Linked SDMX Data: 

As statistical data is inherently highly structured and comes with rich metadata (in form of code lists, data cubes etc.), it would be a missed opportunity to not tap into it from the Linked Data angle. At the time of this writing, there exists no simple way to transform statistical data into Linked Data since the raw data comes in different shapes and forms. Given that SDMX (Statistical Data and Metadata eXchange) is arguably the most widely used standard for statistical data exchange, a great amount of statistical data about our societies is yet to be discoverable and identifiable in a uniform way. Therefore, the goal of this document is to present an investigation of the design and implementation of an SDMX-ML to RDF/XML XSL Transformations, as well as the transformation and publication of OECD, BFS, and FAO datasets with that tooling.

OECD Linked Data:

BFS Linked Data:

FAO Linked Data:

ECB Linked Data:

#LinkedData  +OECD +Food and Agriculture Organization of the UN +United Nations  #BFS   #ECB    #Statistics   #SDMX  
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