The Data in Emergencies (DIEM) Open Science Research Grant Program Phase I is implemented in partnership with The Food and Agricultural Organization (FAO) as well as partners from Tulane University. It is a targeted competitive small grant mechanism that gives funding to university-based researchers within the RAN network, to leverage extensive, real-time field data collected through the FAO interventions and programs to generate actionable knowledge to strengthen resilience of communities and programs implemented by FAO. The mechanism aims to fund researchers to conduct secondary analyses to assess the impact of shocks and stressors such as conflict, climate extremes, and economic instability, on agricultural livelihoods and food security across about 30 food crisis African countries where FAO is implementing projects. The grant mechanism provides up to USD 10,000 per project to support this vital data analysis and knowledge dissemination.

The core objective is to bridge critical evidence gaps in programming by utilizing the DIEM Information System’s vast datasets, which includes surveys from approximately 400,000 households, and /or supplement with other similar datasets. The key thematic priorities to which the funded awards align with include:

  • assessing the effectiveness of FAO resilience and emergency interventions,
  •  characterizing disaster trends and cumulative impacts,
  •  identifying household and community resilience factors, and
  • exploring advanced analytical applications like machine learning to model shock impacts.

The insights generated from these awards over a three months period are intended to directly inform and improve the design, targeting, and implementation of humanitarian and resilience programming by FAO and other development partners. By supporting university-based researchers within the RAN network in Africa, this mechanism aims to explore the analytical depth of the data from the DIEM system, ensuring that decision-making for agricultural support in emergencies is grounded in robust, context-specific evidence.



Categories: RANProject, RanProject_Research