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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.
set_* methodsThe 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"These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.