1. Big Data infrastructures: aspects of collaboration, innovation and engagement
Sander Janssen, Earth Informatics, Wageningen University and Research
21 September 2017, CGIAR Big Data Convention, Cali

3. Big data and the Gartner hype cycle

5. Set up of road map development
Three workshops:
First workshop, July 2017, Montpellier: available infrastructures with a mapping
Second workshop, November 27-28, 2017, Wageningen: scientific challenges and e- infrastructure needs
Third workshop, May 2018: synthesis in roadmap
Combined with:
Workshops with policy officials (3)

6. Outcomes of first workshop
The strategic reasons justifying the need for an e-infrastructure in agri-food;
The technical challenges
the application of the FAIR principles which especially relies on support for interoperability and shared semantic resources,
the delivery of data discovery and processing services and the integration of disruptive technologies;
The cultural challenges
community management and commitment for a change of research practice
sustainable governance and business models that provide incentives for data sharing;
The need to further identify and develop crucial use cases (both scientific and technical) that the e-infrastructure could support.

7. Scientific challenge: what science will become possible with e-infrastructures?
Format for case studies:
Impact pathways
Audiences
Role of Research
Challenges in data, infrastructure, processing power and analytics
Solutions for more efficient Research
Case studies:
Agricultural Monitoring
SDG 2: ending hunger and the missing middle
Animal genetics and breeding
Data services for small holders
Food Safety

9. Mission
Stakeholders benefit from improved and more effective use of open data for agriculture and nutrition by engaging with it in a practical and knowledgeable manner
Many G20 countries are already GODAN partners: directly through their governments (Mexico, Argentina, Brazil, USA, UK) or through governmental technical institutions (Canada, Germany, France, China, Italy, Japan) , other are represented through civil society organizations and academia (India, Australia). We are very proud of that .We today ask all G20 countries to join GODAN and to write the GODAN goals into the Comuniques of the MACS meeting and the ministerial meeting.

10. Activities
Improved interoperability of data through providing improved standards and innovative services.
by providing examples of improved tools for impact assessment, as well as by analysing barriers that hinder the full potential of open data initiatives and investments
The development of tailored training courses will increase the capacity of stakeholders on how to use and handle open data

11. Why weather data?
Weather patterns are changing, growing seasons get less predictable
Higher risk profiles for farming and food production
Yields and income
Access to resources and capital
Local and regional food security
Access to weather data is a strategic asset for farmers

12. Work on standards with weather data
some older standards are still very much used:
legacy and compliance reasons (like BUFR or GRIB), long-term practice in research (NetCDF), wide availability of tools to manipulate them.
recently API-based weather data services started serving data on demand and in application-friendly formats like Json and user-friendly formats like CSV.
Some work on the standardization of variable names across the different communities
No naming convention that is universally used

13. Recommendations for improvement
Promote the benefits in adopting existing metadata standards to weather data publishers.
Providing “wayfinding guides” which is tied to specific use cases.
A Q&A based site (like StackOverflow) focused around weather data, standards and use of data

14. Our understanding of Big Data
Lokers et al Big Data Technologies in Agri-Environmental Researchhttp://dx.doi.org/ /j.envsoft

15. AgroDataCube: a big open data collection for open innovation

16. Soil
Collect
Integrate
Private data
Use & visualize

17. Attributes Time Agricultural Parcels Space Soil Height
Parcels and parcel size
Crop
Time
Agricultural Parcels
Space

19. Lessons & reflections
Reflection and experimentation sessions with private sector firms to develop proto applications
Data itself:
Common problems have been solved
Time/Resource intensive for individual firms
Lowers threshold for innovation: common starting point
Added value in algorithms
Level playing field

20. Thank you!
@Wurcgi