{"id":3680,"date":"2025-05-05T12:38:36","date_gmt":"2025-05-05T10:38:36","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=3680"},"modified":"2025-05-05T12:38:38","modified_gmt":"2025-05-05T10:38:38","slug":"becoming-a-data-space-participant","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/becoming-a-data-space-participant\/","title":{"rendered":"Key Takeaways of a Team-X Implementation Scenario: Becoming a Data Space Participant"},"content":{"rendered":"\n

It\u2019s been three years since the launch of the \u201cTrusted Ecosystem of Applied Medical Data eXchange (TEAM-X)\u201d, the national project within the European GAIA-X<\/a> network. In March 2025, Team-X celebrated the completion of their project at Fraunhofer IIS in Erlangen. Fraunhofer IIS has been one of the eleven consortium partners of Team-X.
Focusing on interoperable communication of data, the Mobile Health Lab<\/a> team provided support in identifying and defining the necessary software component and data modeling. It has also contributed its expertise in the development of telemedical solutions for digital health applications and study management. In this context, a prototypical implementation of study data into the data space was pursued. If you\u2019ve missed the final event of Team-X, here\u2019s a quick guide on how to become a data space participant.<\/p>\n\n\n\n

\"Showing<\/span>
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Researching patient-centered data management<\/strong><\/p>\n\n\n\n

Since 2022, Team-X has been researching on how health data \u2013 some of which is difficult to access \u2013 can be made more easily available to patients, doctors, and healthcare personnel. The aim was to develop a secure and trustworthy digital data ecosystem based on the Gaia-X infrastructure. This will enable the development of data-driven business models, products, and services as the basis for future-oriented healthcare.<\/p>\n\n\n\n

At the core of the project were patients and the issue of maintaining control over their data, allowing them to decide who has access and for what purpose. Two use cases were selected as prototypes for the joint research project: the \u201cMedicine \/ Women\u2019s Health\u201d use case focused on developing a communication tool to improve data management in breast cancer care<\/a>, while the \u201cDigital Care Platform\u201d use case aimed to enhance both inpatient and outpatient care for the elderly.<\/p>\n\n\n\n

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Three steps to becoming a data space participant<\/h2>\n\n\n\n

A significant challenge when working with healthcare data is the vast amounts of data stored in silos with limited access mechanisms. This is where data spaces come in handy, offering an environment where data can be provided, securely exchanged, and accessed.
Addressing this issue in the context of Team-X, the Mobile Health Lab team successfully integrated data from their DPM study management system<\/a> into the project using a data space connector. Linking clinical studies to a data space is increasingly important, especially with the upcoming European Health Data Space (EHDS), which mandates greater data sharing in the future. 
However, to be able to benefit from or contribute to such a data space, individuals, or organizations need to become participants, usually with a system that can receive and send data. Let’s take a closer look at the three steps of joining a data space.<\/p>\n\n\n\n

1. Technical connection<\/strong><\/h2>\n\n\n\n

As with all IT ecosystems, various technical components must be implemented to facilitate data transfers between participants in a data space. The specific components required are dictated by the architecture of the target data space. Originating from Gaia-X and the International Data Spaces Association, a set of open-source tools were created, now maintained under the umbrella of the Eclipse Foundation: the Eclipse Cross Federation Services Components (XFSC)<\/a> and the Eclipse Data Space Components (EDC)<\/a>. In our case, a crucial element is the connector based on the EDC, which streamlines the necessary steps for data exchange, talks to other participants, handles authentication and authorization, manages assets and access policies, as well as controlling and proxying the actual data transfer.<\/p>\n\n\n\n

2. Service description and data format<\/h2>\n\n\n\n

Before another participant can use a service \u2013 or in this case, a data product \u2013 he or she must be aware of its existence. A catalog component of the data space lists all participants as well as consumable services and their attributes in an agreed-upon format. To put it briefly, Gaia-X employs Resource Description Framework (RDF) in the JSON-LD format using its own ontology and Data Catalog Vocabulary (DCAT) to generically describe the various entries within the data space.
To ensure usability by other participants, data to be shared must be presented in a commonly understood format and semantic. The medical domain is special in this regard, because a diverse landscape of standards exists. One of the most important ones is undoubtedly the Fast Healthcare Interoperability Resources (FHIR\u00ae) standard, which is gaining increasing traction in Germany and worldwide. It is used for connecting the DPM to the data space, describing the study as well as encoding the data itself. The existing RDF representation of FHIR resources can be utilized to serialize them as JSON-LD.<\/p>\n\n\n\n

3. Identity & conformity<\/h2>\n\n\n\n

To join in the data space, potential participants must be identifiable by others and must adhere to the common rules and policies established by its members. This requirement is a significant aspect of the Gaia-X initiative, which aims to create a framework that fosters trust among data space participants. Compliance with standards and usage of agreed-upon technologies, as well as proper management and implementation of identity credentials is essential for ensuring a secure and collaborative environment. In short, Gaia-X relies on Verifiable Credentials and Distributed Identifiers (DID) to establish digital identities and uses a network of independent Gaia-X Digital Clearing Houses (GXDCH) to verify compliance with data space rules<\/a> <\/a>and the mentioned service description standards.<\/p>\n\n\n\n

Future relevance for the European Health Data Space (EHDS)<\/h2>\n\n\n\n

As outlined above, the research on enhancing data accessibility and the Team-X project have gained significance following the EU’s regulation on the European Health Data Space (EHDS), which became effective on March 26th<\/sup>. While the definitive operational and technical details of the EHDS remain unclear until the publication of implementing acts in two years\u2019 time, we can already say today that medical data must become more accessible across the EU. Furthermore, availability for research and legislative purposes, the so-called secondary use, must be a big part of it.<\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Image copyright: Fraunhofer IIS<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

It\u2019s been three years since the launch of the \u201cTrusted Ecosystem of Applied Medical Data eXchange (TEAM-X)\u201d, the national project within the European GAIA-X network. In March 2025, Team-X celebrated the completion of their project at Fraunhofer IIS in Erlangen. Fraunhofer IIS has been one of the eleven consortium partners of Team-X.Focusing on interoperable communication of […]<\/p>\n","protected":false},"author":11,"featured_media":3957,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[39,49],"tags":[],"coauthors":[100,75],"class_list":["post-3680","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-health","category-medical-data-space"],"yoast_head":"\nKey Takeaways of a Team-X Implementation Scenario: Becoming a Data Space Participant - SMART SENSING insights<\/title>\n<meta name=\"description\" content=\"Data spaces will become increasingly important in the future of managing health data. 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