dataset 0.3.0: New CRAN release with RDF functionality

The dataset package extends the concept of tidy data and adds further, standardized semantic information to the user’s dataset to increase the (re-)use value of the data object.

• More descriptive information about the dataset as a creation, its authors, contributors, reuse rights and other metadata to make it easier to find and use.
• More standardized and linked metadata, such as standard variable definitions and code lists, enable the data owner to gather far more information from third parties or for third parties to understand and use the data correctly.
• More information about the data provenance makes the quality assessment easier and reduces the need for time-consuming and unnecessary re-processing steps.
• More structural information about the data makes it more accessible to reuse and join with new information, making it less error-prone for logical errors.

Check out the new vignette article From dataset To RDF.

Adding semantic information to variables, observations and the structure

New vignettes on

• Richer Semantics for the Dataset's Variables
• Richer Semantics for the Dataset's Observations
• Richer Description of the Datasets's Structure for Meaningful Reuse Opportunities

CRAN release: new s3 classes and improved interoperability

After reviewing various user experiences and expectations, this is a seriously re-written, yet still rather experimental, release with no new long-format (vignette) documentation. For consulting development plans, please refer to Making Datasets Truly Interoperable.

0.2.1 Documentation improvements

Update README

Improved methods for the dataset s3 class

• New methods for the dataset() s3 class: print.dataset(), summary.dataset(), subset.dataset, [.dataset.
• New vignette on how to use the dataspice package programmatically for publishing dataset documentation.

0.1.9 First CRAN release

cran submission

0.1.7. rOpenSci submission version

• rOpenSci submission

dataset: Create interoperable and well-documented data frames

The goal of dataset is to create datasets from standared R objects (data.fame, data.table, tibble, or well-structured lists like json) that are highly interoperable and can be placed into relational databases, semantic web applications, archives, repositories.

• Added the Motivation of the dataset package vignette article.
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