Here we construct a common EML file, including:
- Constructing more complete lists of authors, publishers and contact.
- Summarizing the geographic, temporal, and taxonomic coverage of the dataset
- Reading in pages of methods descriptions from a Word document
- Adding arbitrary additional metadata
- Indicating the canonical citation to the paper that should be acknowledged when the data is re-used.
- Conversion between EML and other metadata formats, such as NCBII and ISO standards.
In so doing we will take a modular approach that will allow us to build up our metadata from reusable components, while also providing a more fine-grained control over the resulting output fields and files.
A basic knowledge of the components of an EML metadata file is essential to being able to take full advantage of the language. While more complete information can be found in the official schema documentation, here we provide a general overview of commonly used metadata elements most relevant to describing data tables.
This schematic shows each of the metadata elements we will generate. Most these elements have sub-components (e.g. a ‘publisher’ may have a name, address, and so forth) which are not shown for simplicity. Other optional fields we will not be generating in this example are also not shown.
- eml - dataset - creator - title - publisher - pubDate - keywords - abstract - intellectualRights - contact - methods - coverage - geographicCoverage - temporalCoverage - taxonomicCoverage - dataTable - entityName - entityDescription - physical - attributeList
In this example, we will use R to re-generate the EML metadata originally published by Ellison et al (2012), HF205 through the Harvard Forest Long Term Ecological Research Center, accompanying the PNAS paper (Sirota et al. 2013; doi:10.1073/pnas.1221037110). We have made only a few modifications to simplify the presentation of this tutorial, so our resulting EML will not be perfectly identical to the original.
We will build this EML file from the bottom up, starting with the two main components of a
dataTable indicated above: the
attributeList and the
physical file type. We will then slip these two pieces into place inside a
dataTable element, and slip that into our
eml element along with the rest of the generic metadata, much like building a puzzle or nesting a set of Russian dolls.
The original metadata file was created in association with the publication in PNAS based on a Microsoft Word document template that Harvard Forest provides to the academic researchers. Metadata from this template is then read off by hand and an EML file is generated using a combination of a commercial XML editing platform (Oxygen) for commonly used higher-level elements, and the Java platform
Morpho provided by the EML development team for lower level attribute metadata.
A fundamental part of EML metadata is a description of the attributes (usually columns) of a text file (usually a csv file) containing the data being described. This is the heart of many EML files.
attributes <- tibble::tribble( ~attributeName, ~attributeDefinition, ~formatString, ~definition, ~unit, ~numberType, "run.num", "which run number (=block). Range: 1 - 6. (integer)", NA, "which run number", NA, NA, "year", "year, 2012", "YYYY", NA, NA, NA, "day", "Julian day. Range: 170 - 209.", "DDD", NA, NA, NA, "hour.min", "hour and minute of observation. Range 1 - 2400 (integer)", "hhmm", NA, NA, NA, "i.flag", "is variable Real, Interpolated or Bad (character/factor)", NA, NA, NA, NA, "variable", "what variable being measured in what treatment (character/factor).", NA, NA, NA, NA, "value.i", "value of measured variable for run.num on year/day/hour.min.", NA, NA, NA, NA, "length", "length of the species in meters (dummy example of numeric data)", NA, NA, "meter", "real")
Every column (attribute) in the dataset needs an
attributeName (column name, as it appears in the CSV file) and
attributeDefinition, a longer description of what the column contains. Additional information required depends on the data type:
Strings (character vectors) data just needs a “definition” value, often the same as the
attributeDefinition in this case.
Numeric data needs a
numberType (e.g. “real”, “integer”), and a unit.
Dates need a date format.
Factors (enumerated domains) need to specify definitions for each of the code terms appearing in the data columns. This does not fit so nicely in the above table, where each attribute is a single row, so if data uses factors (instead of non-enumerated strings), these definitions must be provided in a separate table. The format expected of this table has three columns:
attributeName (as before),
definition. Note that
attributeName is simply repeated for all codes belonging to a common attribute.
In this case we have three attributes that are factors, To make the code below more readable (aligning code and definitions side by side), we define these first as named character vectors, and convert that to a
dplyr::frame_data function also permits this more readable way to define data.frames inline).
i.flag <- c(R = "real", I = "interpolated", B = "bad") variable <- c( control = "no prey added", low = "0.125 mg prey added ml-1 d-1", med.low = "0,25 mg prey added ml-1 d-1", med.high = "0.5 mg prey added ml-1 d-1", high = "1.0 mg prey added ml-1 d-1", air.temp = "air temperature measured just above all plants (1 thermocouple)", water.temp = "water temperature measured within each pitcher", par = "photosynthetic active radiation (PAR) measured just above all plants (1 sensor)" ) value.i <- c( control = "% dissolved oxygen", low = "% dissolved oxygen", med.low = "% dissolved oxygen", med.high = "% dissolved oxygen", high = "% dissolved oxygen", air.temp = "degrees C", water.temp = "degrees C", par = "micromoles m-1 s-1" ) ## Write these into the data.frame format factors <- rbind( data.frame( attributeName = "i.flag", code = names(i.flag), definition = unname(i.flag) ), data.frame( attributeName = "variable", code = names(variable), definition = unname(variable) ), data.frame( attributeName = "value.i", code = names(value.i), definition = unname(value.i) ) )
With these two data frames in place, we are ready to create our
The documentation of a
dataTable also requires a description of the file format itself. From where can the data file be downloaded? Is it in CSV format, or TSV (tab-separated), or some other format? Are there header lines that should be skipped? This information documents the physical file itself, and is provided using the
physical child element to the
dataTable. To assist in documenting common file types such as CSV files, the
EML R package provides the function
set_physical, which takes as arguments many of these common options. By default these options are already set to document a standard
csv formatted object, so we do not need to specify delimiters and so forth if our file conforms to that. We simply provide the name of the file, which is used as the
objectName. (See examples for
set_physical() for reading other common variations, analogous to the options covered in R’s
physical <- set_physical("hf205-01-TPexp1.csv")
Once we have defined the
physical file, we can now assemble the
dataTable element itself. Unlike the old
EML R package, in
EML version 2.0 there is no need to call
new() to create elements. Everything is just a list. Template lists for a given class can be viewed with the
dataTable <- list( entityName = "hf205-01-TPexp1.csv", entityDescription = "tipping point experiment 1", physical = physical, attributeList = attributeList)
One of the most common and useful types of metadata is coverage information, specifying the temporal, taxonomic, and geographic coverage of the data. This kind of metadata is frequently indexed by data repositories, allowing users to search for all data about a specific region, time, or species. In EML, these descriptions can take many forms, allowing for detailed descriptions as well as more general terms when such precision is not possible (such as geological epoch instead of date range, or higher taxonomic rank information in place of species definitions.)
Most common specifications can be made using the more convenient but less flexible
set_coverage() function in EML. This function takes a date range or list of specific dates, a list of scientific names, a geographic description and bounding boxes, as shown 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")
Careful documentation of the methods involved in the experimental design, measurement and collection of data are a key part of metadata. Though frequently documented in scientific papers, such method sections may be too brief or incomplete, and may become more readily disconnected from the data file itself. Such documentation is usually written using word-processing software such as MS Word, LaTeX or markdown. Users with
pandoc installed (which ships as part of RStudio) can install the
rmarkdown package to take advantage of its automatic conversion into the DocBook XML format used by EML. Here we open a MS Word file with the methods and read this into our methods element using the helper function
set_methods(). While not used in this example, note that the
set_methods() function also includes many optional arguments for documenting additional information about sampling, or relevant citations.
Individuals and organizations appear in many capacities in an EML document. Meanwhile, R already has a native object class,
person for describing individuals, which it uses in citations and package descriptions, among other things. We can use native R function
person() to create an R
person object. Often it is more convenient to use R’s coercion function,
as.person(), to turn a string with standardized notation into a
person class (Though this is not always reliable, for instance, in surnames containing whitespace). However it is constructed, a
person class can then be coerced into the appropriate EML object like so:
Likewise this method can be applied to a list of
Note that R only permits certain codes such as
ctb be be given in square brackets or as the
role slot in a
We can instead always use the list approach to create any of these elements, instead of the shorthand coercion methods shown above. This permits a bit more flexibility, particularly for constructing elements where we want to include more metadata than R’s
person object knows about. Here we define an
address element first, since we can then re-use that element in defining both the contact person and publisher of the dataset:
HF_address <- list( deliveryPoint = "324 North Main Street", city = "Petersham", administrativeArea = "MA", postalCode = "01366", country = "USA")
publisher <- list( organizationName = "Harvard Forest", address = HF_address)
contact <- list( individualName = aaron$individualName, electronicMailAddress = aaron$electronicMailAddress, address = HF_address, organizationName = "Harvard Forest", phone = "000-000-0000")
keywordSet is just a list of lists. Note that everything is a list.
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") ))
Lastly, some of the elements needed for
eml object can simply be given as text strings.
pubDate <- "2012" title <- "Thresholds and Tipping Points in a Sarracenia Microecosystem at Harvard Forest since 2012" abstract <- "The primary goal of this project is to determine experimentally the amount of lead time required to prevent a state change. To achieve this goal, we will (1) experimentally induce state changes in a natural aquatic ecosystem - the Sarracenia microecosystem; (2) use proteomic analysis to identify potential indicators of states and state changes; and (3) test whether we can forestall state changes by experimentally intervening in the system. This work uses state-of-the art molecular tools to identify early warning indicators in the field of aerobic to anaerobic state changes driven by nutrient enrichment in an aquatic ecosystem. The study tests two general hypotheses: (1) proteomic biomarkers can function as reliable indicators of impending state changes and may give early warning before increasing variances and statistical flickering of monitored variables; and (2) well-timed intervention based on proteomic biomarkers can avert future state changes in ecological systems." intellectualRights <- "This dataset is released to the public and may be freely downloaded. Please keep the designated Contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement. For more information on LTER Network data access and use policies, please see: http://www.lternet.edu/data/netpolicy.html."
Many of these text fields can instead be read in from an external file that has richer formatting, such as we did with the
set_methods() step. Any text field containing a slot named
section can import text data from a MS Word
.docx file, markdown file, or other file format recognized by Pandoc into that element. For instance, here we import the same paragraph of text shown above for
abstract from an external file (this time, a markdown-formatted file) instead:
abstract_file <- system.file("examples/hf205-abstract.md", package = "EML") abstract <- set_TextType(abstract_file)
We are now ready to add each of these elements we have created so far into our
dataset element, like so:
dataset <- list( title = title, creator = aaron, pubDate = pubDate, intellectualRights = intellectualRights, abstract = abstract, associatedParty = associatedParty, keywordSet = keywordSet, coverage = coverage, contact = contact, methods = methods, dataTable = dataTable)
dataset in place, we are ready to declare our root
eml element. In addition to our
dataset element we have already built, all we need is a packageId code and the system on which it is based. Here we have generated a unique id using the standard
uuid algorithm, which is available in the R package
eml <- list( packageId = uuid::UUIDgenerate(), system = "uuid", # type of identifier dataset = dataset)
eml object fully constructed in R, we can now check that it is valid, conforming to all criteria set forth in the EML Schema. This will ensure that other researchers and other software can readily parse and understand the contents of our metadata file:
##  TRUE ## attr(,"errors") ## character(0)
The validator returns a status
0 to indicate success. Otherwise, the first error message encountered will be displayed. The most common reason for an error is probably the omission of a required metadata field.
To take the greatest advantage of EML, we should consider depositing our file in a Metacat-enabled repository, which we discuss in the next vignette on using EML with data repositories.