> For the complete documentation index, see [llms.txt](https://docs.informationgrid.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.informationgrid.com/informationgrid-data-sharing-services/igds-background-and-concepts.md). # IGDS - Background & Concepts ## **Introduction** The InformationGrid Data-sharing Services (or [IGDS](https://informationgrid.com/data-sharing)) enable organizations to exchange data within trusted dataspaces while maintaining control over how their data is accessed and used. A dataspace connects multiple organizations that want to share data in a secure, governed, and interoperable way. To make this possible, IGDS is built around a set of core concepts that describe how participants join a dataspace, publish data, discover data from others, and exchange data under agreed conditions. These concepts together form the foundation of trusted data sharing and follow principles established by organizations such as the *International Dataspaces Association* (or [*IDSA*](https://internationaldataspaces.org/)) and trust frameworks like [*iSHARE*](https://ishare.eu/). ## **Background: The rise of Dataspaces** Over the past decade, organizations have increasingly recognized that data becomes far more valuable when it can be shared across organizational boundaries. Many societal and business challenges—such as improving mobility, optimizing logistics, managing urban infrastructure, or advancing scientific research—require collaboration between multiple organizations that each hold different pieces of data. Traditionally, sharing data across organizations has been difficult. Data exchanges often relied on bilateral integrations, centralized platforms, or ad-hoc agreements. These approaches tend to create technical complexity, vendor lock-in, and uncertainty about how data is used once it leaves the control of the provider. The concept of *dataspaces* emerged as a response to these challenges. A dataspace is a trusted, federated ecosystem in which organizations can share and access data while maintaining sovereignty over their own data assets. Instead of moving all data into a central platform, each participant keeps control over its own systems and decides under which conditions its data can be used. Several international initiatives have contributed to the development of dataspace principles and standards. Organizations such as the *IDSA* have defined reference architectures and governance models for trusted data sharing. In parallel, trust frameworks like *iSHARE* provide mechanisms for identity verification, authorization, and legal trust between participants. Lastly standards, like the Dataspace Protocol (or [*DSP*](https://eclipse-dataspace-protocol-base.github.io/DataspaceProtocol)), have been adopted by the Eclipse open-source community. Dataspaces therefore combine *technical interoperability, governance frameworks, and trust mechanisms* to enable secure collaboration between organizations. For organizations, participating in a dataspace this represents several important benefits. It allows them to unlock the value of their data while maintaining control over how it is used. They can collaborate with partners, suppliers, customers, and public institutions without relying on centralized data platforms. And dataspaces also reduce the complexity of integrating with multiple parties, because common standards and shared infrastructure simplify the process of discovering and exchanging data. As a result, dataspaces enable new forms of digital collaboration and innovation across entire ecosystems, industries, and regions. IGDS provides the capabilities that allow organizations to participate in these ecosystems and exchange data in a trusted and governed way. ## **IGDS Concepts** **Organizations and Participants** Data sharing always starts with organizations. An organization can be a public authority, a company, a research institute, or any other entity that wants to exchange data with others. Within IGDS, an organization participates in the dataspace through one or more *participants*. A participant represents the legal and technical presence of the organization in the dataspace. It is the participant that interacts with other participants, publishes data, and consumes data. In some cases, an organization may operate through multiple participants. For example, a city might operate one participant for traffic data and another for mobility analytics. This allows organizations to structure their data sharing activities in a flexible way. Participants are therefore the active actors within the dataspace ecosystem. **Identity and Trust** Trusted data sharing requires participants to know who they are exchanging data with. For this reason, identity and trust play a central role in IGDS. Before organizations can exchange data, their identities must be verified. IGDS integrates with trust frameworks such as *iSHARE*, which provide mechanisms for authentication, authorization, and verification. Through these mechanisms, participants can be confident that: * The identity of other participants is verified * Access rights can be enforced * Contracts and policies are applied to trusted parties This trust foundation enables organizations to collaborate with confidence in a distributed data ecosystem. **Connectors and Datastations** Once a participant is part of the dataspace, it needs a technical component that enables data exchange with other participants. This role is fulfilled by a *dataspace connector*. Principally, a dataspace connector must support the dataspace protocol (DSP) as governed by the Eclipse open-source community. The DSP defines the 2 fundamental processes of a dataspace connector: *contract negotiation* and *data transfer*. Within IGDS, connectors are implemented as part of *Datastations*. A Datastation acts as a secure gateway between the internal data systems of an organization and the external dataspace ecosystem. A Datastation connects internal data sources to the dataspace and manages communication with other participants. It ensures that all data exchanges follow the rules defined by contracts and policies. In practice, Datastations are responsible for tasks such as: * Establishing secure connections between participants * Interact with other dataspace connectors using the DSP * Enforcing data usage policies * Validating contracts and access rights * Transferring data between organizations Each participant typically operates one or more Datastations to interact with the dataspace. **Data Products** The main assets exchanged within a dataspace are *data products*. A data product can represent a dataset, data service, or API that a participant makes available to others. Examples of data products include traffic sensor data, public transport schedules, parking availability, environmental measurements, or mobility analytics services. Each data product includes not only the data itself but also descriptive information and usage conditions. This information typically includes metadata, access conditions, and usage policies. By packaging data in this way, providers can clearly define how their data may be used. Data products therefore represent the units of value that organizations share within the dataspace. **Catalogs and Data Discovery** For data sharing to work effectively, participants must be able to discover the data that others provide. This is the role of *catalogs*. Depending on the structure of a dataspace, multiple catalogs can coexist. Each Datastation of a participant maintains a local catalog that contains the data products they publish. This catalog lists the available data products along with their metadata, access conditions, and usage policies. In addition to local catalogs, data products can also be aggregated and presented in central catalogs that can optionally operate as a *marketplace*. A marketplace collects information about data products from multiple participants and presents them in a unified environment where users can browse, search, and compare available data offerings. Marketplaces therefore make it easier for participants to explore the broader data ecosystem and identify relevant data products from different providers. **Contracts and Policies** Before data can be accessed, the conditions for its use must be clearly defined and agreed upon. IGDS supports this through contracts and policies. A *contract* represents the agreement between a data provider and a data consumer. It specifies who may access the data and under which conditions. Contracts are the result of a successful *contract negotiation* process between 2 participants. A *policy* is used to define the restrictions for use of a data product in the form of an *offer*. It can also be used to define how data may be used once access has been granted. For example, policies may restrict data usage to a specific purpose, limit how long data may be stored, or prohibit redistribution to third parties. Together, contracts and policies ensure that data providers retain control over their data. This principle is often referred to as *data sovereignty*. **Data Exchange** Once a contract has been established and the relevant policies are in place, data exchange can take place between participants. Data exchange is based on the data transfer part of the dataspace protocol (DSP). The typical process works as follows. A participant publishes a data product through its Datastation and lists it in its local catalog. Other participants can then discover this data product, either directly through catalog interactions or through a marketplace that aggregates catalog information. If another participant wants to access the data, it submits a request. The provider can then negotiate a contract with the consumer that defines the conditions for access. The Datastations of both participants enforce the agreed policies and enable the secure transfer of data. Through this process, IGDS ensures that data sharing is not only technically possible but also trusted, governed, and compliant with the agreements between participants. **Dataspace Governance** A dataspace is not only a technical infrastructure but also a trusted, federated ecosystem in which multiple organizations agree on common rules for data sharing. These rules are part of the dataspace governance which defines and guards the set of rules, policies, and agreements that enable trusted, sovereign, and secure data sharing between organizations in a decentralized environment. Governance defines how participants interact with each other, how trust is established, and how responsibilities are distributed across the ecosystem. It ensures that all participants operate under transparent and predictable conditions, which is essential for building trust between organizations. Governance typically covers several aspects of the dataspace. * First, it defines *who is allowed to participate* in the dataspace and how participants are verified. This often involves identity and trust mechanisms provided by frameworks such as *iSHARE*. * Second, governance defines *roles and responsibilities* within the ecosystem. For example, some participants may act primarily as data providers, others as data consumers, while some organizations may operate shared infrastructure or governance services. * Third, governance defines *rules for publishing and using data products*. These rules ensure that participants understand the conditions under which data can be accessed and used. They also provide mechanisms for enforcing policies and resolving potential disputes. Another important element of governance is *auditability and transparency*. To ensure that data sharing remains trustworthy, the ecosystem records important events related to data exchange. These audit logs provide a traceable record of actions such as data access requests, contract agreements, and data transfers. This makes it possible to verify that data has been used according to the agreed policies. Through these governance mechanisms, the dataspace ensures that data sharing is not only technically secure but also legally and operationally reliable. **Dataspace Services** In addition to participants and connectors, dataspaces often rely on an infrastructure of *shared services* that support the functioning of the ecosystem. These services provide common capabilities that make it easier for organizations to discover data, establish trust, and collaborate effectively. One important service is the *identity and trust service*, which verifies the identity of participants and ensures that only trusted organizations can join the dataspace. These services often rely on trust frameworks such as *iSHARE* to manage authentication and authorization. Another key service are the *catalog services*, which enable participants to discover available data products. Catalog services may aggregate information from the local catalogs of multiple participants and present them in a unified view. Marketplaces are a common example of such catalog services, allowing users to browse and compare data products offered by different organizations. In addition, *observability services* continuously monitor the state and health of a dataspace to provide transparency and accountability within the ecosystem. These services record key events related to data sharing, such as contract agreements, access requests, and data transfers. They ensure the reliability, compliance, and performance of data sharing between participants by focusing on provenance, trust, and policy enforcement rather than just infrastructure monitoring. Finally, some dataspaces support *shared semantic services*, which help participants use a common understanding of data. These services provide shared vocabularies, ontologies, or data models that describe how data should be interpreted. By using shared definitions and vocabularies, organizations can ensure that the data they exchange is understood consistently across the ecosystem. Together, these services form the supporting infrastructure that enables participants to collaborate efficiently in a dataspace.