Data space development

When designing your data space, working step by step ensures progress and enhances the chances of success, which is why we opt for a phased approach. Furthermore, it allows you to gradually increase the commitment of the involved stakeholders. The four phases of our approach are based on the common challenges identified for each stage in the development of our use cases. The four phases we designed are;

  1. Explore: identify the benefit of sharing data for a specific use case and which actors need to be involved
  2. Design: design the agreements, tools, and processes that need to be in place to establish trust needed for data sharing in this use case
  3. Implement: create and implement the agreements, tools, and processes needed to realise data sharing in this use case
  4. Scale: develop trust mechanisms to mitigate data sharing barriers between participants and increase adoption

Note: our approach will be tailored to the needs of the use case depending on the context.

EU reference architecture and implementation guidelines

Reference architectures for interoperable data sharing

Reference architectures serve as frameworks for establishing interoperable data spaces, ensuring data sovereignty, trust, and common governance rules.

Please find available documentation below.

Implementation guidelines

Complementary to the reference architectures, several implementation guidelines have been created to ensure the interoperability of data space building blocks. These implementation guidelines cover several different aspects of data spaces.

These documents provide guidance for the implementation of standardised building blocks, as well as semantic standards.

FAIR Data Point

FAIR Data Point (FDP) is a metadata service that provides access to metadata following the FAIR principles. FDP uses a REST API for creating, storing, and serving FAIR metadata. FDP is software that allows the owners/publishers of digital objects to expose the metadata of their digital objects in a FAIR manner and allows digital objects’ consumers to discover information (metadata) about offered digital objects.

A FAIR Data Point ultimately stores information about data sets, which is the definition of metadata. And just like the webserver in the WWW in the beginning of the 1990s brought the power of publishing text to anyone, a FAIR data point aims to give anyone the power of putting their own data on the web.

Discover the FAIR Data Point Specifications

FAIR Implementation profile

To accelerate broad community convergence on FAIR implementation options, the GO FAIR community launched the development of machine-actionable FAIR Implementation Profiles (FIP). The FIP is a collection of FAIR implementation choices made by a community of practice for each of the FAIR Principles. Community specific FAIR Implementation Profiles are captured as FAIR datasets and are openly available to other communities for reuse.

The FAIR Implementation Profiles, which represent the implementation strategies of various communities, can be used as the basis to optimise the reuse of existing FAIR-enabling resources and interoperation within and between domains. Ready-made and well-tested FAIR Implementation Profiles created by trusted communities can find widespread reuse among other communities, and vastly accelerate convergence onto well-informed FAIR implementations.

Create your FAIR Implementation profile

Interpreting FAIR

The FAIR Guiding Principles provide guidance when improving Findability, Accessibility, Interoperability and Reusability of digital resources. But they do not dictate specific technological implementations. The GO FAIR Foundation believes that whatever FAIR implementation choices are made, they should always ensure interoperability, machine-actionability, global participation, and convergence towards accessible, robust, widespread, and consistent FAIR implementations.

Read more on Interpreting FAIR

Reference Guide for Inter AI Data Space Interoperability (NL AIC)

This guide actively supports organisations with challenges regarding data sharing for AI applications. This guide focuses on the guidelines and building blocks to interconnect data spaces. 

Download the Reference Guide for Inter AI Data Space interoperability

Reference Guide for Intra AI Data Space Interoperability (NL AIC)

This guide actively supports organisations with challenges regarding data sharing for AI applications. In doing so, this guide focuses on the guidelines and building blocks for individual (sectoral, application-specific) AI data spaces.

Download the Reference Guide for Intra AI Data Space interoperability


The IDS-RAM (International Data Spaces Reference Architecture Model) provides a generalised approach for creating a secure “network of trusted data”. Unlike other concrete software solutions, it operates at a higher abstraction level, focusing on concepts, functionality, and overall processes. The model follows a five-layer structure, adhering to common system architecture models and standards (e.g., ISO 42010, 4+1 view model).

Download the IDS-RAM

Gaia-X Labelling criteria

Gaia-X developed a Trust Framework and Labelling Framework that safeguard data protection, transparency, security, portability, and flexibility for the ecosystem as well as sovereignty and European Control.

The Labelling Framework is based on the trust framework (named compliance framework in former documents) based on self-descriptions. Thus, it ensures that all information required to make a qualified choice between different services is available in a consistent and standardised machine-readable form.

Download the Gaia-X labelling criteria

Gaia-X Trust Framework

Gaia-X developed a Trust Framework and Labelling Framework that safeguard data protection, transparency, security, portability, and flexibility for the ecosystem as well as sovereignty and European Control.

The Trust Framework is the set of rules that defines the minimum baseline to become part of the Gaia-X Ecosystem. These rules ensure a common governance and the basic levels of interoperability across individual ecosystems, while giving the users full control over their choices.

The Trust Framework uses verifiable credentials and linked data representation to build a FAIR knowledge graph of verifiable claims from which additional trust and composability indexes can be automatically computed.

Download the Gaia-X Trust Framework

Gaia-X Architecture

This document describes the top-level Gaia-X Architecture model. It focuses on conceptual modelling and key considerations of an operating model and is agnostic regarding technology and vendor. In doing so, it aims to represent the unambiguous understanding of the various Gaia-X stakeholder groups about the fundamental concepts and terms of the Gaia-X Architecture in a consistent form at a certain point in time.

Download the Gaia-X Architecture

Phase 4 – Data Sharing Canvas

The Data Sharing Canvas is a document that provides a foundation for agreements and serves as a stepping-stone to facilitate trust and technical interoperability for cross-domain data sharing at scale. These generic agreements concern the business, legal, operational, functional, and technical conditions under which data can and is allowed to be shared. Examples of topics include roles & responsibilities, governance, security standards, incident management, and functional scope.

Phase 3 – Implement: Use Case Implementation Guide (UCIG)

In the third phase of data space development, all involved stakeholders work on the implementation of the use case. After implementation of the required agreements, you will have a live pilot of your use case. The tool that helps you in this phase is our Use Case Implementation Guide (UCIG). This document is based on the lessons learned so far from our Green Loans use case and provides you with requirements for the functional and technical implementation of your use case, which helps to ensure interoperability and scalability in the future.

Phase 1 – Explore: Use Case Playbook

In the first phase of data space development, we guide you through several steps that enable you to kickstart a data sharing use case. The goal of this phase is to establish a clear scope of the use case and identify its potential impact. This is typically achieved after participating in workshops with the CoE-DSC project team and conducting market research (e.g., interviews, desk research). During this process, we help you to structure and clearly formulate your use case. You can get a glimpse of the process in our Use Case Playbook. After finishing this process, the result is a news article published on the website of the CoE-DSC.

Feel free to contact us once you have your initial idea and the first version of the completed playbook to get help in your use case development journey.

Phase 2 – Design: Use Case Blueprint

In the second phase of data space development, we work with a group of stakeholders representing all actors involved in the use case to create an overview of all relevant topics in a high-level use case design. The goal of this phase is to establish a high-level use case design that covers all elements needed to create the necessary trust to share data in this use case and that is ready for potential future scalability. For example, the high-level design can be used when applying for funding for the implementation – it shows that all relevant topics needed for trust have been considered. By organising workshops with all relevant stakeholders and by applying our Use Case Blueprint, you will produce the design for the use case. In the workshops, the BLOFT framework is used to create an overview of all topics relevant for your use case. BLOFT is an abbreviation for Business, Legal, Operational, Functional, and Technical topics that are relevant to organise trust in data sharing between participants. For example, a topic that must be considered under Business is Fee structures, for Legal it could be Governance, Contracts Applicable Regulation and for Functional it could be required functional support, UX and Privacy features. Download our Blueprint for more examples.

Furthermore, the CoE-DSC can support you to ensure that all contextual criteria are met before moving to the implementation phase. This could for example include support to arrange funding, to involve a sufficient number of stakeholders, or to create awareness of the value of the use case.