Alliance for a Data Revolution: CGIAR Platform for Big Data in Agriculture 2017 Convention Report
On September 19-22, 2017 the Consultative Group for International Agricultural Research1 (CGIAR) gathered over 300 local and international researchers, non-profits, public and private sector actors for the first CGIAR Platform for Big Data in Agriculture Convention, hosted by the International Center for Tropical Agriculture (CIAT) in Palmira, Colombia. The Convention marked the programmatic launch of the Platform, which aims to enable the development sector to embrace data and other digital technology approaches to solve agricultural development problems faster, better and at greater scale. The Platform works across the CGIAR network and CGIAR Research Programs (CRPs) and with the gamut of stakeholders in the agriculture sector as they grapple with creation, curation, and sharing data to enable new approaches to complex development challenges. The Platform is designed around three strategic pillars: Organize, Convene, and Inspire. The first aims to organize data so datasets are findable, accessible, and interoperable so they can be used increasingly in big data analytics. In addition, this pillar will develop open digital infrastructures for the sector that support the CGIAR’s work and enable new partnerships and innovations. The aim to convene analysts, researchers and public, private and non-profit actors in the agriculture sector will build new partnerships that both shape and fully leverage digital technologies in support of global agricultural development. The final pillar is to inspire these actors to push the limits of research and innovation to generate new data-driven approaches that solve real world development problems faster, cheaper, and more efficiently.
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| Format: | Report biblioteca |
| Langue: | English |
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International Center for Tropical Agriculture
2017
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| Sujets: | data, big data, grupo consultivo sobre investigación agrícola internacional, investigación agricola, technological innovation, innovation, data sets, agriculture, |
| Accès en ligne: | https://hdl.handle.net/10568/89448 |
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