Presentation delivered at the Agricultural Data Interoperability Interest Group -- Research Data Alliance (RDA) 4th Plenary Meeting -- Amsterdam, September 2014

1. Wheat Data Interoperability
Esther DZALE YEUMO KABORE
Richard FULSS

2. Survey results – Data storage practices 2
114 of the196 respondents currently store their data on local drives; 84 are willing to use shared databases and
repositories.

3. 3
Use cases
 Scenario No 0: interoperability among heterogeneous data
sources does not’ exist
 An institution/organization decides to build an information service on
Wheat Data harvesting different and heterogeneous sources.
Constraints at interoperability level appear when data are not
standardized (data formats, models and semantics) and therefore the
information service needs a big investment on improving the data in
house.
 Scenario No. 1: interoperability among research teams
located in different places
 In this scenario, the Data are produced or collected in a precise place
and sent in an other one. The 2 teams need to agree on a standard
format for the exchanged data

4. 4
Use cases
 Scenario No. 2: interoperability among heterogeneous data
sources
 In this scenario, let’s assume a researcher wants to perform a meta-analysis
that incorporates data from many different data sources
containing information related to Wheat. How to do such an analysis
without creating a huge data warehouse? There is a need of shared
data formats but also a need to provide semantic context to the
information so the researcher will be able to quickly and easily
understand any given data.
 Scenario No. 3. interoperability among heterogeneous data
sources to build an information service on Wheat
 An institution/organization decides to build an information service on
Wheat Data harvesting different and heterogeneous sources of
information. Interoperability is facilitated by the use of data formats
and models, and therefore the integration of data requires a huge data
warehouse.

5. 5
Use cases
 Scenario No. 4. interoperability among heterogeneous data
sources to build an information service on Wheat (use of LOD
met)
 An institution/organization decides to build an information service on
Wheat Data aggregating different and heterogeneous sources of
information using linked data methodologies. The use of semantic
technologies and controlled vocabularies (multilingual scenario)
facilitate the interoperability, and therefore the integration of data and
maintenance of the service.

6. 5
Use cases
 Scenario No. 4. interoperability among heterogeneous data
sources to build an information service on Wheat (use of LOD
met)
 An institution/organization decides to build an information service on
Wheat Data aggregating different and heterogeneous sources of
information using linked data methodologies. The use of semantic
technologies and controlled vocabularies (multilingual scenario)
facilitate the interoperability, and therefore the integration of data and
maintenance of the service.