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Call for expression of interest for contribution to the Linked Open Data for Global Disaster Risk Research

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Contribute to policy briefs on disaster risk reduction in 2020!

The CODATA Task Group on Linked Open Data for Global Disaster Risk Research (LODGD) will be working with the Integrated Research on Disaster Risk (IRDR), the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), Tonkin+Taylor International, and LODGD partners, to produce a series of policy briefs on disaster risk reduction in 2020.  The first policy brief is expected to be released in August 2020. The global pandemic is a powerful reminder of the necessity of the international community’s intensified and sustained commitment to emergency preparedness.  

We are thus inviting experts in disaster risk reduction data and policy issues to collaborate on preparing these documents.  Please follow the link below for the EOI.

Important dates: 

Expression of interest:       15 April 2020
Draft contributions:             30  May 2020
Review by ISC/CODATA:   15  June 2020 
Final policy brief:                30 June 2020
Publishing:                         15 August 2020

  • Policy and challenges in disaster risk reduction and data: Informed decision making and coordinated action for effective disaster risk reduction require timely and reliable data and information. Due to technological advances, previously unknown relationships or patterns in all aspects of nature and society can now be quickly established. However, despite these discoveries and even with the guidance from the Sendai Framework, the Paris Agreement and the Sustainable Development Goals (SDGs), many countries are still facing numerous challenges using different data for decision making, which ultimately increases fatalities and causes enormous financial losses due to disasters. The COVID-19 pandemic is a good example how some countries learned from others and how data could assist in slowing down the disease spread. It is necessary to identify what challenges are faced by the government,  nongovernmental organisations and policy users in using data for disaster risk reduction and how the FAIR principle could be applied at all government levels.
  • Interoperability and interdependency of data for disaster risk reduction: There are a number of factors that act as barriers to data interoperability and interdependency. These factors are caused by local and international limitations, which affect or compromise the effectiveness of disaster risk reduction. It is proposed to develop a guideline that breaks down these barriers and focuses on how to make domain-specific data/metadata be transformed in a way that cross-domain approaches regarding discovery and analysis can be supported for disaster risk reduction. 
  • Policy brief on data to accelerate the transition from disaster response to recovery: A number of challenges are usually faced post-disaster, including ineffective coordination between parties at both local and international levels, limited resources and financial constraints. These challenges have numerous complex factors, which  lead to long response times and even longer recovery times, causing a great deal of tension, conflict in addition to other cascading problems in the communities affected by the disaster. It is proposed to set up a baseline data with integrated data repository for disaster response to accelerate transition between the response and recovery phase. This would enable the world to better understand the health, social, economic, environmental, and other problems that arise when we fail to invest adequately in combating natural hazards. Using different domain-data could enhance better management to deal with the emergency response process and a swift transition from the response to the recovery phase.
  • Policy brief on agriculture loss modelling using big data:It is likely that climate change will lead to an increased frequency and intensity of weather-related natural disasters such as floods, storms and droughts. These events require the need to evacuate livestock to housing or flood-free areas as well as to address damage of pastures, drainage systems and field infrastructure, reduction in crop yields, loss of soil biodiversity and an increased risk of animal disease. It is proposed that big data is used in a climate forecast application to inform decision making and adaptation options, aiming to better prepare farmers and agricultural companies to reduce damages and increase productivity by adopting adaptation options.

Guidance for the policy briefs:  

The policy brief would be a summary document (6 pages maximum) containing current status, challenges, good practices and recommendations.