Bryce Mecum

The goal of 'dataspice' is to make it easier for researchers to create basic, lightweight, and concise metadata files for their datasets. These basic files can then be used to make useful information available during analysis, create a helpful dataset "README" webpage, and produce more complex metadata formats to aid dataset discovery. Metadata fields are based on the 'Schema.org' and 'Ecological Metadata Language' standards.

Provides a programmatic interface to the 'Request Tracker' (RT) HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. 'RT' is a popular ticket tracking system.

Provides a set of functions for interacting with the 'Digital Ocean' API <https://www.digitalocean.com/>, including creating images, destroying them, rebooting, getting details on regions, and available images.

Provides read and write access to data and metadata from the DataONE network <https://www.dataone.org> of data repositories. Each DataONE repository implements a consistent repository application programming interface. Users call methods in R to access these remote repository functions, such as methods to query the metadata catalog, get access to metadata for particular data packages, and read the data objects from the data repository. Users can also insert and update data objects on repositories that support these methods.

Work with Ecological Metadata Language ('EML') files. 'EML' is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), <doi:10.1146/annurev.ecolsys.37.091305.110031>.

This is a utility for transforming Ecological Metadata Language ('EML') files into 'JSON-LD' and back into 'EML.' Doing so creates a list-based representation of 'EML' in R, so that 'EML' data can easily be manipulated using standard 'R' tools. This makes this package an effective backend for other 'R'-based tools working with 'EML.' By abstracting away the complexity of 'XML' Schema, developers can build around native 'R' list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the 'JSON-LD' representation enables the use of developer-friendly 'JSON' parsing and serialization that may facilitate the use of 'EML' in contexts outside of 'R,' as well as the informatics-friendly serializations such as 'RDF' and 'SPARQL' queries.

The Resource Description Framework, or 'RDF' is a widely used data representation model that forms the cornerstone of the Semantic Web. 'RDF' represents data as a graph rather than the familiar data table or rectangle of relational databases. The 'rdflib' package provides a friendly and concise user interface for performing common tasks on 'RDF' data, such as reading, writing and converting between the various serializations of 'RDF' data, including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld'; creating new 'RDF' graphs, and performing graph queries using 'SPARQL'. This package wraps the low level 'redland' R package which provides direct bindings to the 'redland' C library. Additionally, the package supports the newer and more developer friendly 'JSON-LD' format through the 'jsonld' package. The package interface takes inspiration from the Python 'rdflib' library.

Provides users with a simple and convenient mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface) compatible 'ODBC' (Open Database Connectivity) interface. 'Virtuoso' is a high-performance "universal server," which can act as both a relational database, supporting standard Structured Query Language ('SQL') queries, while also supporting data following the Resource Description Framework ('RDF') model for Linked Data. 'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote 'Virtuoso' servers using popular 'R' packages such as 'DBI' and 'dplyr', while also providing a high-performance solution for working with large 'RDF' 'triplestores' from 'R.' The package also provides helper routines to install, launch, and manage a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using the standard interactive installers from the 'R' command-line. By automatically handling these setup steps, the package can make using 'Virtuoso' considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing 'R' tools, including 'rdflib' and 'redland' packages.