This document summarizes the activities of the Agricultural Data Interest Group (IGAD) at various RDA meetings between 2013-2017. It discusses the establishment of IGAD and several working groups focused on specific data types like wheat, rice, and farm data. It also outlines several deliverables produced by each working group, including standards, frameworks, and guidelines related to data management, sharing, and interoperability for different agricultural domains. Finally, it emphasizes that the RDA structure enables collaboration across geographic and topical divisions to address diverse data issues in agriculture.

1. Agricultural Data Interest Group (IGAD)
Codata 2017
Hugo Besemer
FAO Consultant

2. Recent history: agricultural data discussed since RDA’s inception
2013 March Gothenburg - SWE Informal discussion
2013 September Washington DC - USA Group established
2014 March Dublin - IRL Wheat data , 2 sessions
2014 September Amsterdam - NLD IGAD, 2 sessions, Wheat
Data joint session
2015 March San Diego - USA IGAD session
2015 September Paris - FRA Pre-meeting, Wheat data
joint session
2016 March Tokyo - JPN Pre-meeting, GODAN joint
session
2016 September Denver - USA Break-out session, joint
meeting IG, WG’s Rice,
Wheat, Agrisemantics
2017 April Barcelona - ESP Pre-meeting,
2017 September Montreal - CAN Pre-meeting, breakout IG
and WG’s

3. Areas of application presented in IG meetings
2014 March Dublin - IRL Wheat interoperability
2014 September Amsterdam - NLD Wheat interoperability / Soil data /
Germplasm data
2015 March San Diego - USA What’s going on in China / What’s going on in
Japan / Researcher profiles / Wheat data
2015 September Paris - FRA Soil data / Land rights / Geospatial
Science indicators / What’s going on in Japan /
Plant genetics &genomics
2016 March Tokyo - JPN Soil Data
Rice Data
2016 September Denver - USA Semantics – State of the art in North America /
Farm data
2017 April Barcelona - ESP Farm data / Science metrics and indicators /
Wheat data / Rice Data / Germplasm data / Soil
data / Weather data / Wine data
2017 September Montreal - CAN Semantics applications / Data quality / Climate &
Weather /Farm data

4. Interest Group
Agricultural Data Interest Group (IGAD)
Working Groups
Wheat Data Interoperability Working Group (Completed)
Rice Data Interoperability (endorsed and active)
AgriSemantics (endorsed and active)
On-farm Data Sharing (endorsed and active)
Birds of Feather Groups
Metrics and Indicators in Agricultural Sciences
Capacity development activities
Research data management course in Spanish
Contribution to Open Science workshops in Africa

5. Institutional
capacities
Open data (wider than research)
Infrastructures
Domain specific
Starting today ….

6. Charter:
“The Agricultural Data Interest Group is a domain oriented interest group to
work on all issues related to data important for the development of global
agriculture. The interest group aims to represent all stakeholders producing,
managing, aggregating, sharing and consuming data for agricultural research
and innovation. Efforts will be made to get an active representation of the
major international institutions, which work on agricultural research and
innovation”
“The Agricultural Data Interest Group has a specific interest in data
interoperability. This refers not only to exchange of data of the same
type, but also to data of a different types, which refers to the same
object. “
From the initial concept note
Focus area 1 : Supporting initiatives for publishing ontologies / vocabularies
Large ontologies, including vocabularies and thesauri, have been constructed. Some of these corpora are
the result of many years of collaborative effort. Such corpora form indispensable resources to foster
semantic interoperability among information systems. The first objective here is to develop a consensus
on these corpora which are sufficiently mature and robust which can be adopted as standards for the
agricultural research. The second objective is to help make these resources publicly available for
application developers.
To this end the working group will undertake the following activities:
Produce a Working Group Note on guidelines for transforming an existing
representation into an RDF/OWL representation. This note should be based on
experiences in this area.
Work with ontology / vocabulary owners to convert their corpora to RDF/OWL.
Support the publication of the resulting RDF/OWL ontology on a publicly
accessible spot.
The result should be a set of publicly available high-profile ontologies.
Focus area 2 : Data catalog
The aim here is to aggregate descriptions of semantic assets (data, reference data, etc.) from
stakeholders. To this end, the use of a common metadata vocabulary such as ADMS to document the
semantic assets in a uniformed and structured manner (name, status, version, where they can be found
on the Web, etc) would be a plus.
Focus area 3: Repository of tools and demo applications
Other focus areas?
More history: between Gothenburg and
Washington DC:
Making up minds

7. Wheat data interoperability WG
Form and description of final deliverables
• A report on the survey of existing standards
• A Wheat linked data framework specification (cookbook)
• Library of vocabularies/ontologies
• Decision tree for describing/representing data based on data and metadata
description recommendations file formats recommendations

9. Rice Data Interoperability WG
• A report on the survey of existing standards among rice research and development organizations. Focus on data
availability, accessibility and applicability, formats, ontology, standards and metadata used. A complete analysis of
interoperability (or otherwise of) among rice databases and repositories.
• A set of recommendations on good practices, ontologies, tools and examples to create, manage and
share data related to Rice. This work will be based on the existing Wheat Data WG Guidelines, The WG will Identify and
adopt those relevant to rice data, and will customize accordingly. New types of data might be added according to the
results of point 1. The expected output is a Rice Data Framework specification (cookbook)
• Evaluation of a prototype on Rice specific data registry, Recommendations on how to develop this type of
tools would be prepared and disseminated as good practices.
• Recommendations for a Rice ontology which should align existing rice ontologies, thesauri, controlled
vocabularies. This should be the basis for a prospect on multi-lingual conversion of ontologies (TH KU/JP NARO/IRRI/ IIRR
/ Bioversity) which will not be covered by this WG as a deliverable.
• Good practices/method(s) for digitization of rice legacy data in line with India's data repository that can
serve as a model for getting Thailand national legacy data available and identify best practices (India - IIRR, TH Rice
Dept/Ministry of Agric)

10. Agrisemantics WG

11. Agrisemantics WG
• A report on semantics landscape for agricultural data,
• A set of use cases and requirements,
• A document on recommendations for the future of semantics for agricultural data -
software, functionalities, semantic assets to enhance data interoperability in
agriculture.

12. On-Farm Data Sharing WG
The final deliverables of the On-Farm Data Sharing WG will consist of:
• Guidelines for minimum data requirements for field-scale, replicated strip trials completed by farmers
using GPS-guided equipment including combines with calibrated yield monitors.
• Guidelines for collecting, handling, storage and formatting results and metadata from field-scale,
replicated strip trials
• Guidelines for stewardship of data collected from field-scale, replicated trials completed on production
grain fields, which will include guidelines for:
• Who has access to the data
• Allowable uses of the data
• Curation of the data
• Maintaining confidentially of the data

13. Metrics and Indicators in Agricultural Sciences BOF
• Adresses both scientific and societal impact
• White paper end of 2017
• To support white paper there is a survey at tinyurl.com/agscindicators

14. Finally
• The structure of RDA made it possible that different groups
(geographically, different issues) come together and get things done
• There is not one blueprint that will work for all data-related issues
• Even between wheat and rice there are differences
• On-farm data brings new dimensions like ownership, privacy and acceptable
uses