Data Management and FAIRification underpin every scientific activity within ELIXIR CZ. As the volume, heterogeneity, and complexity of life-science datasets grow, the need for robust practices ensuring that data are Findable, Accessible, Interoperable, and Reusable has become mission-critical. Modern data stewardship is not limited to documentation and archiving—it now encompasses automated workflows, semantic interoperability, machine-actionable metadata, and integration with AI-ready data pipelines.
ELIXIR CZ plays a central role in advancing these principles especially within the Czech research ecosystem. With established leadership in the development of the Data Stewardship Wizard (DSW), contributions to FAIR Implementation Profiles (FIP), the ELIXIR-wide Life Science AAI, and conceptual modelling innovations, ELIXIR CZ is one of Europe’s recognised leaders in data stewardship.
The strategic aim for 2026–2030 is to modernise and expand Data Management beyond traditional datasets, extending stewardship to research software, workflows, AI models, and complex multi-omics resources. As Europe moves toward federated data ecosystems (EOSC, EHDS, GDI), ELIXIR CZ will provide the Czech scientific community with reliable, interoperable, and automated solutions for data handling throughout the research lifecycle.
Current Situation and Identified Strengths
ELIXIR CZ has established itself as a European leader in Data Management and FAIR data stewardship, with contributions that reach far beyond the Czech research community. Over the past decade, the ELIXIR CZ has become a core contributor to FAIRification methods, semantic interoperability standards, and machine-actionable data management planning—areas that are now central to European science policy, EOSC development, and ELIXIR-wide service provision.
The flagship service, the Data Stewardship Wizard (DSW), is one of the most widely adopted tools for creating machine-actionable Data Management Plans (maDMPs). DSW is used in major European consortia, national infrastructures, and institutional data offices, and has become a reference implementation for FAIR Implementation Profiles (FIPs), automated FAIR assessment, and domain-specific metadata guidance. Through its flexible knowledge model and automation capabilities, DSW enables researchers to produce consistent, high-quality metadata aligned with funder requirements, ELIXIR practices, and emerging EOSC standards.
ELIXIR CZ also plays a leading role in semantic interoperability—an area often overlooked but crucial for the full implementation of FAIR principles. ELIXIR CZ contributes to the conceptual modelling expertise, ontology development, and semantic harmonisation tools that support a wide range of scientific communities. Contributions to the EDAM ontology, metadata schemas, and semantic curation pipelines ensure that Czech tools and services integrate smoothly with ELIXIR Platforms and European initiatives. ELIXIR CZ is known for its methodological strength in conceptual modelling, which has influenced several ELIXIR communities and Research Data Alliance (RDA) working groups.
At the infrastructure level, ELIXIR CZ benefits from a mature ecosystem supported by e-INFRA CZ. DSW Cloud, Life Science AAI, secure data stores, registries, and workflow engines are all hosted on stable national infrastructure, ensuring reliability, scalability, and continuity of service for thousands of users. This technical robustness, combined with strong software engineering capabilities, enables Czech teams to deliver production-grade services with high availability.
Furthermore, ELIXIR CZ is deeply integrated into European and global initiatives including EOSC, Research Data Alliance (RDA), GO FAIR, CODATA, and community-led standardisation efforts. Czech experts are involved in shaping guidelines for maDMPs, FAIR evaluation metrics, metadata interoperability, and cross-platform data integration.
An increasingly important strength is ability of ELIXIR CZ to prepare AI-ready datasets. The close interaction between Data Management, FAIRification, and the AI/ML domain enables ELIXIR CZ to provide machine-readable, semantically enriched, well-curated datasets suitable for training and inference—something very few European nodes can currently offer.
Finally, ELIXIR CZ has created a strong national network of data stewards, research support staff, and institutional FAIR leaders who actively promote good data practices in universities, research institutes, and hospitals. This bottom-up community complements the top-down infrastructure and has become a major asset in implementing FAIR culture in the Czech Republic.
Together, these strengths position ELIXIR CZ as one of Europe’s most advanced and influential nodes in Data Management and FAIRification, capable of driving innovation and supporting the transition to a fully FAIR and AI-ready research ecosystem.
Challenges and New Directions
As scientific data continue to grow in scale, diversity, and regulatory complexity, the Data Management domain faces a new generation of challenges that require more than incremental improvement—they demand a fundamental shift toward automation, semantic precision, and cross-domain integration.
- A key challenge is the need to evolve from human-centric stewardship to automated, machine-actionable FAIRification workflows. Many Czech researchers still rely on manually created metadata, fragmented documentation practices, inconsistent data structures and manual or semi-manual steps in their workflows. This limits interoperability, reproducibility, and the ability to prepare AI/ML-ready datasets. The next strategic period must therefore focus on automating metadata extraction, enforcing semantic standards, and embedding FAIR principles directly into daily research workflows through DSW and related tools.
- A key challenge lies in the fact that FAIRification must broaden beyond the stewardship of datasets. Modern research requires stewardship of software, workflows, computational environments, AI/ML models, provenance records, and full research objects. This aligns with international initiatives promoting Research Object Management Plans (ROMPs), workflow provenance standards, and FAIR principles for software. Supporting this expansion requires new tools, knowledge models, and best practices, and places ELIXIR CZ in a leadership role as one of the few nodes capable of delivering a comprehensive approach.
- Another challenge is connected with Semantic interoperability which remains one of the most technically demanding challenges. Harmonising ontologies, aligning domain-specific metadata schemas, and building conceptual models that span genomics, structural data, chemical data, imaging, and clinical contexts requires sustained expertise. With the expansion of multi-omics and multimodal research, the need for precise semantics is greater than ever—both to support FAIR data and to enable integrative workflows and AI inference. ELIXIR CZ is uniquely positioned to lead in this area but must continue investing in ontology development, semantic tooling, and community coordination.
- A particularly important emerging challenge is preparing datasets to be AI-ready. AI methods require clean, well-structured, richly annotated, machine-actionable datasets—conditions that are still far from standard practice in life sciences. FAIRification workflows must evolve to include AI-assisted metadata generation, consistency checking, and automated ontology-based enrichment. Collaboration with the AI/ML domain will be critical to ensure that FAIRification and AI-readiness co-evolve.
- As the Czech Republic invests in Human Data infrastructures and TRE-aligned environments, Data Management must adapt to the strict requirements for handling sensitive data. Ensuring provenance tracking, version control, auditing, consent compliance, and secure metadata management will be essential for integrating genomic and clinical data into national and European federated networks (GDI, FEGA, EHDS). This adds a regulatory dimension to FAIRification that requires coordination with legal, ethical, and clinical partners.
- Finally, sustainability is an ongoing challenge. As the number of tools, institutions, and datasets grows, maintaining consistent FAIR quality, community training, and long-term service availability requires institutional and financial commitment. ELIXIR CZ must balance innovation with operational stability, ensuring long-term support for widely adopted tools like DSW while developing next-generation FAIRification technologies.
These challenges, while substantial, present a strategic opportunity: ELIXIR CZ can guide Czech science through the transition to a fully FAIR, interoperable, AI-ready data ecosystem and strengthen its influence within European infrastructures.
