1. Supporting open data & models in the agri-food sector: Experiences from the RDA Agriculture Data IG & Wheat Data Interoperability WG V. Protonotarios (Agro-Know), I. Subirats (UN FAO), D. Madalli (ISI), J. Keizer (UN FAO) and E. Dzale (INRA) Engagement in RDA from Southern-Eastern Europe, Mediterranean and Caucasus region Workshop 25/6/2015, Athens, Greece

2. 2 An extraordinary company that captures, organizes and adds value to the rich information available in agricultural and biodiversity sciences, in order to make it universally accessible, useful and meaningful. About Agro-Know http://www.agroknow.gr http://blog.agro-know.com

3. 3 large scale data-related projects  OpenMinTeD: Open Mining INfrastructure for TExt and Data (2015- 2018)  15 partners (incl. UoA, EBI, INRA); tech+data, requirements & evaluation  Big Data Europe: Integrating Big Data, Software and Communities for Addressing Europe’s Societal Challenges (2015-2018)  12 partners (incl. FAO); agri-food community & use cases  agINFRA: a data infrastructure to support agricultural scientific communities (2011 - 2015)  12 partners (incl. FAO); tech coordinator, evaluation, sustainability  in G8 Open Data in Agriculture Action Plan for Europe  SemaGrow: Data intensive techniques to boost the real-time performance of global agricultural data infrastructures (2012 - 2015)  8 partners (incl. FAO, WUR); tech, evaluation, sustainability  in G8 Open Data in Agriculture Action Plan for Europe

4. 4 indicative list of partners & clients  Food and Agriculture Organization (FAO)  Global Forum on Agricultural Research (GFAR)  International Fund for Agricultural Development (IFAD)  The Open Data Institute (ODI)  World Bank Group (WB)  UK’s Dept for International Development (DFID)  Michigan State University (MSU)  Wageningen University & Research (WUR)  French Institute of Agricultural Research (INRA)  International Centre for Research in Organic Food Systems (ICROFS)

5. 5 advocates of open (1/2)  CIARD.net: a global movement dedicated to open agricultural knowledge  Global Open Data for Agriculture and Nutrition (GODAN): make agricultural and nutritionally relevant data available, accessible, and usable for unrestricted use worldwide

6. 6 advocates of open (2/2)  The Hague Declaration aims to foster agreement about how to best enable access to facts, data and ideas for knowledge discovery in the Digital Age.  The Bouchout Declaration for Open Biodiversity Knowledge Management

7. 7 The Agricultural Data Interest Group (IGAD)

8. 8  Agricultural Data Interest Group (IGAD): a domain oriented interest group to work on all issues related to data important for the development of global agriculture  IGAD aims to represent all stakeholders collecting, producing, managing, aggregating, sharing and consuming data for agricultural research, policy formulation, and innovation.  Goal: to promote best practices in the research domain  data sharing policies,  data management plan,  data interoperability-related aspects. Background

9. 9  Established during the 1 st RDA Plenary Meeting  March 18-20, 2013, Gothenburg, Sweden  Chairs:  Johannes Keizer (FAO of the United Nations)  Devika Madalli (Indian Statistical Institute)  Imma Subirats-Coll (FAO of the United Nations)  Representation of global agriculture research initiatives IGAD in brief

10. 10  Ensure the application of common standards for the interoperability of research outcomes in the agri-food sector  Data policies  Metadata & vocabularies  Ensure active participation of major organizations & initiatives  Work closely with the Wheat Data Interoperability WG (as a use case) Aims & objectives

11. 11  IGAD is working towards building a community of scientists, experts and Information managers to work on Soil data and common issues, as this is the International year of Soils. IGAD and the International Year of Soils http://www.fao.org/soils-2015/en

12. 12 Pre-meeting at RDA 6 th Plenary Meeting (Paris, September 21-22, 2015)  Organize discussions around 4 groups: 1.Assess and find ways to increase participation from universities, government and research organizations in the Agricultural sector worldwide 2.Define situation and requirements to meet Open Access and Research Data Policies in Agriculture 3.Increase data access and availability (formats, users) 4.Interoperability (policies, tools, taxonomies, standards) Next steps (1/2)

13. 13 RDA 6 th Plenary Meeting (Paris, September 23-25, 2015) Based on the outcomes of the pre-meeting:  identify the most important & current topics for IGAD to work on;  forge collaborations among existing members;  engage new members (initiatives, organizations, individuals);  leverage and use the RDA platform to function, report and plan outreach of the results. Next steps (2/2)

14. 14 The Wheat Data Interoperability Working Group (WDI)

15. 15  The wheat research community is facing societal challenges  Wheat is the most widely grown crop in the world and provides 20% of the daily protein and food calories in the human diet;  Wheat is the 2 nd most important food crop in the developing world (after rice);  With a predicted world population of 9 billion in 2050, the demand for wheat is expected to increase by 60% compared with 2010;  To meet this demand, mean annual yield increases must rise from the current level of 1% (2001-2010) to 1.6% (2011-2050).  Interoperability of Wheat related data is necessary to address these challenges  A variety of new technologies are producing an important quantity of heterogeneous data;  Wheat related information and data systems are diverse;  There is a lack of data harmonization and standards; The context

16. 16 The WDI working group in brief  Endorsement: March 2014  Chairs:  Esther Dzale (INRA)  Richard Fulss (CIMMYT)  Members:  ~=30 members and 15 active members,  Wheat scientists, data and metadata technologists  Goal: contribute to the improvement of Wheat related data interoperability by  Building a common interoperability framework (metadata, data formats and vocabularies)  Providing guidelines for describing, representing and linking Wheat related data

17. 17  WGI outcomes 1.A report of the survey of existing standards; 2.A cookbook intended for the Wheat data managers community, which provides them with guidelines on what data formats, metadata schemas, vocabularies and ontologies they should use to describe, represent and link different types of Wheat data; 3.A library of linked vocabularies and ontologies in machine readable formats with respect to the Linked Data standards; 4.A prototype which showcases the gain of interoperability Initial plans

18. 18 Where we are Surveys Landscape of Wheat related standards and their use by the community Comprehensive overview of Wheat related ontologies and vocabularies Workshops Recommendations Mappings between different data formats Actions to conduct in order to improve the current level of Wheat related data interoperability Interoperability use cases Implementation Interactive cookbook: recommendations + guidelines A repository of Wheat related linked vocabularies (Bioportal)

19. 19 Examples of use cases TitleSearching for germplasm with specific traits DescriptionExample of searching for germplasm with specific traits - tagged with ontology terms? Data types Germplasm Phenotype Challenges ●Metadata very important ~ standardized format ●Association of genes to traits, linked to germplasm, marker information ●Need for quality controls- how confident are you of the data source? ●Provenance of the germplasm- pedigree, ownership, ●Standard system for tracking germplasm, names Title Identification of wheat genes that control root growth DescriptionRequires: Annotated genes (Gene Ontology, PFam, and other functional annotation) Data typesGenomic annotations? - Gene location ? (IWGS-SS ID or MIPS HCS link) Challenges Mapping between wheat genes and orthologs from other species (deduce function by seq. similarity); Access to RNASeq data (genes that are not expressed in roots may be irrelevant) ; mapping of wheat genes and information on their function based on literature TitleQuery on trial data associated with varieties Data typesPhenotypic data, GIS data, (wheat economy/production data) Description To search wheat varieties with distribution maps, production figures, performances in wheat mega environments, associated projects worldwide plus layers of climatic data on specific wheat production areas and disease prevention information. ChallengesPhenotypic data should be linked to GIS data. Using keywords or ontology terms a system or a tool should be able to pull out such information from different websites/systems developed by wheat community.

20. 20 http://ist.blogs.inra.fr/wdi

21. 21  Assess the level of visibility and interoperability of Wheat related vocabularies and ontologies  Is the vocabulary/ontology updated regularly?  What license and/or copyright is used?  Is the vocabulary/ontology part of any ontology communities or listing services?  Is the vocabulary/ontology used or implemented in any database/repository?  Does the vocabulary/ontology interlink and/or map to other vocabularies and ontologies?  Does the vocabulary/ontology  Identify the domain covered by the ontologies and vocabularies  Refine the cookbook  Collect more interoperability use cases  Collect some technical details Wheat related ontologies & vocabularies survey

22. 22 Guidelines and Repository What level of visibility/operability? What content? What formats, and technologies? Wheat related ontologies & vocabularies survey

23. The Wheat related BioPortal allows one to search for terms across multiple ontologies, browse mappings between terms in different ontologies, receive recommendations on which ontologies are most relevant for a corpus, annotate text with terms from ontologies

24. 24  Target users  Wheat related data managers and database developers: guidance for determining formats for representing and storing wheat data  Wheat related researchers: guidance for documenting Wheat data  Software developers designing semantic-based search tools: guidance for choosing specific vocabularies or ontologies to base on  Impacts  Improved data discovery, reusability and interoperability  Standardization and harmonization of data will reduce variability and increase relevance of Wheat data related search tools. Expected Impact

25. 25 1.The outputs of the WDI are intended to be a building block for the Wheat Initiative Wheat Information System (WheatIS)  The WheatIS brings together the major wheat bioinformatics platforms and experts and aims to create a framework for the establishment of a global Wheat Information System 2.Agriculture related communities and initiatives such as FAO AIMS, CIARD & GODAN 3.A position paper Adoption

26. 26 Next steps  Metadata (harmonization, minimal metadata sets)  Mappings  Next workshop (29-30/6/2015)  Review and complete the recommendations  Refine and complete the guidelines and the best practices  Finalize the repository of Wheat related vocabularies  Prototyping: a semantic knowledge base  Integrate data from different data sources  Provide smart search capabilities that leverage the vocabularies used against the metadata.  Mapping exercise for researchers & organizations  Identify, record and publish stakeholders of the wheat research community

27. 27 Regional adoption perspectives

28. 28  Agricultural Data IG  Adoption investigated at global level  Initiatives like GODAN & CIARD  e.g. Global Agricultural Concept Scheme (GACS)  Regional/National level  Potential for adoption by early adopters  Slow adoption of new standards and approaches in general  Wheat Data Interoperability WG  Adoption a global level  Wheat Initiative  Regional/National level  Identify the needs of research institutes / researchers’ needs On the adoption of results

29. 29  Mapping exercise to identify regional stakeholders  To take place by August 2015 through the Wheat Data WG  Aims to identify, record and organize persons, organizations & projects working with Wheat Data  Output will be a registry that can be queried Related activities EU Map Big Data in agri-food research: www.akstem.com/bde

30. 30 Thank you vprot@agroknow.gr http://www.agroknow.gr @vprot vprot@agroknow.gr http://www.agroknow.gr Most of the material for this presentation was kindly provided by the chairs of the Agriculture Data IG & the Wheat Data WG