1. Wheat Data Interoperability Esther DZALE YEUMO KABORE Richard FULSS

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

3. 3  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 Use cases

4. 4  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. Use cases

5. 5  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. Use cases