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Published December 2, 2022 | Version 0.1.9
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dataset: Create Data Frames that are Easier to Exchange and Reuse



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The primary aim of dataset is create well-referenced, well-described, interoperable datasets from data.frames, tibbles or data.tables that translate well into the W3C Data Cube Vocabulary based on the statistical SDMX data cube model. Such standard R objects (data.fame, data.table, tibble, or well-structured lists like json) become highly interoperable and can be placed into relational databases, semantic web applications, archives, repositories. They follow the FAIR principles: they are findable, accessible, interoperable and reusable.

Our datasets:

  • Contain Dublin Core or DataCite (or both) metadata that makes the findable and easier accessible via online libraries. See vignette article Datasets With FAIR Metadata.

  • Their dimensions can be easily and unambigously reduced to triples for RDF applications; they can be easily serialized to, or synchronized with semantic web applications. See vignette article From dataset To RDF.

  • Contain processing metadata that greatly enhance the reproducibility of the results, and the reviewability of the contents of the dataset, including metadata defined by the DDI Alliance, which is particularly helpful for not yet processed data;

  • Follow the datacube model of the Statistical Data and Metadata eXchange, therefore allowing easy refreshing with new data from the source of the analytical work, and particularly useful for datasets containing results of statistical operations in R;

  • Correct exporting with FAIR metadata to the most used file formats and straighforward publication to open science repositories with correct bibliographical and use metadata. See Export And Publish a dataset.

  • Relatively lightweight in dependencies and easily works with data.frame, tibble or data.table R objects.


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