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Results

OmicSpace Project Results

OmicSpace has developed a technological, organizational, and collaborative infrastructure to promote biomedical research based on health data through a federated data space. The main project outcomes are structured into seven strategic areas.

1.

OmicSpace Technological Platform

A comprehensive infrastructure for the management, governance, and use of health data

What is it?
The OmicSpace Platform is the technological infrastructure developed to support the management, governance, processing, analysis, and utilization of health data in federated environments. It integrates data cataloguing, interoperability, advanced analytics, and artificial intelligence capabilities within a common architecture designed for biomedical research.
Modern biomedical research requires the management of large volumes of data originating from multiple organizations and information sources. To transform these data into actionable knowledge, capabilities covering the entire data lifecycle are needed, including:
Data integration and ingestion.
Data processing and transformation.
Secure and scalable storage.
Governance, auditing, and regulatory compliance.
Resource cataloguing and discovery.
Advanced analytics and artificial intelligence.
Visualization and exploitation of results.
Traditionally, these capabilities are distributed across different tools and platforms, making their adoption and integrated operation more challenging.
OmicSpace has developed a multi-layer technological architecture that integrates all the capabilities required for the management and exploitation of biomedical data in a coordinated manner. The platform includes:
Data integration and ingestion.
ETL/ELT processing and quality control.
Storage and management of large-scale information.
Data virtualization.
Governance, security, and auditing.
Asset cataloguing and publication.
Data marketplace and analytical capabilities.
Federated model training.
Advanced analytics and artificial intelligence.
Business Intelligence and reporting.
Model operations and monitoring.
Data preparation and analytical exploitation.
The 15-layer architecture developed within OmicSpace covers the entire process, from data acquisition to knowledge generation for health research and innovation.
The OmicSpace Platform provides a common technological foundation for managing and exploiting biomedical data throughout its entire lifecycle. Thanks to this infrastructure, it is possible to:
Manage data from multiple organizations.
Apply consistent governance, security, and auditing mechanisms.
Publish and discover data resources.
Execute advanced analytics and artificial intelligence workflows.
Develop federated scientific collaboration capabilities.
Facilitate data reuse for research and personalized medicine.
Provide an infrastructure ready for future health data spaces.
The platform represents one of the main technological assets generated by OmicSpace and establishes a reusable foundation for future health data initiatives.
OmicSpace has developed a comprehensive technological platform for health data. Its architecture supports the entire data lifecycle, from integration and governance to advanced analytics and artificial intelligence, providing a common infrastructure for biomedical research, federated collaboration, and knowledge generation.

2.

Federated Collaboration Ecosystem

Advancing biomedical research through federated collaboration based on trust, interoperability, and data sovereignty.

What is it?
OmicSpace is a federated ecosystem that connects healthcare organizations, research centers, patient associations, biotechnology companies, and data spaces, enabling collaboration around biomedical data while ensuring that data always remains under the control of the originating organization. The ecosystem combines resource discovery capabilities, distributed analytics, and federated artificial intelligence within a shared collaborative network
Data-driven research faces significant challenges arising from the distribution of information across multiple organizations and systems.
Key challenges include:
Data fragmentation across institutions.
Lack of interoperability between systems and information models.
Regulatory and data protection complexities.
Limited reuse of existing resources.
Difficulties in establishing effective collaborations between public and private organizations.
The need for sustainable and scalable infrastructures capable of maintaining long-term collaboration.
Data spaces emerge as a direct response to these challenges.
OmicSpace provides three core capabilities for federated collaboration:

Federated Catalog

Enables the publication and discovery of resources through standardized metadata, facilitating the identification of available assets without exposing the underlying data.

Federated Analytics

Allows distributed analyses to be carried out across organizations by sharing only aggregated results while keeping data at its source.

Federated Learning Enables artificial intelligence models to be trained across multiple organizations without transferring original data, sharing only model parameters and the knowledge generated during training.
These capabilities are supported by an interoperable infrastructure that facilitates the incorporation of new participants and connectivity with other data spaces.

 

One of the main achievements of OmicSpace has been the deployment of an operational federated network among its founding nodes.

This infrastructure enables:

Sharing data catalogs and resources.
Performing distributed analyses across organizations.
Training AI models through Federated Learning.
Maintaining data sovereignty within each institution.
Incorporating new participants into the ecosystem.
Interoperating with national and European data spaces.
In addition, OmicSpace is already aligned and connected with initiatives such as TARTAGLIA, GVA PharmaTrace Hub, VHITDades, and Image Data Space, expanding collaboration opportunities within the European Health Data ecosystem.
OmicSpace transforms isolated organizations into a collaborative health data network. The project has successfully deployed an operational federated ecosystem that enables catalog sharing, distributed analytics, and AI model training across multiple organizations while preserving privacy, security, and data sovereignty.

3.

Ecosystem Governance and Trust

The foundations that ensure responsible, secure, and sustainable management of health data.

What is it?

OmicSpace has developed a governance framework that establishes the rules, responsibilities, and procedures required to operate a health data space in a secure, transparent, and sustainable manner.

This framework enables multiple organizations to collaborate under common rules while maintaining control over their data and ensuring legal and ethical compliance.

Health data collaboration requires much more than technology. To enable different organizations to participate in a data space, it is necessary to define:
Who can access the data.
Under which conditions data can be used.
The responsibilities of each participant.
How regulatory compliance is ensured.
How ethical and privacy risks are managed.
How decisions are made within the ecosystem.
Without a clear governance framework, collaboration becomes difficult to scale and sustain over time.

OmicSpace has defined a set of documents, rules, and organizational structures governing the operation of the ecosystem.

Key elements include:

Rulebook del espacio de datos.

General principles and data access conditions.
Ecosystem governance model.
Governance Council.
Participant roles and responsibilities.
Data Sharing Agreement.
New member onboarding procedures.
Digital capabilities and control mechanisms.
Standards ensuring security, privacy, and traceability.
The model is based on the principles of data sovereignty, legality, ethics, accountability, transparency, efficiency, and fairness.

The governance framework turns OmicSpace into a trusted and scalable data space.

It enables:

Guaranteed data sovereignty.
Structured onboarding of new participants.
Common rules for collaboration.
Regulatory and ethical compliance.
Enhanced transparency and traceability.
A sustainable foundation for future ecosystem growth.
The governance model is one of OmicSpace’s strategic assets and transforms a technological network into a trust-based collaborative community.
OmicSpace has developed a comprehensive governance framework for the responsible management of health data. This framework provides the rules, processes, and organizational structures required for different organizations to collaborate securely, transparently, and in alignment with legal, ethical, and operational requirements.

4.

Data Space

A federated environment that enables knowledge sharing and value creation from health data while always maintaining control at the source.

What is it?
OmicSpace is a federated data space that enables hospitals, research institutes, technology platforms, and other organizations to collaborate using health data without transferring or centralizing it. Each organization maintains control over its data while participating in research initiatives, collaborative analyses, and advanced artificial intelligence projects

Data required to advance biomedical research is often distributed across multiple organizations. Although combining these datasets can generate significant scientific value, there are major barriers related to privacy, regulation, interoperability, and governance.

Key challenges include:

Protecting highly sensitive health data.
Limitations on information sharing across organizations.
Heterogeneous systems and data models.
Challenges in conducting multicenter studies.
The need to preserve sovereignty over institution-generated data.

OmicSpace combines different technological components to facilitate secure collaboration between organizations.

The catalog and connectors enable the identification of available resources, participant management, and access policy definition. Privacy-Enhancing Technologies (PETs) enable distributed analyses and federated model training without moving original data outside the institution where it is hosted.

As a result:

Data always remains at its source.
Organizations retain full control over their resources.
Analyses can be executed in a distributed manner.
Only aggregated results or model parameters are shared.
Collaboration occurs within a common governance and traceability framework.

OmicSpace has enabled the transition from isolated resources to a collaborative ecosystem capable of generating knowledge in a secure and privacy-preserving manner.

The Data Space provides:

Secure inter-organizational collaboration.
Discovery of distributed resources.
Capabilities for multicenter studies.
Federated AI model training.

Collaboration takes place within a common framework of governance and traceability.

Common governance, traceability, and control mechanisms.
Alignment with European principles for the responsible reuse of health data.
As a result, OmicSpace lays the foundations for accelerating biomedical research, advancing personalized medicine, and enabling new forms of collaboration while preserving privacy, security, and data sovereignty.
OmicSpace transforms distributed data into shared knowledge. Through a federated architecture based on interoperability, governance, and advanced privacy technologies, organizations can collaborate, analyze information, and develop innovative research models without moving data from its source.

5.

Data Catalog

The foundations that ensure responsible, secure, and sustainable management of health data.

What problem does it solve?

Health data is often distributed across hospitals, research institutes, patient associations, biobanks, and other organizations. This fragmentation makes it difficult to identify what data exists, where it is located, and how it can be reused for research and innovation.

OmicSpace creates a common discovery point that enables users to locate data resources without directly accessing sensitive information

.

The catalog brings together resources from hospitals, health research institutes, technology platforms, biobanks, and patient associations participating in OmicSpace. It currently integrates biomedical, clinical, omics, and socio-health assets, including:

Longitudinal clinical datasets covering a range of diseases and care pathways, including breast cancer, acute coronary syndrome, and hidradenitis suppurativa.

Clinical and oncology cohorts containing clinical information, biomarkers, treatments, healthcare utilization, and health outcomes, including lung cancer and sarcomas.

Genomic and pharmacogenomic data focused on the analysis and interpretation of genetic variants and the study of population genetic variability.

Advanced omics data, including transcriptomic and epigenomic datasets derived from biological research samples.

Microbiological information generated through surveillance projects based on microbial genomic sequencing.

Biobank resources, combining information on biological samples and associated clinical data for biomedical research.

Rare disease and highly complex diagnostic datasets, including inborn errors of metabolism.

OMOP-structured synthetic data, designed to support interoperability, methodological validation, and the development of analytical processes.

Health and social care information related to Alzheimer’s disease and other dementias, including care services, non-pharmacological therapies, and patient association statistics.

Metadata harmonized according to European standards, enabling the discovery, comparison, and evaluation of distributed resources without exposing the underlying data.

The catalog enables users to:
Discover datasets without exposing personal or clinical information.
Identify the organization responsible for each asset.
Review access conditions, coverage, provenance, and governance.
Facilitate collaboration while preserving data sovereignty.
Use European standards to ensure interoperability and reuse.
The catalog is one of the key assets generated by OmicSpace and provides a common foundation for discovering distributed biomedical resources. It contributes to:
Increased visibility of available resources.
Enhanced multicenter research.
Greater data reuse.
Improved interoperability.
Alignment with the European Health Data Space (EHDS).

6.

Pharmacogenomics Use Case

How genetics helps personalize treatments and improve patient safety.

What challenge does it address?
Drug response is not the same for every patient. Certain genetic variants can influence both treatment effectiveness and the risk of adverse reactions. The OmicSpace use case focused on studying this relationship using statin therapy as a validation scenario.
The study integrated genomic and clinical information from different participating organizations to evaluate:
The frequency of 83 clinically relevant pharmacogenetic variants.
Statin use according to genetic profile.
Adverse drug reactions.
Associations between genetic variants and adverse effects
The analysis was conducted in a federated environment where data remained within each organization and only aggregated results were shared. Common data models, harmonization processes, and federated analytics technologies based on uTile were used to avoid transferring individual-level information between institutions.

The use case demonstrated the feasibility of integrating distributed genomic and clinical data for collaborative health data analyses.

It also validated reusable components for future personalized medicine, pharmacogenomics, and federated analytics studies based on real-world data maintained under the custody of each participant organization.

7.

Training and Capacity Building

Fostering the competencies needed to build and manage health data spaces.

Need
The implementation of health data spaces requires specialized expertise in interoperability, biomedical data analytics, bioinformatics, artificial intelligence, data governance, security, and organizational management. Technology alone does not guarantee the success of a federated ecosystem if participating organizations lack the knowledge required to use and maintain it effectively.
OmicSpace promoted a multidisciplinary training program targeting different professional profiles. Training areas included:
Data spaces and interoperability.
Bioinformatics and omics data.
Clinical data analysis and standardization.
Artificial intelligence and analytics.
Data governance, quality, and compliance.
Cybersecurity.
Digital health.
Project management and sustainability.
Communication, knowledge transfer, and adoption.
Specialized training in uTile, IBM Cloud Pak for Data, IBM Knowledge Catalog, and watsonx.
The training program was aimed at:
Bioinformatics, data science, and analytics professionals.
Researchers.
Data engineers and data architects.
Governance, quality, security, and compliance officers.
Management, support, and communication staff.
The training initiative has helped create a shared knowledge base across organizations and professional profiles, strengthening the ecosystem’s ability to operate, govern, and exploit health data safely and sustainably. As a result, OmicSpace now has teams prepared to participate in health data initiatives, manage biomedical information assets, publish standardized metadata, and contribute to the long-term sustainability and evolution of the data space beyond the duration of the project

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