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FAIR Data for Disaster Risk Research

Mission and objectives

To demonstrate data science applications in the field of disaster risk reduction.

To study the mechanism for connecting data and data networks to enable easier and faster discovery, access and creating positive impact in the disaster risk reduction.

Significance

FAIR-DRR is an increasingly important activity linking and ensuring coherence of major global milestones – the Sendai Framework for Disaster Risk Reduction (SFDRR), Sustainable Development Goal (SDG), Paris Agreement for Climate Change and the New Urban Agenda (NUA)-Habitat III. Although large amounts of data exist today, they are typically dispersed geographically, owned by various entities including government agencies, research centers, community groups and, sometimes, individuals around the world, lacking standards, interoperability, and accessibility, making them difficult to access and utilise for research, assessment and policy decision.  There can be significant delays in the publication of available data and, despite improvements in data integration, significant data silos remain, forming barriers to the use of the data. Therefore, there need to be clear processes in place for ensuring that data are accessible and available following the FAIR principle for disaster and climate risk management. In 2015, the CODATA task group FAIR Data for Disaster Risk Research (FAIR-DRR, formerly called LODGE) was established to study the mechanism for connecting such data and data networks to enable easier and faster discovery, access and creating positive impact in the society. The FAIR-DRR was able to demonstrate data science applications in the field and in recognition, CODATA awarded the GEO SDG Testimonial Award for work on Rapid Damage Mapping response in support of SDG11 in 2020.

Impact

The experiences of the Covid-19 pandemic in the past year have made all disciplines keenly aware that solutions to complex and difficult problems require data to be readily assessable and actionable by machines using big data in combination with the most advanced hardware and software technologies. Our technology is advancing rapidly, however, our data systems are not able to achieve the same milestones. The fundamental enabler of data-driven science is an ecosystem of resources that enable data to be FAIR (Findable, Accessible, Interoperable, and Re-usable) for humans and machines. FAIR- DRR ensures effective, maximally automated data stewardship, and effective terminologies,  metadata specifications, and partnerships.

Planned activities and outputs for 2021-2023:

  • Volunteer Rapid Disaster Monitoring and Mapping in collaboration with Earth-GEO
  • Enhance interdisciplinary data integration using FAIR-DRR’s sequence, partnership with other networks and documenting good practices.
  • Engage with users and sectors for greater alignment and consistency of hazard definitions, standardisation of data loss quantification and risk assessment
  • Demonstrate transdisciplinary approaches linking climate scientists, engineers and sectoral professionals to identify future emerging and complex research using data
  • Capacity building using monthly newsletters, policy papers, conference, webinars and white papers.

Contacts

Co-chairs:

  • Dr Bapon Fakhruddin, Tonkin + Taylor, New Zealand
  • Prof Guoqing Li, Aerospace Information Research Institute, CAS, China
  • Dr Carol Song, Rosen Centre for Advanced Computing, Purdue University, USA
  • Dr Nina I. Frolova, Russia Academy of Sciences and Extreme Situation Research Center, Russia

TG Secretary:

  • Dr Bapon Fakhruddin, Tonkin + Taylor, New Zealand