=Paper=
{{Paper
|id=Vol-3705/paper01
|storemode=property
|title=FAIRness in Dataspaces: The Role of Semantics for Data Management
|pdfUrl=https://ceur-ws.org/Vol-3705/paper01.pdf
|volume=Vol-3705
|authors=Marco Hauff,Lina Molinas Comet,Paul Moosmann,Christoph Lange,Ioannis Chrysakis,Johannes Theissen-Lipp,Sebastian Schmid,Daniel Schraudner,Andreas Harth,Sayed Hoseini,Andreas Burgdorf,Alexander Paulus,Tobias Meisen,Christoph Quix,André Pomp,Jonas Steinbach,Gertjan De Mulder,Ben De Meester,Beatriz Esteves,Ruben Verborgh,Tomasz Sołtysiński,Jens Niederhausen,Sascha Eichstädt,Medina Andresel,Veronika Siska,Robert David,Sven Schlarb,Axel Weißenfeld,Alex Acquier,Andy Donald,Edward Curry,Ihsan Ullah,Umair ul Hassan,Lucía Sánchez-González,Ana Iglesias-Molina,Oscar Corcho,María Poveda-Villalón,Benedikt T. Arnold,Johannes Theissen-Lipp,Diego Collarana,Christoph Lange,Sandra Geisler,Edward Curry,Stefan Decker,Inès Akaichi,Wout Slabbinck,Julián Andrés Rojas,Casper Van Gheluwe,Gabriele Bozzi,Pieter Colpaert,Ruben Verborgh,Sabrina Kirrane,Ieben Smessaert,Patrick Hochstenbach,Ben De Meester,Ruben Taelman,Ruben Verborgh,Rohit A. Deshmukh,Diego Collarana,Joshua Gelhaar,Johannes Theissen-Lipp,Christoph Lange,Benedikt T. Arnold,Edward Curry,Stefan Decker,Maximilian Stäbler,Paul Moosmann,Patrick Dittmer,DanDan Wang,Frank Köster,Christoph Lange
|dblpUrl=https://dblp.org/rec/conf/sds3/HauffCM0CT24
}}
==FAIRness in Dataspaces: The Role of Semantics for Data Management==
https://ceur-ws.org/Vol-3705/paper01.pdf
FAIRness in Dataspaces: The Role of Semantics for
Data Management
Marco Hauff1 , Lina Molinas Comet2 , Paul Moosmann2 , Christoph Lange2,3 ,
Ioannis Chrysakis4,5,6 and Johannes Theissen-Lipp2,3
1
Fraunhofer Institute for Integrated Circuits IIS, Nuremberg, Germany
2
Fraunhofer Institute for Applied Information Technology FIT, Aachen, Germany
3
RWTH Aachen University, Aachen, Germany
4
IDLab, Department of Electronics and Information Systems, Ugent, imec, Belgium
5
DTAI, Department of Computer Science, KU Leuven, Belgium
6
Netcompany-Intrasoft, Research and Innovation Development Department, Luxembourg
Abstract
Effective 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 different 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.
Keywords
Dataspaces, Data Spaces, FAIR Data, Semantics, Data Sharing
1. Introduction
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 [1].
Consequently, organizations across all industries are adapting their strategies to incorporate data
into their processes and take advantage of opportunities for optimization and automation [2].
Relevant data may originate from various sources belonging to different actors in the supply
chain or from other market participants, which could also include competitors. Therefore,
The Second International Workshop on Semantics in Dataspaces, co-located with the Extended Semantic Web Conference,
May 26–27, 2024, Hersonissos, Greece
$ marco.hauff@iis.fraunhofer.de (M. Hauff); lina.teresa.molinas.comet@fit.fraunhofer.de (L. M. Comet);
paul.moosmann@fit.fraunhofer.de (P. Moosmann); christoph.lange-bever@fit.fraunhofer.de (C. Lange);
ioannis.chrysakis@ugent.be (I. Chrysakis); theissen-lipp@dbis.rwth-aachen.de (J. Theissen-Lipp)
0009-0006-1619-8762 (M. Hauff); 0000-0001-5446-6947 (L. M. Comet); 0009-0005-2114-8578 (P. Moosmann);
0000-0001-9879-3827 (C. Lange); 0000-0003-2665-4056 (I. Chrysakis); 0000-0002-2639-1949 (J. Theissen-Lipp)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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data-driven value is often created by combining datasets of multiple actors [3]. By facilitating
cross-organizational data sharing, dataspaces are becoming increasingly important for value co-
creation from distributed data [4, 5]. Their potential is also being recognized at a political level,
and governmental funding for dataspace initiatives such as Gaia-X encourages the development
of secure infrastructures for data sharing [6].
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 [7]. This heterogeneity poses a major challenge for dataspaces.
Therefore, various dataspace initiatives agree on the four principles of findability, accessibility,
interoperability, and reuse (FAIR) as common data management practices to enable smooth
integration of multi-source data [8]. However, the FAIR Principles are described at a general level
and allow for various implementation options, complicating standardized data management.
Semantics enrich data with context and thus enable automated search and processing. Using
common, established semantic vocabularies can support FAIRness – i.e., compliance with the
FAIR Data Principles – in dataspaces [8, 9]. Previous literature deals with individual aspects of
FAIR that need to be synthesized to provide a guideline for data management in dataspaces.
Therefore, we aim to answer the following research question: How can semantics contribute to
FAIR compliance in dataspaces?
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.
2. Background
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 [9,
10]. Franklin et al. [11] describe dataspaces as an abstraction for managing data regardless of its
format or structure. Unlike database management systems, dataspaces do not force the complete
integration of data. Instead, they adopt an incremental integration approach and gradually
improve data accessibility and interoperability by leveraging Semantic Web technologies for
metadata management [11, 12]. Recently, dataspaces have been attributed immense potential
for value co-creation from distributed data, providing a decentralized infrastructure for cross-
organizational data sharing while maintaining data sovereignty [4, 13]. Large-scale, government-
funded initiatives such as Gaia-X1 aim to establish a standardized platform that facilitates data
sharing and minimizes the effort required to integrate multi-source datasets. Repetitive and
low-level administrative tasks such as search functionality, naming conventions, data lineage,
and access management should be reduced and streamlined [9, 11]. So far, the dataspaces
initiatives are still in their infancy and standards have yet to be defined.
1
https://gaia-x.eu
FAIR Data Principles. Data governance and management are essential for the reusability of
datasets. In many cases, individual procedures are applied that remain undefined and opaque,
thus complicating or preventing data reuse by third parties. Wilkinson et al. [7] define four
foundational principles, known as the FAIR data principles, to standardize inconsistent data
management and archiving practices in the scientific environment. Within the FAIRification
process, digital resources become manageable. The underlying four principles are interconnected
but can be implemented independently of each other. Besides the actual data, the process also
includes the algorithms, tools, and workflows relevant to data collection. Overall, compliance
with the FAIR Principles reduces human intervention by enabling machines to automatically
discover, process, and integrate data [7, 14]. Efficient data management in line with the FAIR
Principles is considered particularly important for dataspaces to facilitate data sharing [15].
Semantics. A common language for describing data assets is crucial for managing and sharing
data in dataspaces [10]. Semantics are predefined vocabularies to describe relationships between
data entities in a machine-readable format, supporting data integration and enabling automated
data discovery [16]. The variety of terminologies poses a critical challenge for communication
and uniform understanding between different parties [10]. Seamless data integration from
multiple sources requires semantic interoperability, i.e., a shared understanding of the vocabulary.
Ontologies aim to reduce semantic heterogeneity through standardized and generally accepted
vocabularies [17].
3. Methodology
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.
4. Results
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.
4.1. FAIRness Requirements
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 39862 . 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 [8]. In addition to PIDs,
the self-description of resources also includes information about the entities’ attributes and
relationships, often using subject-predicate-object triples to comply with metadata [20, 21].
Therefore, the Resource Description Framework (RDF) data model is commonly used to model
these data structures. Using RDF vocabularies, such as the Data Catalog Vocabulary (DCAT)3 ,
dataspace participants can exchange standardized information, including asset name, type, and
lineage. This results in data structures that are both human and machine actionable. Catalogs and
search tools, such as the RDF query language SPARQL, allow users to access shared information
and browse descriptions to find valuable data for their specific use case [22].
Accessibility. Semantics are crucial for accessibility in FAIR-compliant dataspaces, particu-
larly 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 au-
thenticated 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 offerings, 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, com-
munication, 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 offer a vocabulary hub, providing an overview of commonly es-
tablished 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
2
https://datatracker.ietf.org/doc/html/rfc3986
3
https://www.w3.org/TR/vocab-dcat-3/
Table 1
Requirements for FAIRness in dataspaces
Requirement Objective References
FINDABLE
Self-Description Use unique identifiers and metadata self- 8, 20, 21, 22, 29
descriptions to accurately and unambiguously
characterize participants, services, and assets.
Metadata Use standard vocabularies and ontologies, to 10, 21, 29, 30, 31,
ensure data is described accurately and com- 32, 33
prehensibly.
Catalog Use a publicly available catalog that aggre- 11, 21, 34, 35, 36,
gates participants, services and assets to en- 37, 38, 39
able discoverability.
ACCESSIBLE
Authentication Use a self-description to participate in the 23, 24, 29, 40, 41
dataspace that can be verified by an external
identity provider
Authorization Use policies to manage data sharing among 25, 26, 29, 42, 43,
participants and determine the constitutions 44
and parties involved.
Pipeline Use service descriptions to automate interac- 20, 21, 27, 28, 45
tions and processing pipeline.
INTEROPERABLE
Standardization Use shared ontologies and community stan- 10, 29, 30, 32, 33,
dards to ensure consistent understanding and 37, 46, 47
interoperability.
Integration Use reasoning and aggregation techniques to 10, 36, 48, 49, 50,
incorporate heterogeneous data sources. 51
REUSABLE
Reliability Use verification and validation tools to en- 32, 48, 52, 53, 54.
hance the integrity, quality, and usability of
data assets.
Enrichment Use contextual metadata annotations to en- 26, 29, 31, 36, 45,
hance data assets and facilitate advanced 48, 52
queries and links.
relevant parties, promoting a shared understanding [29]. Therefore, it enables the integration of
data from different 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 reusabil-
ity. 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. Addi-
tionally, 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].
4.2. State of Development
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 different actors or partici-
pants can exchange data. These implementations can then be sector-specific, such as Catena-X4
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-office
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 capa-
bilities. 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 Broker 11 .
In this paper, we solely focus on assessing the leading and most frequently mentioned initia-
tives 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 official documentation of the specifications and the repositories with available
4
https://catena-x.net/en/about-us/operating-environment-1
5
https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/PositionPaper-DataSpace.html
6
https://dataspace.prometheus-x.org/
7
https://www.nfdi.de/fair-data-spaces/?lang=en
8
https://github.com/eclipse-edc
9
https://github.com/eclipse-edc/MinimumViableDataspace
10
https://github.com/sovity
11
https://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.
Table 2
The evaluated FAIRness of current dataspace initiatives
Gaia-X IDS EOSC
Self-Description ✓ ✓ (✓)
Metadata (✓) ✓ ✓
Catalog ✓ ✓ ✓
Authentication ✓ ✓ (✓)
Authorization ✓ ✓ (✓)
Pipeline * * *
Standardization ✓ ✓ ✓
Integration (✓) * *
Reliability ✓ ✓ *
Enrichment * * ✓
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
12
https://www.gxfs.eu/set-of-services/
13
https://gitlab.eclipse.org/eclipse/xfsc/.
14
https://docs.gaia-x.eu/policy-rules-committee/trust-framework/22.10/
15
https://registry.lab.gaia-x.eu/v1/docs
16
https://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 re-
garding 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 speci-
fied in the IDS Reference Architecture Model 4.022 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 man-
agement 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 con-
cepts, 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.
17
docs.gaia-x.eu/technical-committee/architecture-document/latest/component_details/#service-composition
18
https://docs.internationaldataspaces.org/ids-knowledgebase/v/dataspace-protocol/overview/readme
19
https://international-data-spaces-association.github.io/InformationModel/docs/index.html
20
https://github.com/International-Data-Spaces-Association/InformationModel/tree/develop/testing
21
https://github.com/International-Data-Spaces-Association/DataspaceConnector
22
https://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
23
https://github.com/International-Data-Spaces-Association/IDS-G/tree/main/Components/ClearingHouse
24
http://docs.gaia-x.eu/technical-committee/architecture-document/latest/enabling_services/
25
https://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 offers 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 efforts cover aspects like authentication and
authorization, offering 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 main-
tained 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 .
26
https://faircore4eosc.eu/
27
https://faircore4eosc.eu/eosc-core-components
28
https://docs.gaia-x.eu/framework/?tab=software
5. Discussion and Conclusion
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 com-
prehensive 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 frame-
work 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, po-
tentially 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-as-
you-go approach, which introduces variability in the implementation and adherence to the
proposed FAIR Principles, affecting 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 efficient 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 efficiency 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
29
https://greendatai.eu/
30
https://www.flex4res.eu/
for the seamless integration of different data sources. To ensure scalability, the continuous
optimization of graph databases and the use of parallel processing to efficiently 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.
Acknowledgments
This publication is based upon work from COST Action DKG (CA19134), supported by COST
(European Cooperation in Science and Technology). This work has been partially funded by
the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the
Antrieb 4.0 project (Grant No. 13IK015B), by the European Union’s funded Projects MobiSpaces
(Grant agreement no 101070279), Green.DAT.AI (Grant agreement no 101070416), AgriDataValue
(Grant agreement no 101086461) and by the FAIR Data Spaces project of the German Federal
Ministry of Education and Research (BMBF) under the grant number FAIRDS05.
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