wheat data interoperability (3) by esther dzale yeumo kabore and richard fulss

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Wheat Data Interoperability Esther DZALE YEUMO KABORE Richard FULSS

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Page 1: Wheat Data Interoperability (3) by Esther DZALE YEUMO KABORE and Richard FULSS

Wheat Data InteroperabilityEsther DZALE YEUMO KABORE

Richard FULSS

Page 2: Wheat Data Interoperability (3) by Esther DZALE YEUMO KABORE and Richard FULSS

2Survey results – Data storage practices

114 of the196 respondents currently store their data on local drives; 84 are willing to use shared databases and repositories.

Page 3: Wheat Data Interoperability (3) by Esther DZALE YEUMO KABORE and Richard FULSS

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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

Page 4: Wheat Data Interoperability (3) by Esther DZALE YEUMO KABORE and Richard FULSS

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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

Page 5: Wheat Data Interoperability (3) by Esther DZALE YEUMO KABORE and Richard FULSS

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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

Page 6: Wheat Data Interoperability (3) by Esther DZALE YEUMO KABORE and Richard FULSS

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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