EML is a widely used metadata standard in the ecological and environmental sciences. We strongly recommend that interested users visit the EML Homepage for an introduction and thorough documentation of the standard. Additionally, the scientific article The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere (Jones et al 2006) provides an excellent introduction into the role EML plays in building metadata-driven data repositories to address the needs of highly heterogeneous data that cannot be easily reduced to a traditional vertically integrated database. At this time, the
EML R package provides support for the serializing and parsing of all low-level EML concepts, but still assumes some familiarity with the EML standard, particularly for users seeking to create their own EML files. We hope to add more higher-level functions which will make such familiarity less essential in future development.
EML v2.0 is a complete re-write which aims to provide both a drop-in replacement for the higher-level functions of the existing EML package while also providing additional functionality. This new
EML version uses only simple and familiar list structures (S3 classes) instead of the more cumbersome use of S4 found in the original
EML. While the higher-level functions are identical, this makes it easier to for most users and developers to work with
eml objects and also to write their own functions for creating and manipulating EML objects. Under the hood,
EML relies on the emld package, which uses a Linked Data representation for EML. It is this approach which lets us combine the simplicity of lists with the specificity required by the XML schema.
This revision also supports the recently released EML 2.2.0 specification.
Here we show the creation of a relatively complete EML document using
EML. This closely parallels the function calls shown in the original EML R-package vignette.
The original EML R package defines a set of higher-level
set_* methods to facilitate the creation of complex metadata structures.
EML provides these same methods, taking the same arguments for
set_textType, as illustrated here:
geographicDescription <- "Harvard Forest Greenhouse, Tom Swamp Tract (Harvard Forest)" coverage <- set_coverage(begin = '2012-06-01', end = '2013-12-31', sci_names = "Sarracenia purpurea", geographicDescription = geographicDescription, west = -122.44, east = -117.15, north = 37.38, south = 30.00, altitudeMin = 160, altitudeMaximum = 330, altitudeUnits = "meter")
We read in detailed methods written in a Word doc. This uses EML’s docbook-style markup to preserve formatting of paragraphs, lists, titles, and so forth. (This is a drop-in replacement for EML
We can also read in text that uses Markdown for markup elements:
Attribute metadata can be verbose, and is often defined in separate tables (e.g. separate Excel sheets or
.csv files). Here we use attribute metadata and factor definitions as given from
attributes <- read.table(system.file("extdata/hf205_attributes.csv", package = "EML")) factors <- read.table(system.file("extdata/hf205_factors.csv", package = "EML")) attributeList <- set_attributes(attributes, factors, col_classes = c("character", "Date", "Date", "Date", "factor", "factor", "factor", "numeric"))
physical metadata specifying the file format is extremely flexible, the
set_physical function provides defaults appropriate for
.csv files. DEVELOPER NOTE: ideally the
set_physical method should guess the appropriate metadata structure based on the file extension.
EML R package, objects for which there is no
set_ method are constructed using the
new() S4 constructor. This provided an easy way to see the list of available slots. In
eml2, all objects are just lists, and so there is no need for special methods. We can create any object directly by nesting lists with names corresponding to the EML elements. Here we create a
keywordSet from scratch:
keywordSet <- list( list( keywordThesaurus = "LTER controlled vocabulary", keyword = list("bacteria", "carnivorous plants", "genetics", "thresholds") ), list( keywordThesaurus = "LTER core area", keyword = list("populations", "inorganic nutrients", "disturbance") ), list( keywordThesaurus = "HFR default", keyword = list("Harvard Forest", "HFR", "LTER", "USA") ))
Of course, this assumes that we have some knowledge of what the possible terms permitted in an EML keywordSet are! Not so useful for novices. We can get a preview of the elements that any object can take using the
emld::template() option, but this involves a two-part workflow. Instead,
eml2 provides generic
construct methods for all objects.
For instance, the function
eml$creator() has function arguments corresponding to each possible slot for a creator. This means we can rely on tab completion (and/or autocomplete previews in RStudio) to see what the possible options are.
eml$ functions exist for all complex types. If
eml$ does not exist for an argument (e.g. there is no
eml$givenName), then the field takes a simple string argument.
my_eml <- eml$eml( packageId = uuid::UUIDgenerate(), system = "uuid", dataset = eml$dataset( title = "Thresholds and Tipping Points in a Sarracenia", creator = aaron, pubDate = "2012", intellectualRights = "http://www.lternet.edu/data/netpolicy.html.", abstract = abstract, keywordSet = keywordSet, coverage = coverage, contact = contact, methods = methods, dataTable = eml$dataTable( entityName = "hf205-01-TPexp1.csv", entityDescription = "tipping point experiment 1", physical = physical, attributeList = attributeList) ))
We can also validate first and then serialize: