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Overview of CGIAR’s Big Data Platform

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Publié le

Medha Devare
Sr. Research Fellow – IFPRI
[Big Data Platform Module Lead]

Ibnou Dieng
AfricaRice

Publié dans : Données & analyses
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Overview of CGIAR’s Big Data Platform

  1. 1. Overview of CGIAR’s Big Data Platform Medha Devare Sr. Research Fellow – IFPRI [Big Data Platform Module Lead] Ibnou Dieng AfricaRice February 15, 2019
  2. 2. The goal of the Big Data Platform is to harness the capabilities of data to accelerate and enhance the impact of international agricultural research.
  3. 3. https://thelukewarmersway.wordpress.com/2016/02/07/climate-scientists-in-like-flint/
  4. 4. Why share data? Funder, country policies
  5. 5. Journals increasingly require data underlying publications to be shared or deposited within an accessible database or repository – as a condition for publication. …and there are a growing number of data journals, that provide citations similar to those for publications – may be used as KPIs… Why share data? Journal requirements
  6. 6. Piwowar, H.A et al. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone .0000308 Why share data? Citation advantages Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin ...
  7. 7. “The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard
  8. 8. Hey Cigi, should I direct seed or transplant my rice? How should I manage my crop? Real-time decision support for farmers Easy natural language as an interface Smart artificial intelligence trained by CGIAR and partners Leveraging open, harmonized and interoperable “small” data into queriable large data pool
  9. 9. 1. Making data FAIR: Technical support towards CGIAR Center and partner efforts to make data Findable, Accessible, Interoperable, and Reusable. 2. Enabling data discovery: Enable the contextually-linked discovery of resources (research outputs, experts, geographies) across CGIAR. 3. Building capacity: Facilitate FAIR data and comfort with Big Data technologies - the power and the risks (in-person; guidance materials; webinars). 4. Enabling data exploration, analysis, visualization: Leverage interoperability and reusability to allow semantic exploration and seamless “plug and play” with analytical and visualization tools. How is the Platform helping with CGIAR’s FAIRy tale?
  10. 10. Support for Center repositories to implement CG Core Metadata Schema; tools to facilitate repository and data-level metadata entry using CG Core elements, AGROVOC, ontologies Refine and develop Crop Ontology, AgrO, SociO; invest in tools to enable data annotation Facilitate standardization of agronomic trial data at collection rather than at archiving, via ontology-based field book (field-testing starts early 2019) Support for Interoperability...
  11. 11. Support for Reusability (best practices in privacy/ethics)…… https://bigdata.cgiar.org/responsible-data-guidelines/
  12. 12. Getting to (and leveraging) FAIR… http://gardian.bigdata.cgiar.org/
  13. 13. Results filtered for AfricaRice
  14. 14. 71 publications, 18 datasets for Benin
  15. 15. Click to see other data these authors may have published
  16. 16. Filter to find data in GARDIAN based on these controlled vocabulary/ontology terms
  17. 17. GARDIAN algorithms attempt to find pubs related to dataset – and vice versa
  18. 18. GARDIAN brings in data from Genebanks Platform’s Genesys
  19. 19. Zoom in on map and drop pin for pop- up summary of “rice rainfed yield” for the country (Benin) – from SPAM 2005; data from 2010 and 2015 coming soon
  20. 20. …or use polygon feature for summary of “rice rainfed yield” in a particular area of interest…
  21. 21. Collaborate and convene around data and agricultural R4D Developing Technical Partnerships Providing Shared Services (data and tools) Providing Technical Training Supporting six Communities of Practice Mini-Grants for Key Datasets Convene CommunitiesofPractice Data-Driven Agronomy | CIAT Crop Modeling | CIMMYT Geospatial Analysis | IFPRI Livestock Data | Univ. Edinburgh Ontologies | Bioversity Int’l Socio-Economic Data | CIMMYT Plus…?
  22. 22. Innovation process to enhance data science research in CRPs Competition 5 pilots (100K ea); 1 scale-up (250K) Criteria - Data use - Scale - Impact - Sustainability - Innovation Inspire Topics - Revealing Food System Flows - Monitoring Pests & Diseases - Disrupting Impact Assessment - Empowering Data-Driven Farming
  23. 23. S. Mohapatra: Head, Marketing & Communications C. Kacou: Interim Head of ICT Unit M. Bernard: Head Knowledge Management P. Kouame: Data Manager AfricaRice Contributors
  24. 24. Thank you! bigdata.cgiar.org Questions? i.dieng@cgiar.org m.devare@cgiar.org Thank you!

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