{"id":2258,"date":"2021-11-10T12:26:25","date_gmt":"2021-11-10T12:26:25","guid":{"rendered":"http:\/\/codata.org\/blog\/?p=2258"},"modified":"2021-11-10T14:43:26","modified_gmt":"2021-11-10T14:43:26","slug":"fair-data-for-disaster-risk-research-task-group-renewal-proposal","status":"publish","type":"post","link":"https:\/\/codata.org\/blog\/2021\/11\/10\/fair-data-for-disaster-risk-research-task-group-renewal-proposal\/","title":{"rendered":"FAIR Data for Disaster Risk Research – Task Group renewal proposal"},"content":{"rendered":"

Bapon Fakhruddin and\u00a0Li Guoqing, co-chairs<\/em><\/p>\n

After four terms of study, the award winning FAIR Data for Disaster Risk Research (FAIR-DRR<\/a>) has focused itself on addressing enabling technology (i.e. decision support system, rapid damage mapping, etc.), scientific questions, technical challenges, and best practices of disaster data standards and applications in risk assessment across disciplines, development partners and governments.\u00a0 FAIR-DRR also developed a data ecosystem by integrated other networks to work collaboratively (i.e. IRDR of UNDRR, GEO-DRR, NASA DRR working group, SDG, Disaster Statistic etc.) and applied data for cross-domain studies.\u00a0 These activities closely allied with the ISC\u2019s Decadal Programme \u201cMaking data work for cross-domain grand challenges\u201d.<\/p>\n

FAIR-DRR is an increasingly important activity linking and ensuring coherence of major global milestones \u2013 the\u00a0<\/a>Sendai Framework for Disaster Risk Reduction<\/a>\u00a0(SFDRR),\u00a0<\/a>Sustainable Development Goal<\/a>\u00a0(SDG),\u00a0<\/a>Paris Agreement for Climate Change<\/a>\u00a0and the\u00a0<\/a>New Urban Agenda (NUA)-Habitat III<\/a>.<\/p>\n

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. This ecosystem must include effective, maximally automated stewardship of data, and effective terminologies,\u00a0 metadata specifications and partnerships.<\/p>\n

Following these works the\u00a0Fifth<\/strong>\u00a0term of the FAIR-DRR task group proposed the following objectives for the 2022-2023 term.<\/p>\n