Create Datasets that are Easy to Combine and Reuse

Tue, 22 Nov 2022 09:09:00 +0100

The latest Reprex R package, dataset was released today on the Comprehensive R Archive Network. It is a very early, conceptual package that will help make scientific achievements more open, governmental data easier to find, and store information that can be better combined.

Data interoperability is almost a buzzword, yet we see very few comprehensive, good solutions to apply it. Try to find information on open government portals or on big open science repositories—apart from a few good examples, most datasets are as disorganized as any PC’s hard disk that is collecting dust in a shed.

The dataset package aims to bring together the best practices of data semantics, data organization, and the use of standard metadata to make sure that whatever you store in a data table, it will be immediately available for data analysis, activation, or combination in any new database.

Ambitious? It is, and dataset 0.1.9 is a very experimental product. While our other packages are aimed at intermediate users with a clear use case in mind, dataset at this point is aimed at package developers. Casual or even heavy R users are unlikely to download it as a standalone product. Instead, dataset aims to be a stable developer basis for our existing products, rOpenGov packages, and many new uses.

Download dataset

The metadata aim of dataset it to add standardized metadata to r data.frames, tibbles, data.tables and other similar structured, tabular objects. The organization and semantic objectives are to bring the tidy data concept closer to the datacube model, which is the basis of all statistical data exchanges, and W3C standards, which foster machine-to-machine data communications on the traditional web APIs and the semantic web.

Makes data importing easier and less error-prone;
Leaves plenty of room for documentation automation, resulting in far better reusability and reproducibility;
The publication of results from R following the FAIR principles is far easier, making the work of the R user more findable, more accessible, more interoperable and more reusable by other users;
Makes the placement into relational databases, semantic web applications, archives, repositories possible without time-consuming and costly data wrangling (See From dataset To RDF).

The first official release offers little immediate benefits. However, if you are an R package developer, we can bring you a few steps nearer to releasing your data products in a way that conforms the FAIR metadata principles. We can make a few steps to streamline your data wrangling. Make integration with relational databases easier. To make a step towards the semantic web.

dataset: Create Interoperable FAIR Datasets

Thu, 11 Aug 2022 00:00:00 +0000

Interoperable, FAIR datasets

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 DataSet definition within the Data Cube Vocabulary in a reproducible manner. The data cube model in itself is is originated in the Statistical Data and Metadata eXchange, and it is almost fully harmonzied with the Resource Description Framework (RDF), the standard model for data interchange on the web[^1].

A mapping of R objects into these models has numerous advantages:

Makes data importing easier and less error-prone;
Leaves plenty of room for documentation automation, resulting in far better reusability and reproducability;
The publication of results from R following the FAIR principles is far easier, making the work of the R user more findable, more accessible, more interoperable and more reusable by other users;
Makes the placement into relational databases, semantic web applications, archives, repositories possible without time-consuming and costly data wrangling (See From dataset To RDF).

Our package functions work with any structured R objects (data.fame, data.table, tibble, or well-structured lists like json), however, the best functionality is achieved by the (See The dataset S3 Class), which is inherited from data.frame().

Contact

For contact information, contributors, see the package homepage.

Code of Conduct

Please note that the dataset project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Semantic web | CCSI Data Observatory

Create Datasets that are Easy to Combine and Reuse

dataset: Create Interoperable FAIR Datasets

Interoperable, FAIR datasets

Contact

Code of Conduct