Big Data and Maize
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The document discusses the formation of an International Agro-Informatics Alliance to harness big data capabilities to accelerate agricultural research and development. The alliance would organize CGIAR data to be truly open and available, convene partnerships between CGIAR and big data organizations, and inspire solving development problems with big data. The maize program's roles would include facilitating open access to all maize data, generating new datasets to fill gaps, and linking with other crop data communities of practice on topics like socioeconomics, crop modeling, and spatial data. The alliance aims to clean, analyze, and link diverse data sets to support research and development.
Big Data and Maize
- 1. Big Data and Maize Gideon Kruseman
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- 4. International Ago-Informatics Alliance • University of Minnesota CFANS-MSI • CIMMYT • IMPRAPA • SYNGENTA • PEPSI
- 6. The Vision: starting point for discussion The data revolution is changing the role, reach and modus operandi of research and development organizations such as CGIAR. It represents an unprecedented opportunity to find new ways of reducing hunger and poverty, but also has its risks: unequal access to and use of information could widen social inequity, and exacerbate yield gaps in agriculture. CGIAR is uniquely positioned to be a thought leader on the use of big data and information technology to drive equitable rural development, ensuring that the data revolution is democratic, and reaches the poor and marginalized.
- 7. Overview Goal: to harness the capabilities of Big Data to accelerate and enhance the impact of international agricultural research, and solve development problems faster, better and at greater scale Organise: Make CGIAR data truly open and available, revolutionise how agricultural data is collected and managed Convene: Bring big data to agriculture and agriculture to big data by partnering the CGIAR with 42 Big Data powerhouse partners Inspire: Solve development problems with big data; generate new international public goods around big data in agricultural development
- 8. Theory of change for Big Data in Agriculture • Unless our data is organized, we cannot use it effectively -> OA/OD critical factor for success • CGIAR can and should play a role on the boundary between “silicon valley” and poor rural regions • New partnerships are needed, CGIAR needs to build a foundation (human, infrastructure, social) to be at the lead of the dialogue of big data in agriculture in developing countries, and partner partner partner • Harness capacity to do CGIAR research and development smarter and faster • We need to inspire – show how it can be done, and attract private sector investment and sustainable business providing big data based services to rural communities
- 9. Big Data: A behavior change • YES big data requires large amounts of data and therefore big servers, BUT it is much more than that: • REUSING the data: Extracting embedded knowledge from existing datasets to answer questions that don’t have to do with the initial purpose for which the data was captured. • COMBINING datasets that were originally not supposed to meet, enable to relate more variables and uncover useful correlations. • ANALYZING with CREATIVITY: the data scientist needs to be innovative in the uses he is giving the data. Who would have guessed that Google requests could help fighting flu?
- 10. Many partners: central to achieving breakthrou gh big data science
- 11. Role for the Maize here: • Facilitate OA/OD compliance on all maize data: Identify standards, protocols and platforms for ensuring all CGIAR Spatial Data is OA/OD compliant • Generate groundbreaking new datasets: Identify key data gaps and derive novel ways of filling them • Link with maize data management Kate
- 12. Role for the maizehere: • socioeconomid Community of Practice: US$100k to facilitate knowledge management across the CRPs/centers (+ with partners) around socioeconomic survey data (facilitate open access socio-economic data, collaboration, and better leverage CGIAR capacity with partner capacity) CIMMYT-led • Same as above for crop modelling data CoP. This CIMMYT-led • Spatial data CoP, ontology CoP, CoP on data driven agronomy and ICTs are not CIMMYT-led • Benefit from shared services: Cloud computing, high-end processing, high-end analytics support
- 14. International agro-informatics alliance • Data cleaning and consistency testing • Data analysis using standard tools • Linking data with other data sets
- 15. International agro-informatics alliance • Maize international nursery data has been used in 2016 as a test case. • Socio-economic data is the next step • Close collaboration with: – SEP ~ Gideon – Maize data ~ Kate – Bioinformatics unit ~ Juan – ITC ~ Jens