Data Innovation Fund (DIF)
Learn more about the various innovation projects, which address Sustainable Development Goals in countries around the world. You'll find final reports, lessons learned, case studies and links to project outputs.
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April 5, 2021 — Progress toward the achievement of the Sustainable Development Goals (SDGs) is contingent on the availability of good quality data, and no small amount of innovation. However, persistent data gaps plague low- and middle-income countries, due to a range of challenges,
March 3, 2021 — The demand for cocoa is increasing worldwide, but cocoa-producing regions are suffering from declining yields because of declining soil fertility, climate change, and disease which threatens the cocoa supply chains.
December 15, 2020 — Globally, the population aged 65 and over is growing faster than all other age groups. According to data from World Population Prospects: the 2019 Revision, by 2050 one in six people in the world will be over age 65 (16%), up from one in 11 in 2019 (9%).
November 24, 2020 — Remote sensing techniques in the Amazon can provide a wide range of data, from deforested areas to potential oil and gas exploration sites. How can the sustainable development need of indigenous peoples be addressed without exploiting
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The Global Data Facility is the new 香港六合彩资料-hosted innovative global funding instrument for the world's most critical data impact opportunities. For the latest updates, visit our new website at .
About the Data Innovation Fund
In August 2014, UN Secretary-General Ban Ki-moon asked an Independent Expert Advisory Group to make concrete recommendations on bringing about a in sustainable development. was one of the four areas of consultation identified by the group, along with a recognition that few mechanisms were in place to that explored newer methods of data collection, curation, and management. Collaboration between the private and public sectors was seen as critical to achieving this goal.
In 2016, the 香港六合彩资料 Development Data Group (DECDG) established the Data Innovation Fund (DIF) as a key component of the Trust Fund for Statistical Capacity Building III (TFSCB-III) to support innovative collaborations for data production, dissemination, and use. In a partnership with the (GPSDD), the fund was open to three rounds of proposals (between 2016 and 2018) from a variety of sectors and disciplines, calling for innovative solutions to a diverse set of problems identified by governments or other key actors. Each of the funded interventions benefited low- and lower-middle-income countries and were linked to the .
Given the 香港六合彩资料’s interest in replicability and scalability, the DIF results and lessons learned were designed to be readily adapted to other operations, regions, and sectors. The Bank was particularly interested in projects that resulted in data or methods being produced more quickly, at lower cost, and/or at a higher resolution or granularity, towards addressing critical data gaps. Ultimately, the goal of DIF was to encourage collaboration, experimentation, learning, and capacity development in the field of sustainable development data. DIF closed in early 2021.
Financing Partners
The funding for DIF was made available from the 香港六合彩资料’s Trust Fund for Statistical Capacity Building III (TFSCB-III), financed by the United Kingdom Foreign, Commonwealth and Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea.
Contact us
Email: data@worldbank.org
Social media: , , #data4sdgs
The Data Innovation Fund (DIF) financed scalable, replicable, and innovative approaches to improving data on the ground, with a strong emphasis on knowledge-sharing. During three calls for proposals in 2016, 2017, and 2018, the Fund received approximately 900 proposals for projects in support of multiple SDGs across Sub-Saharan Africa, East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, and South Asia. The 38 winning projects received funding in the range of $25,000 to $250,000 each.
The first round of the DIF was and called for proposals covering any theme related to the SDGs. Leveraging the Global Partnership for Sustainable Development Data’s (GPSDD) large network of partners, the call resulted in the submission of over 400 proposals. A multi-stakeholder panel was assembled to review and evaluate proposals and . Round 1 winners were brought together for a one-day Innovation Fund Learning Session Workshop in March 2018. Read the .
Drawing on lessons learned from the first round, the second round in 2017 focused on two thematic areas – ‘leave no one behind’ and the environment – requiring that the group of collaborators on any proposal include the potential user of the innovation, to ensure that the innovation was geared to practical needs and existing demands. After , the resulted in the submission of over 200 proposals. Proposals were reviewed by a multi-stakeholder expert review panel assembled from the 香港六合彩资料, the UK Department for International Development (now Foreign, Commonwealth & Development Office), and GPSDD’s Technical Advisory Group and partners. The were announced in January 2018.
Following the success of the first two rounds of funding, DIF launched the . This round called for ideas with an established proof of concept that benefited local decision-making, resulting in approximately 300 proposal submissions. DIF was looking for collaborative projects that fostered synergies and took advantage of the relative strengths and responsibilities of official and non-official actors in the data ecosystem.
The tackled issues such as disease outbreak warnings, urban sanitation, urban planning, air pollution, agriculture, and more. The projects were implemented by collaborating organizations that each brought diverse yet complementary sets of tools, skills, and expertise.
By Craig Hammer, Siddhesh Kaushik, Sun Hwa Song and Leslie Ricketts
By Sun Hwa Song, Leslie Ricketts and Siddhesh Kaushik
By Sun Hwa Song, Siddhesh Kaushik and Leslie Ricketts
By Sun Hwa Song, Siddhesh Kaushik and Leslie Ricketts
By Gayatri Singh, Jeremia Sir Nindyo Mamola and Daniela Evia Duarte
By Craig Hammer, Leslie Ricketts & Niharika Hanglem
By Sun Hwa Song, Siddhesh Kaushik, Leslie Ricketts and David Mariano
By Sun Hwa Song, Siddhesh Kaushik, and Leslie Ricketts
By Masako Hiraga & Sun Hwa Song
Below are case studies of completed projects.
Use our interactive tool to explore these and other projects.
- : Scale-up of systems which take data from remote sensors and surveys, on livestock, food and water, and produce a mix of reports & alerts which help pastoral herders navigate climate shocks. Satellite imagery, SMS (short message service) surveys, phone call surveys, and GIS were the technologies used to support SDG 1, SDG 2, SDG3, SDG 8 and SDG 15.
- : Scale-up of community mapping of refugee camps and non-camp communities to capture data on population, built environment, and services to aid humanitarian organizations and government agencies to improve service delivery and support to refugee communities. Geospatial, crowd sourced, and mobile data collection were used to support SDG 3, SDG 4, SDG 6, SDG 10, SDG 11 and SDG 17.
- : Use of earth observation data and citizen engagement to create electrification plans for indigenous peoples in Amazonia, and support SDG 1, SDG 7, SDG 13 and SDG 15.
- : Community mobile data collection effort among older people in urban Kenya and India in order to better understand the spatial and social barriers to older residents in accessing services. This project supported SDG 1, SDG 10 and SDG 11.
- : Scale-up of a system to help smallholders respond to climate risks by integrating multiple sources of data; presented a personalized, accessible perspective on how to adapt and respond to specific challenges. The project supported SDG 12, SDG 13, SDG 15 and SDG 17.
- : This methodology to rapidly and accurately assess post-disaster damage, extended the current focus on the most-damaged (disaster-induced losses) to the most-needed (disaster-induced vulnerability). Geospatial data, citizen science and machine learning were used to support SDG 1, SDG 10 and SDG 11.