2026-10-28 –, LUMC06
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.
Rationale. Contemporary disasters, cascading, compounding, and cross-sectoral, demand information systems that can assemble reliable signals from dozens of sources and translate them into coordinated action within hours. Current Disaster and Emergency Management (DEM) frameworks struggle with the very characteristics that define modern crises: non-linearity, emergent behaviour, and coordination across heterogeneous actors. Information bottlenecks in disaster response, including siloed datasets, incompatible standards, and missing "last mile" connectivity, are themselves a form of structural vulnerability.
The CODATA Task Group on FAIR Data for Disaster Risk Research has found that the critical gap is not data volume but the ability to locate, connect, and act on data across sectors in real time. Understanding what types of PIDs are used and the metadata they provide is a critical first step towards building a persistent, navigable, machine-actionable graph of the entities and relationships, including hazards, sensors, datasets, systems, organisations, people, and places, that constitute a data-driven disaster risk information ecosystem. PIDs are foundational to FAIR data infrastructure, but their application in disaster risk reduction has been fragmented. A PID graph for DRR would connect meteorological observation records to hydrological models, early warning systems to community-level response plans, damage assessments to reconstruction datasets, and operational actors to their mandates and capabilities, all in a machine-traversable, policy-aware form.
CODATA's Cross-Domain Interoperability Framework (CDIF) will be applied as a way of framing the challenge of building a PID graph for DRR. CDIF domains of Discovery, Data Access, Controlled Vocabularies, Data Integration, and Universals (time, geography, and units of measure), will be mapped onto draft recommendations for the functional requirements of a DRR PID graph. CDIF's approach to machine-actionable access policies using ODRL is particularly relevant for the sensitive, time-critical, multi-jurisdictional data sharing that disaster response demands. Similarly, CDIF's universals profile addresses the geographic and temporal anchoring essential to any early-warning or situational-awareness system.
Workshop Design
0-9: Framing the problem: the complex adaptive systems perspective on DEM (drawing on the CADEM framework) and participants' practical experience building end-to-end multi-hazard early warning systems.
10-39: Mapping the PID landscape: How are PIDs used to describe the different elements and systems in DRR? Which entities in the DRR ecosystem most urgently need PIDs or need to extend the metadata expressed through PIDs? Where are the critical gaps within the disaster management cycle (preparedness, response, recovery, reconstruction)?
40-49: Applying CDIF: How do CDIF's discovery, integration, access, and vocabulary profiles need to be extended or specialised for cross-domain DRR use cases? What does a minimal viable DRR metadata profile look like?
50-60: Governance and implementation barriers: What institutional, policy, and technical obstacles prevent a functional DRR PID graph today, and what practical first steps, including standardisation mandates, pilot implementations, and community registries, could move this forward?
Session closes with a brief summary of commitments and next steps towards a community-driven agenda for building a PID graph for DRR.
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).