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.
library(EML)me <- list(individualName = list(givenName = "Carl", surName = "Boettiger"))
my_eml <- list(dataset = list(
title = "A Minimal Valid EML Dataset",
creator = me,
contact = me)
)
write_eml(my_eml, "ex.xml")
#> NULL
eml_validate("ex.xml")
#> [1] TRUE
#> attr(,"errors")
#> character(0)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_coverage, set_attributes, set_physical, set_methods and
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
set_method())
methods_file <- system.file("examples/hf205-methods.docx", package = "EML")
methods <- set_methods(methods_file)We can also read in text that uses Markdown for markup elements:
abstract_file <- system.file("examples/hf205-abstract.md", package = "EML")
abstract <- set_TextType(abstract_file)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 .csv files.
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"))Though the 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.
physical <- set_physical("hf205-01-TPexp1.csv")In the 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$givenName), then the field takes a simple string argument.
aaron <- eml$creator(
individualName = eml$individualName(
givenName = "Aaron",
surName = "Ellison"),
electronicMailAddress = "fakeaddress@email.com")HF_address <- eml$address(
deliveryPoint = "324 North Main Street",
city = "Petersham",
administrativeArea = "MA",
postalCode = "01366",
country = "USA")publisher <- eml$publisher(
organizationName = "Harvard Forest",
address = HF_address)contact <-
list(
individualName = aaron$individualName,
electronicMailAddress = aaron$electronicMailAddress,
address = HF_address,
organizationName = "Harvard Forest",
phone = "000-000-0000")my_eml <- eml$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:
eml_validate(my_eml)
#> [1] TRUE
#> attr(,"errors")
#> character(0)
write_eml(my_eml, "eml.xml")
#> NULLEML will use the latest EML specification by default. To switch to a
different version, use emld::eml_version()
emld::eml_version("eml-2.1.1")
#> [1] "eml-2.1.1"Switch back to the 2.2.0 release:
emld::eml_version("eml-2.2.0")
#> [1] "eml-2.2.0"