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The interoperability and reusability of datasets is greatly enhanced if they follow a standardized and practical format. Our datasets follow the tidy data principles^[Wickham, H.(2014). Tidy Data. Journal of Statistical Software, 59(10), 1–23. <https://doi.org/10.18637/jss.v059.i10>] and are interoperable with the W3C [DF Data Cube Vocabulary]^[RDF Data Cube Vocabulary, W3C Recommendation 16 January 2014 <https://www.w3.org/TR/vocab-data-cube/#metadata>] (semantic web) and SDMX (statistical) dataset definitions.
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The interoperability and reusability of datasets is greatly enhanced if they follow a standardized and practical format. Our datasets follow the tidy data principles^[Wickham, H.(2014). Tidy Data. Journal of Statistical Software, 59(10), 1–23. <https://doi.org/10.18637/jss.v059.i10>] and are interoperable with the W3C ^[DF Data Cube Vocabulary]^[RDF Data Cube Vocabulary, W3C Recommendation 16 January 2014 <https://www.w3.org/TR/vocab-data-cube/#metadata>] (semantic web) and SDMX (statistical) dataset definitions.
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Both W3C and SDMX uses are more complex object, the Datacube in its description. The dataset is a redused datacube. To adhere to tidy data principles and easy use in reproducible resaerch workflows, we further reduced our subjective definition of the dataset.
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