2026-10-28 –, LUMC01
Effective disaster detection and response depends on integrating heterogeneous signals across meteorological, geospatial, epidemiological, infrastructure, and community domains, yet these systems remain siloed, lacking a persistent, machine-actionable way to navigate the relationships among them. This workshop explores how persistent identifiers (PIDs) and a connected PID graph can serve as the connective tissue for a complex adaptive disaster management system. Drawing on the CADEM framework's emphasis on non-linear, multi-actor, feedback-driven response, and on Fakhruddin's work on FAIR data policy for disaster risk, we ask: what would it take to make cross-domain disaster data not just FAIR, but actionable at speed? Applying CODATA's Cross-Domain Interoperability Framework (CDIF), participants will work collaboratively to map PID requirements across disaster management phases, identify the governance and technical barriers to a functional DRR PID graph, and propose a practical implementation roadmap aligned with the Sendai Framework.
Many of society’s most pressing challenges, including disaster risk reduction, pandemic preparedness, climate-related hazards and their health and societal impacts, and biodiversity loss, depend on combining multiple data types from multiple disciplines, sectors, institutions, and jurisdictions. Yet PID systems and metadata may be siloed within domains where vocabularies, classifications, and PIDs are often optimised for discovery and reuse within a particular community rather than for integration across communities.
This workshop will explore how PIDs and PID graphs can enable cross-domain data discovery, integration, attribution, and reuse. Rather than proposing a single universal metadata model, the workshop will use concrete societal challenges to identify recurring barriers that cut across domains: linking datasets to the concepts, variables, locations, organizations, people, grants, data management plans, methods, and policy objectives needed to interpret and reuse them; aligning domain-specific vocabularies and classifications; and tracing downstream reuse so that data contributors receive recognition and benefit and the costs, benefits, and harms of reusing data can be surfaced to enable data driven data policy.
Participants will work with case studies, including disaster and early-warning systems, pandemic preparedness, climate-related hazards such as wildfires and urban heat, and biodiversity, to examine which relationships must be made persistently identifiable and machine-actionable. Using the CODATA Cross-Domain Interoperability Framework (CDIF), including its approach to profiling data for discovery, data access, controlled vocabularies, data integration, and universal elements, small groups will:
- map the data and relationships required to address a cross-domain societal challenge;
- identify where existing PID and metadata practices fail to support discovery, linkage, interpretation, or provenance;
- distinguish use case-specific requirements from reusable cross-domain infrastructure;
- explore how PIDs can connect datasets with grants, machine-actionable data management plans, repositories, methods, policies, and downstream outputs to understand the funding-to-data-to-policy and other impact lifecycle; and
- identify practical technical, governance, and community actions needed to strengthen PIDs for cross-domain (meta)data reuse.
The workshop aims to bring together PID infrastructure providers, domain researchers, repositories, interoperability and FAIR practitioners, policymakers, funders, and communities working on real-world societal challenges. We will work to identify a small set of shared requirements and candidate implementation pathways for PID-enabled cross-domain data reuse.
Lauren Maxwell is an epidemiologist and mixed methods researcher focused on enabling the FAIR and equitable reuse of data and samples for pandemic preparedness. She leads the FAIR and Equitable Data and Sample Reuse Research Group at Universitätsklinikum Heidelberg and serves as a Senior Researcher with the Ecraid Foundation, where she addresses data interoperability and governance challenges within a European network of clinical trials. She leads data work packages for several EU-funded pandemic preparedness consortia, including ECRAID-Base, CoMeCT, CONTAGIO, and the BE READY European Partnership for Pandemic Preparedness, developing the metadata, governance frameworks, and policy intelligence needed to support federated, pandemic-ready data ecosystems. She co-chairs the CODATA-RDA Health Data Commons Working Group and is a co-author of the CODATA Cross-Domain Interoperability Framework (CDIF).
