datapack: A Flexible Container to Transport and Manipulate Data and Associated Resources

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The datapack R package provides an abstraction for collating heterogeneous collections of data objects and metadata into a bundle that can be transported and loaded into a single composite file. The methods in this package provide a convenient way to load data from common repositories such as DataONE into the R environment, and to document, serialize, and save data from R to data repositories worldwide.

Note that this package (‘datapack’) is not related to the similarly named rOpenSci package ‘DataPackageR’. Documentation from the DataPackageR github repository states that “DataPackageR is used to reproducibly process raw data into packaged, analysis-ready data sets.”

Installation Notes

The datapack R package requires the R package redland. If you are installing on Ubuntu then the Redland C libraries must be installed before the redland and datapack package can be installed. If you are installing on Mac OS X or Windows then installing these libraries is not required.

The following instructions illustrate how to install datapack and its requirements.

Installing on Mac OS X

On Mac OS X datapack can be installed with the following commands:


The datapack R package should be available for use at this point.

Note: if you wish to build the required redland package from source before installing datapack, please see the redland installation instructions.

Installing on Ubuntu

For Ubuntu, install the required Redland C libraries by entering the following commands in a terminal window:

sudo apt-get update
sudo apt-get install librdf0 librdf0-dev

Then install the R packages from the R console:


The datapack R package should be available for use at this point

Installing on Windows

For windows, the required redland R package is distributed as a binary release, so it is not necessary to install any additional system libraries.

To install the R packages from the R console:


Quick Start

See the full manual for documentation, but once installed, the package can be run in R using:


Create a DataPackage and add metadata and data DataObjects to it:

dp <- new("DataPackage")
mdFile <- system.file("extdata/sample-eml.xml", package="datapack")
mdId <- paste("urn:uuid:", UUIDgenerate(), sep="")
md <- new("DataObject", id=mdId, format="eml://", file=mdFile)
addData(dp, md)

csvfile <- system.file("extdata/sample-data.csv", package="datapack")
sciId <- paste("urn:uuid:", UUIDgenerate(), sep="")
sciObj <- new("DataObject", id=sciId, format="text/csv", filename=csvfile)
dp <- addData(dp, sciObj)
ids <- getIdentifiers(dp)

Add a relationship to the DataPackage that shows that the metadata describes, or “documents”, the science data:

dp <- insertRelationship(dp, subjectID=mdId, objectIDs=sciId)
relations <- getRelationships(dp)

Create an Resource Description Framework representation of the relationships in the package:

serializationId <- paste("resourceMap", UUIDgenerate(), sep="")
filePath <- file.path(sprintf("%s/%s.rdf", tempdir(), serializationId))
status <- serializePackage(dp, filePath, id=serializationId, resolveURI="")

Save the DataPackage to a file, using the BagIt packaging format:

bagitFile <- serializeToBagIt(dp) 

Note that the dataone R package can be used to upload a DataPackage to a DataONE Member Node using the uploadDataPackage method. Please see the documentation for the dataone R package, for example:

vignette("upload-data", package="dataone")


Work on this package was supported by:

Additional support was provided for working group collaboration by the National Center for Ecological Analysis and Synthesis, a Center funded by the University of California, Santa Barbara, and the State of California.