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GEO Data branding

 

The intergovernmental Group on Earth Observations (GEO), through its members and participating organisations has been a strong proponent of open data. In recent years GEO has worked towards practical implementation of declarations from G8 and G20 on open data. A key success story of GEO is the definition and uptake of data sharing principles. It has been a great step forwards for users to know that data are available under GEO data sharing principles or as part of GEO Core Infrastructure. However data users face a challenge in knowing to what extent they can rely on data. Users often need more information, such as what they might expect in terms of updating and or quality control in order to make use of data sources and manage dependencies created. For this reason GEO developed the Global Earth Observation System of Systems (GEOSS) Data Management Principles (DMP) to promote and encourage best practices that will result in users being able to derive the most benefit from data obtained via GEOSS.

 

The data management principles were adopted by the XII GEO Plenary in Mexico city declaration giving 10 principles that GEOSS providers are encouraged to follow. In practice, a dataset that follows the DMPs is considered a good GEOSS dataset.

 

Data providers often make data available freely for re-use. Maximised re-use creates added societal value from investments in creating and maintaining data. Datasets are often regularly updated, and undergo rigorous quality control for which there is little visibility.

 

The GEO label is a voluntary label based on GEO data management principles that enables data providers to gain more visibility for the effort they put into making data available, and at the same time can maximise the potential for appropriate re-use and combination with other data sources.

 

Users looking for data will increasingly be able to rely on data branded with GEO label as a easily recognizable stamp of quality. Data providers making data available will get increased visibility for their data and the effort they put into making their processes conformant with the DMP.