone 6: FAIR Assessment Commons

Software Release

Training Library

Mentorship Program

This result includes improved versions of key platforms used in Research Data Management (RDM):

• DMP platforms for planning and tracking data practices,
• SKGs for connecting and contextualising research outputs,
• FAIR Assessment tools for evaluating how well data and outputs align with FAIR principles.

Each has been enhanced through OSTrails to better support interoperability, automation, and alignment with EOSC requirements.

Researchers, data stewards, service providers, and institutional managers who use or offer RDM services and need tools that work seamlessly with other systems and policies.

These platforms help reduce duplication, improve data quality, and provide actionable insights across the research lifecycle. By aligning with OSTrails' interoperability frameworks and APIs, they are more connected, more FAIRer, and better suited for integration in both national and EOSC infrastructures.

• DMP platforms support machine-actionable plans that integrate with repositories, PID services, and monitoring systems. 

• SKGs enable linking of research outputs (datasets, software, publications) using structured metadata. 

• FAIR assessment tools apply standardised criteria to evaluate data practices, helping users meet funder and institutional expectations. 

These tools were enhanced in pilot environments, tested for real-world usability, and guided by the PTA Framework. 

• Other Useful Sources & Documentation

The OSTrails reference architecture provides guidance on realising interactions between key components identified in the OSTrails pathways : Data Management Plans (DMPs), Scientific Knowledge Graphs (SKGs), and FAIR Assessment. It clarifies which interactions are standardised within the Interoperability Frameworks and which are relevant to the project without prescribing specific implementation methods.

Architecture: DMP IF

Architecture: SKG IF

Architecture: FAIR IF

This result delivers a curated collection of machine-actionable FAIR tests (maFAIRTests) and complementary toolkits, tailored to the specific needs of different scientific disciplines. It allows users to assess how well their data practices align with the FAIR principles, with meaningful feedback and actionable suggestions.

Researchers, data stewards, and service providers working in domain-specific contexts who want to evaluate and improve the FAIRness of their research outputs. It can also be relevant to funders and infrastructure providers aiming to support community-specific standards and practices.

FAIR assessments is often too generic or focused on repositories rather than data use and reusability. This toolkit enables consistent and transparent evaluation aligned with real-world research practices, helping communities better meet the expectations of EOSC and Open Science.

• Includes reusable, domain-tailored FAIR tests that can be applied to a wide range of digital objects. 

• Offers configuration options to reflect community needs and use cases. 

• Integrates with FAIR assessment tools and platforms within the OSTrails Interoperability Framework (FAIR-IF). 

• Provides user-friendly guidance and recommendations, including discipline-specific data quality indicators and actionable next steps. 

A set of easy-to-use guidelines and tools that help review how well a DMP is written. It checks whether key topics, like how data will be stored, shared, or reused, are clearly addressed.

Designed for researchers writing DMPs, and for funders, institutions, and reviewers who need to check their quality. It is also useful for research support staff helping improve DMPs.

Good DMPs help make research more open, reusable, and trustworthy. But many plans vary in quality. This tool helps ensure DMPs include what is needed, guiding good research practices.

• Offers a clear list of what a good DMP should include
• Adapts the guidance to different types of research and disciplines
• Can be used directly by people, or connected to DMP platforms to give helpful feedback
• Helps track where improvements can be made over time

• Research Outputs

Deliverable 3.1: DMP Evaluation Rubric and Service Specifications

This deliverable defines an evaluation framework for machine-actionable Data Management Plans (maDMPs), aiming to ensure alignment with FAIR principles and support automated, interoperable assessments. 

The SKG Product Quality Toolbox is a collection of reusable resources that help creators and maintainers of SKGs check and improve the quality of the information they publish.

Scientific knowledge graphs are powerful tools to link and explore research information. But if they lack consistency or are difficult to understand, they lose value. This toolbox helps ensure SKGs are reliable, well-structured, and compatible with other systems.

• Offers clear recommendations on what high-quality SKG content looks like
• Includes tools to check for common issues in structure, completeness, and alignment with standards
• Encourages consistent use of names, links, and metadata
• Supports better linking of research data, publications, people, and organisations

• Research Outputs

Deliverable 1.2: FAIRness Reference Model for Digital Objects V1

This deliverable 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. 

Deliverable 3.4: Disciplinary and Thematic Community-based Evaluation Extensions for Compliance Assessment V1

This deliverable defines an Assessment Interoperability Framework (Assessment-IF) to ensure consistent and transparent FAIR assessments of Digital Objects (DOs) across diverse typologies and research disciplines.

The Integrated Competence Centre (ICC) is a collaborative support structure that connects national and research infrastructures with practical expertise, training resources, and tailored guidance to adopt Open Science tools and policies effectively.

It is designed for research-performing organisations, national Open Science stakeholders, infrastructure providers, and policy implementers seeking hands-on support in advancing Open Science.

Implementing Open Science can be complex, especially across different national contexts and disciplines. The ICC helps bridge this gap by offering targeted support, peer learning, and practical tools to ensure OSTrails outcomes are adopted and sustained in the real world.

• Acts as a central coordination point linking national and thematic efforts
• Offers training, helpdesk functions, and community engagement activities
• Supports adoption of OSTrails tools (like FAIR assessment, DMPs, SKGs)
• Helps tailor Open Science practices to institutional and national priorities
• Strengthens long-term sustainability by building local capacity and expertise

• Other Useful Sources & Documentation

Training Library

The Integrated Competence Centre within OSTrails focuses on providing open learning resources for both B2B and B2C training approaches. This includes a network of trainers and a community on Zenodo, where users can explore various training materials and resources. The Centre aims to enhance the quality of research and ensure that researchers are well-equipped with the necessary skills and knowledge to effectively implement and evaluate their research outcomes.

The toolkits are meant for anyone involved in research data management or Open Science, especially those looking to improve their practices around planning, tracking, and assessing research outcomes.

New tools and frameworks are only useful if people can confidently use them. The toolkits provide the clarity, context, and support needed to implement OSTrails solutions in real-world research settings, fostering broader adoption and long-term impact.

• Offers step-by-step guides, templates, and checklists for using OSTrails tools 

• Includes real-world case studies from pilots and best practices from across Europe 

• Provides policy briefs and technical notes for strategic decision-makers 

• Covers FAIR metrics governance and community-driven evaluation methods 

• Supports training, onboarding, and awareness-building efforts 

The Guidance Toolkit for OSTrails is designed to support researchers, research support personnel, and research funding organizations in achieving FAIR practices throughout the research life cycle. It includes a variety of resources such as tutorials, guides, lesson plans, and case studies to enhance the quality of Data Management Plans (DMPs), Scientific Knowledge Graphs (SKGs), and FAIR Assessment. The toolkit aims to empower FAIRness by providing tools and guidance for assessing data and plans in a standardized and effective manner. It also focuses on making assessments more user - friendly and improving the quality of DMPs. The toolkit is co-created and adopted with different communities, testing the system in real-world scenarios, gathering feedback, and providing training.