Knowledge Base

International Data Spaces Association (IDSA)

The IDSA releases a monthly Data Connector Report provides a comprehensive overview of the latest developments in the world of data connectors. This page always latest data connector report. This report also indicates which Data Connectors have received the IDS Certification.

International Data Spaces Association (IDSA)

Recently, the IDSA has started verifying Data Space Connectors. This page shows those connectors as well as the certification scheme and criteria.

International Data Spaces Association (IDSA)

The IDS-RAM (International Data Spaces Reference Architecture Model) provides a generalised approach for creating a secure network of trusted data exchange in which allows for data sovereignty. 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).

Gaia-X

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.

Gaia-X

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.

Gaia-X

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.

GO FAIR Foundation

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.

GO FAIR Foundation

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.

GO FAIR Foundation

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.

Netherlands AI Coalition (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. 

Netherlands AI Coalition (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.

CoE-DSC

The CoE-DSC Reference Implementation Report explores the functionality and application of Data Spaces to address diverse data sharing challenges, emphasizing use cases like Self-Sovereign Identities (SSI), high-volume low-latency streaming, and semantic interoperability. It highlights Data Spaces as decentralized alternatives, offering enhanced control and scalability compared to traditional methods. The report provides detailed insights into technical and organizational requirements for developing Data Spaces, integrating specific use cases into a generic architecture to demonstrate their flexibility and scalability for modern data sharing needs.