Tools

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;

Explore: identify the benefit of sharing data for a specific use case and which actors need to be involved
Design: design the agreements, tools, and processes that need to be in place to establish trust needed for data sharing in this use case
Implement: create and implement the agreements, tools, and processes needed to realise data sharing in this use case
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.

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 hellip

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 hellip

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 hellip

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 hellip

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.

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 hellip

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 hellip

Gaia-X Labelling criteria

IDS-RAM

The IDS RAM International Data Spaces Reference Architecture Model provides a generalised approach for creating a secure 8220 network of trusted data 8221 Unlike other concrete software solutions it operates at a higher hellip

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.

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 hellip

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 hellip

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 hellip

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 hellip

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 hellip

Tools

Data space development

Phase 1 – Explore: Use Case Playbook

Phase 2 – Design: Use Case Blueprint

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

Phase 4 – Data Sharing Canvas

EU reference architecture and implementation guidelines

Gaia-X Architecture

Gaia-X Trust Framework

Gaia-X Labelling criteria

IDS-RAM

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

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

Interpreting FAIR

FAIR Implementation profile

FAIR Data Point

FAIR Data Point

FAIR Implementation profile

Interpreting FAIR

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

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

IDS-RAM

Gaia-X Labelling criteria

Gaia-X Trust Framework

Gaia-X Architecture

Phase 4 – Data Sharing Canvas

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

Phase 1 – Explore: Use Case Playbook

Phase 2 – Design: Use Case Blueprint

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