Efective data governance and management are necessary but challenging prerequisites for creating value from data assets. Findability, accessibility, interoperability, and reusability are guiding principles for data owners in managing and archiving datasets, known as the FAIR Principles. Dataspaces provide an infrastructure for heterogeneous, multi-source data integration and cross-organizational data sharing that would benefit from FAIR compliance. In this paper, we propose semantics as an approach to ensure data FAIRness, enabling machine-aided discovery and reuse of data in diferent formats and structures. We conduct a systematic literature review to translate the overarching principles into ten concrete methods that can be implemented using semantic technologies. In addition, we analyze three mature dataspace initiatives for their adherence to the FAIR Principles and describe their specific implementation. In summary, we argue that semantics provide a common and infrastructure-independent foundation for data management in emerging dataspaces.
Data is a valuable strategic resource for competitiveness, driving innovation and the digital
transformation of organizations. Business value is created by using and reusing data assets [
Handling the increasing volume of unstructured data necessitates proper data governance
and management practices. Typically, decisions on their concrete implementation are left to the
data owner and remain undefined [
The remainder of the paper is structured as follows. First, we detail the concept of dataspaces, semantics, and the FAIR Principles. Second, we describe our methodological approach. Third, we present the semantic approach we identified for each FAIR Principle. Fourth, we assess three mature dataspace initiatives for their FAIR compliance. Finally, we conclude with a discussion of our findings.
Dataspaces. Database management systems are limited to structured data that correspond to
a predefined schema. Consequently, these traditional systems are unsuitable for integrating and
processing the increasing volume of heterogeneous data coming from multiple data sources [
This paper investigates and answers the research question through a systematic literature review. The process is based on the structures proposed by vom Brocke et. al [18] and Webster & Watson [19], which describe the path from general to specific results. Following vom Brocke et. al [18], we begin our research by conceptualizing the topic and delineating the search fields: ‘dataspaces’, ‘FAIR Data Principles’, and ‘semantics’. The purpose of this study is to examine the correlation between semantics and FAIR dataspaces. To achieve this, we consider each of the FAIR Data Principles separately. Thus, we use the search string: "dataspace*" OR "data Space*" AND ("FAIR" OR "find*" OR "access*" OR "interoper*" OR "reuse*") AND "semantic*" . In the second step, we search the following databases for relevant literature: ACM Digital Library, AIS eLibrary, IEEE Xplore, and Scopus. For ACM and Scopus, we limit the search to the title, keywords, and abstract. The search was conducted in February 2024. ACM yielded n=30 articles, AIS n=58, IEEE Xplore n=225, and Scopus n=163 articles. We evaluate the identified articles in an initial review by scanning the title, abstract, and keywords for relevance. Articles that do not address any of our defined search fields are excluded. Duplicates and non-peer-reviewed articles are also removed. After this first phase, we are left with n=78 articles. We then perform an in-depth screening of the abstract, methodology, results, discussion, and conclusion, removing those that do not address synergies between semantics and FAIR dataspaces. After this screening, n=26 articles remain. In addition, we perform a forward and backward search as proposed in [19], resulting in n=21 additional articles. These articles are analyzed in the same cycle as those retrieved directly from the search string, resulting in a total of n=37 articles.
From the literature, we extract the semantic approaches that are applicable in dataspaces. We code and list the ten most frequently mentioned ones, assign them to the underlying FAIR Principle according to their intended use and create from this the requirements profile for dataspaces, as shown in Table 1. Then, based on the analysis of architecture documents and code repositories, we compare the extent to which the semantics in the three currently most popular dataspace initiatives ensure FAIRness according to these criteria.
Findability. To ensure findability in dataspaces, three requirements can be met by semantics:
self-descriptions, metadata, and catalogs. Digital resources require globally unique and persistent
identifiers (PIDs), such as Uniform Resource Identifiers, specified in RFC 3986 2. These identifiers
are widely used in current initiatives, such as Gaia-X, allowing resources to be addressed,
modified, and shared separately, even across multiple dataspaces [
Accessibility. Semantics are crucial for accessibility in FAIR-compliant dataspaces, particularly regarding authentication, authorization, and pipeline. Like assets, dataspace participants receive a unique and descriptive identity, managed and verified by external identity providers. Trust between identities is ensured through cryptographic verification, which allows only authenticated actors to engage within the dataspace [23, 24]. Due to the sensitivity of data, global sharing is not desired. Authorization for access and usage rights is required. Thus, participants manage their oferings, data or services, via policy frameworks to determine the use and scope of data. Standards such as the Open Digital Rights Language (ODRL) facilitate the creation, communication, and execution of contracts in the data space [25, 26]. Leveraging semantics enables machines to understand interfaces and processes, and thus allows for automation [27, 28]. Interoperability. Dataspaces promote the exchange of data between parties. To ensure a seamless process, dataspaces must be interoperable. This requires participants to exchange data to be able to comprehend and integrate them. Semantics can aid in standardization and integration. Usually, dataspaces ofer a vocabulary hub, providing an overview of commonly established and standardized terms and vocabularies as examples and best practices for describing data, services, and contracts [37, 46]. The hub facilitates the exchange of documentation among 2https://datatracker.ietf.org/doc/html/rfc3986 3https://www.w3.org/TR/vocab-dcat-3/ Use unique identifiers and metadata self- 8, 20, 21, 22, 29 descriptions to accurately and unambiguously characterize participants, services, and assets.
Use standard vocabularies and ontologies, to 10, 21, 29, 30, 31, ensure data is described accurately and com- 32, 33 prehensibly.
Use a publicly available catalog that aggre- 11, 21, 34, 35, 36, gates participants, services and assets to en- 37, 38, 39 able discoverability.
Use a self-description to participate in the 23, 24, 29, 40, 41 dataspace that can be verified by an external identity provider Use policies to manage data sharing among 25, 26, 29, 42, 43, participants and determine the constitutions 44 and parties involved.
Use service descriptions to automate interac- 20, 21, 27, 28, 45 tions and processing pipeline.
Use shared ontologies and community standards to ensure consistent understanding and interoperability.
Use reasoning and aggregation techniques to incorporate heterogeneous data sources. relevant parties, promoting a shared understanding [29]. Therefore, it enables the integration of data from diferent sources, such as multiple datasets in a structured format (e.g., RDF), which can be aggregated and queried together. This allows to reveal previously unknown connections between data [50, 51].
Reusability. All of the previously mentioned principles contribute to the concept of reusability. For example, if an entity is easily findable, it is more likely to be reused. Similarly, easily comprehensible entities by following a common standard increase the likelihood of reuse. Additionally, semantics can improve the reliability and enrichment of data. Reliability assessment and assurance can be achieved by using shapes to verify the format of incoming data and automating standardization, as mentioned under interoperability. RDF validation is often performed against shapes defined in the Shapes Constraint Language (SHACL) or Shape Expressions (ShEx), which define the requirements that an RDF graph must meet [ 53, 54]. Enrichment can be applied throughout the data lifecycle, mapping raw data into a structured format and augmenting it with additional information from external sources [45].
Several endeavors aim to define principles and guidelines for the creation and development of dataspaces and to transfer concepts from theory into practice. Today, several well-known initiatives in various application areas have reached distinct stages of development and maturity. Initially, such initiatives define the requirements, principles and specifications to be considered when implementing dataspaces. Subsequently, these specifications can lead to implementations or elements that facilitate the establishment of dataspaces in which diferent actors or participants can exchange data. These implementations can then be sector-specific, such as Catena-X 4 in the automotive sector, in Industry 4.05 with a focus on manufacturing, Prometheus-X6 in the education and skill sector, or with more general developments such as FAIR Data Spaces7 at the interface between research and various application domains.
It is also worth mentioning that additional projects looking to facilitate the implementation of dataspaces exist, such as the Eclipse Dataspace Components (EDC)8, whose adoption in several projects is documented. The EDC is a framework for data sharing in a cross-organizational and sovereign manner supporting specifications from IDS and Gaia-X. One of its main components is the EDC Connector, a well-defined interface exposing the dataspace participants’ back-ofice infrastructure, aiming to bring together their otherwise incompatible and without access/usage control infrastructure. The connector provides functionalities such as discovering, connecting, automated contract negotiation, policy enforcement, and auditing processes. The Minimum Viable Dataspace9 is a sample implementation of the EDC, leveraging it and showing its capabilities. Currently, these two do not support semantics, but one could extend the connector to include such functionality. Other implementations of connectors exist, such as those listed in [55]. The Sovity Connector10, based on the EDC and the Dataspace Connector, extends the functionality of the EDC. For example, it allows the EDC to communicate with the catalog called IDS Metadata Broker11.
In this paper, we solely focus on assessing the leading and most frequently mentioned initiatives in research. The most commonly noted initiatives in research include Gaia-X, International Data Spaces (IDS), and the European Open Science Cloud (EOSC). These initiatives define the architectural frameworks for dataspaces [56]. Our assessment of the initiatives consists of reviewing the oficial documentation of the specifications and the repositories with available 4https://catena-x.net/en/about-us/operating-environment-1 5https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/PositionPaper-DataSpace.html 6https://dataspace.prometheus-x.org/ 7https://www.nfdi.de/fair-data-spaces/?lang=en 8https://github.com/eclipse-edc 9https://github.com/eclipse-edc/MinimumViableDataspace 10https://github.com/sovity 11https://github.com/International-Data-Spaces-Association/metadata-broker-open-core code. In this way, we want to find out whether the written specifications reflect concrete implementations and how these align with the dataspace requirements introduced in Table 1. The results of our assessment are shown in Table 2. There, we summarize our findings as follows: the symbol ✓ indicates that the requirement is fully implemented, (✓) shows that the requirement is only partially supported, i.e., it is only present as a specification but not yet implemented. However, a * indicates that the requirement is not even part of the specification. Gaia-X. Gaia-X aims to establish an ecosystem, whereby data is shared and made available in a trustworthy environment. To achieve this goal, Gaia-X defines several federation services, where a federation implements and federates a dataspace. These services are grouped into four sets, namely Identity & Trust, Federated Catalogue, Sovereign Data Exchange, and Compliance12. One major reference implementation of federation services is provided by the XFSC (Cross Federation Service Components) repositories under the Eclipse Foundation13. The Identity & Trust set of services contains an Authentication/Authorization service, which implements these requirements as defined in Table 1. The Federated Catalogue set consists of the Federated Catalogue as well as tool support for self-descriptions. The implementation of the XFSC Federated Catalogue enables the management of self-descriptions and also allows for the validation against SHACL shapes as a measure towards reliability. The topic of Metadata and Standardization are addressed in the Gaia-X Trust Framework14. In this framework, a Gaia-X ontology is specified, which has to be used to describe all participants and services of a Gaia-X dataspace. Additionally, constraints are specified, which are modelled using SHACL. All ontology and SHACL graphs can be accessed using the Gaia-X Registry15. The Gaia-X specifications also specify the re-use of certain metadata vocabularies such as DCAT, for example, for the data exchange services16. The topic of integration is partly specified by the notion of service 12https://www.gxfs.eu/set-of-services/ 13https://gitlab.eclipse.org/eclipse/xfsc/. 14https://docs.gaia-x.eu/policy-rules-committee/trust-framework/22.10/ 15https://registry.lab.gaia-x.eu/v1/docs 16https://docs.gaia-x.eu/technical-committee/data-exchange/latest/dewg/ compositions17, which allow the aggregation of several services. This way, a service can re-use data that emerged from the application of another service to apply further processing steps. the remaining requirements defined in Table 1, namely pipeline and enrichment, are currently not addressed by Gaia-X specifications or implementations.
IDS. The latest developments of the IDS Dataspace Protocol18 reflect the specifications regarding the Dataspace Information Model, which covers the definitions of the main concepts to be considered in IDS-based dataspaces. The implementation of connectors in an IDS dataspace context must respond with a JSON-LD data object compliant with JSON Schemas and SHACL shapes. Moreover, participants describe themselves and their resources and infrastructure. These self-descriptions can be registered, published, queried, and maintained by the IDS Metadata Broker. The self-descriptions are metadata, and the use of existing semantic web standards is favored. In this sense, the IDS Information Model [23] is modeled as an RDF/OWL ontology and reuses concepts of the DCAT, ODRL, Time, DQV, and other vocabularies19. Moreover, SHACL shapes are available for testing20. There is also an implementation of the Dataspace Connector21, which uses the IDS Messaging Services for the functionalities and message handling, as speciifed in the IDS Reference Architecture Model 4.0 22 and integrates the IDS Information Model. Moreover, the Catalog Protocol specifies how a data consumer requests a catalog from a catalog service. Such a catalog is DCAT and ODRL compliant. In the latest version of IDS RAM 4.0, the Identity Authority role includes specifications about the authorization functionalities and the clearing house23, which serves as an intermediary to provide clearing and settling services for the data exchange transactions in the IDS. Such a component is similar to the Data Exchange Logging Service from Gaia-X24. Additionally, the Dynamic Attribute Provisioning Service25 is part of the Identity Provider to verify the attributes of the participants and connectors in the dataspace.
EOSC. The EOSC initiative addresses the FAIR Principles, with interoperability as a core concept, and aims to create a shared data space for research, science and innovation data management while ensuring the protection of data through EU laws [57]. The specified requirements regarding semantic interoperability are the following: there should be a definition of the concepts, their metadata and data schemas, and they should be publicly available; semantic artefacts should be FAIR, available preferably using open licenses, and have associated documentation, and support maintenance; a metadata model, based on existing standards, should be available to allow discovery over existing federated research data and metadata; there should be building blocks and protocols to facilitate the federation and harvesting of semantic artefacts catalogs. 17docs.gaia-x.eu/technical-committee/architecture-document/latest/component_details/#service-composition 18https://docs.internationaldataspaces.org/ids-knowledgebase/v/dataspace-protocol/overview/readme 19https://international-data-spaces-association.github.io/InformationModel/docs/index.html 20https://github.com/International-Data-Spaces-Association/InformationModel/tree/develop/testing 21https://github.com/International-Data-Spaces-Association/DataspaceConnector 22https://docs.internationaldataspaces.org/ids-knowledgebase/v/ids-ram-4/layers-of-the-reference-architecture-model/ 3-layers-of-the-reference-architecture-model/3-1-business-layer/3_1_1_roles_in_the_ids 23https://github.com/International-Data-Spaces-Association/IDS-G/tree/main/Components/ClearingHouse 24http://docs.gaia-x.eu/technical-committee/architecture-document/latest/enabling_services/ 25https://github.com/International-Data-Spaces-Association/IDS-G The technical layer defines a common security and privacy framework covering authorization and authentication functionalities. The FAIRCORE4EOSC26 project focuses on the development of core components27 for the EOSC namely the Compliance Assessment Toolkit to provide services related to policies and vocabulary services; the EOSC Data Type Registry to register the PID metadata elements including provenance information; the Metadata Schema and Crosswalk Registry to allow register users to create, register and version schemas and crosswalks with PIDs; the EOSC PID Meta Resolver to map items into records; the Research Activity Identifier Service to provide persistent identifiers for research projects; the EOSC Research Discovery Graph Service to allow discovery of EOSC elements from the catalog (resources and communities); the EOSC Research Software APIs and Connectors to guarantee the enduring preservation of research software across various disciplines. Although metadata registration and vocabularies services are mentioned, the concept of self-descriptions is not noted.
Summary. Table 2 shows that two of the initiatives, Gaia-X and IDS, satisfy most of the requirements for semantics, while EOSC has specifications for such requirements, but they are not all implemented. It also shows that these initiatives mostly lack specifications for the more specific functionalities such as pipeline, integration, and enrichment requirements for dataspaces, except EOSC which ofers more advanced features covering research discovery through their metadata. Such findings indicate that the initiatives are currently focused on defining the main elements of dataspaces. These eforts cover aspects like authentication and authorization, ofering a catalog of available resources for transfer, ensuring self-descriptions and metadata to identify these resources, promoting standardization by using existing (W3C) standards, and providing SHACL shapes for validation and reliability.
Another important remark, based on our research of the repositories of the initiatives and connectors, is that some IDS components, such as the IDS Connector are not currently maintained in their original repositories. However, Sovity is supporting the maintenance of the IDS Connector, and extending some of its functionalities. Regarding Gaia-X, some specifications and implementations have not been updated since their initial release, e.g., the implementations of the XFSC components implement the specifications given by the 21.03 version of the Trust Framework, which has since then been replaced by the newer 22.10 version28. 26https://faircore4eosc.eu/ 27https://faircore4eosc.eu/eosc-core-components 28https://docs.gaia-x.eu/framework/?tab=software
In our paper, we derive a framework to assess the semantic FAIRness of dataspaces, which we directly apply to investigate the FAIRness of three mature initiatives. Our framework provides a basis for rating further initiatives and third-party developments that also deal with the development of dataspaces. Research and innovation projects such as MobiSpaces [58], Green.Dat.AI29 or Flex4Res30, which aim to use dataspace technology, have not yet been assessed. The same applies to projects funded by companies or through private programs that already provide the essential elements for setting up dataspaces, e.g., Solid [59].
Theoretical Contribution. This paper synthesizes the existing literature to provide a comprehensive overview of the evolution and current state of semantics in dataspaces. By extending the FAIR Principles, we propose a framework with tangible requirements that provide semantic clarity and interoperability. Accordingly, our paper establishes a foundation for evaluating future dataspace approaches based on the extended FAIR Principles, promoting a structured and objective assessment process.
Practical Implication. Connector implementations can leverage our extended FAIR framework to derive the requirements that a dataspace should fulfill. Vocabularies could be further developed and concretized specifically for the requirements found to cover potential demands. Limitations. The analysis is limited to a specific number of dataspace developments, potentially missing emerging trends and niche innovations. Insight into the development and operational intricacies of these dataspaces is limited, relying on publicly available information and academic publications. Standardization processes within dataspaces often have a pay-asyou-go approach, which introduces variability in the implementation and adherence to the proposed FAIR Principles, afecting the generalizability of our findings.
Conclusion. Our literature review suggests that semantic approaches in theory and practice can guide the FAIRness of dataspaces. Semantic tools focus on standardization, establishing a uniform understanding through shared and standardized vocabularies. They support all FAIR Data principles and contribute to improvements. Utilizing standardized identifiers, shared ontologies, and rich contextual metadata, semantics enhance the individual aspects of the FAIR Principles and create a more interconnected and eficient data ecosystem. Current dataspace initiatives demonstrate similar approaches, with most FAIR Principles already guaranteed by semantics. However, there are additional approaches available, especially in automation such as our identified requirements pipeline, integration, and enrichment. Future directions could see dataspaces evolving to facilitate automatic data exchange and analysis, thereby improving the eficiency of data use. Capable and reliable integration systems can provide a larger data basis that can be further enhanced through enrichment. This includes further research into the development of advanced, dynamic ontologies and the automation of ontology matching 29https://greendatai.eu/ 30https://www.flex4res.eu/ for the seamless integration of diferent data sources. To ensure scalability, the continuous optimization of graph databases and the use of parallel processing to eficiently manage large amounts of data are essential. In addition, developing secure, ethical semantic technologies to protect privacy and promote responsible data use is a necessity if stakeholders are to share their data.
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