Our symposia will feature presentations, poster discussions, and interactive demos across the following topic tracks – each designed to spark exchange, inspire collaboration, and show how metadata is making a difference in research.
All HMC Conference 2026 Track Topics
1. Advancing FAIR Metadata with AI: Methods, Challenges, and Synergies
Chair: Dr. Santiago Casas (Research Data Management, Scientific Information, German Aerospace Center (DLR e.V.))
This session explores the synergy between metadata and AI, where tools such as large language models (LLMs), retrieval-augmented generation (RAG), and machine learning methods enhance metadata quality, completeness, and interoperability. At the same time, FAIR and rich metadata improve AI performance by providing structured, unambiguous context. We will discuss innovative methods, practical challenges, and collaboration opportunities in making metadata workflows scalable, user-centric, and ready for AI-driven research.
2. Empowering Research Communities: Turning Metadata into Action
Chair: Dr. Emanuel Söding (Ocean Research Technology Centre, GEOMAR Helmholtz Centre for Ocean Research Kiel)
Turning metadata recommendations into practice requires engagement across all levels of the research data ecosystem. Overcoming differences in knowledge, communication, and technical skills requires coordinated efforts in training, devising shared guidelines and establishing communities of practice. This session explores formats, initiatives, and practical building blocks that enable communities to improve their (meta)data handling and foster sustainable change in research practices.
3. Enriched Metadata for Decision Support Systems
Chair: Hamideh Haghiri (Medical Imaging Computing, German Cancer Research Center)
This track focuses on how metadata enrichment fuels advanced decision support systems by enhancing data quality, context, and interoperability. Submissions are welcome from both conceptual and applied perspectives. We invite contributions that propose new concepts, frameworks, or models for enriching metadata to improve the foundations of decision support systems, as well as implementations or case studies that demonstrate how enriched metadata can be effectively used within decision support tools and infrastructures.
4. From Harmonisation to Action(ability)
Chair: Thomas Jejkal (Data Exploitation Methods, Scientific Computing Center, Karlsruhe Institute of Technology)
Harmonising metadata is the first step in turning data structures into machine actionable items. In this track we explore packaging and exchange formats such as RO-Crates, versatile and uniform query interfaces like SPARQL, and emerging concepts such as FAIR Digital Objects and how they support scientific impact. We particularly encourage submissions presenting practical applications, early showcases, or lessons learned from putting harmonised (meta)data into practice.
5. From Minimum Requirements to FAIR and AI-Ready: Assessing Metadata Quality
Chair: Dr. Volker Hofmann (Metadata and Information systems, Institute for Advanced Simulation - Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH)
Assessing metadata quality can follow many paths — from institutional policies and FAIR principles to domain-specific needs like AI-readiness. Yet, current formalised assessment approaches remain limited in producing objective, actionable results.
This session invites researchers and infrastructure developers to share and discuss methods for evaluating metadata quality, with a focus on strengthening assessment frameworks that can effectively guide implementation towards interoperability, and data reuse.
6. Harmonisation of Metadata: Closing Semantic Gaps
Chair: Gerrit Günther (Experiment Control and Data Acquisition, Helmholtz-Zentrum Berlin für Materialien und Energie)
The session will focus on the development of metadata standards that balance the specific requirements of a community and interoperability with advancing technologies and existing standards. We invite contributions emphasising on the semantic level, structuring of metadata, technical implementation to local and global infrastructures, up to social dimensions of standardisation, including consensus-building within diverse stakeholder groups. We encourage participants to share their experience, insights, and best practice on this topic.
7. Human-Machine Collaboration in (Meta)data Acquisition
Chair: Marta Dembska (German Aerospace Center (DLR e.V.))
Scientific progress increasingly depends on effective collaboration between humans and machines. Central to this is the digital, standardised acquisition and management of (meta)data in laboratories and fieldwork alike.
This session explores how digital tools (ELNs, LIMS), formalised workflows using ontologies and controlled vocabularies, and automation through robotics or AI agents can enhance (meta)data capture. Emphasis will be placed on metadata standardisation, intuitive data entry, and user support to ensure interoperability and reuse across research domains.
8. Semantics in Practice: Domain & Application Ontologies
Chair: Dr. Said Fathalla (Metadata and Information systems, Institute for Advanced Simulation - Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH)
This track invites contributions on the design, development, and practical application of ontologies at the domain and application levels that support the design and implementation of robust, interoperable data infrastructure. Submissions may showcase novel approaches, reuse of existing ontologies, semantic alignment strategies, and solutions that enable automated discovery, integration, and meaningful use of data across disciplines.
9. Software Interoperability for (Meta)data Acquisition
Chair: Martin Held (Helmholtz-Zentrum hereon)
Modern research labs rely on diverse (meta)data acquisition systems — from instrument software to ELNs, LIMs, and workflow managers. To capture the scientific process coherently, these systems must interoperate seamlessly.
This session focuses on practical concepts and existing solutions for bridging across these platforms — from APIs to file-based integration — including the use of semantic or machine-learning tools to enhance interoperability.
