Performs an (imperfect) conversion of dataspice metadata to EML. It's
very likely you will get validation errors and need to fix them afterwards
but spice_to_eml is a good way to a richer metadata schema (EML) when
you're already using dataspice but need a richer metadata schema.
Usage
spice_to_eml(spice = file.path("data", "metadata", "dataspice.json"))Examples
# Load an example dataspice JSON that comes installed with the package
spice <- system.file(
"examples", "annual-escapement.json",
package = "dataspice"
)
# And crosswalk it to EML
spice_to_eml(spice)
#> Warning: variableMeasured not crosswalked to EML because we don't have enough information. Use `crosswalk_variables` to create the start of an EML attributes table. See ?crosswalk_variables for help.
#> You might want to run EML::eml_validate on the result at this point and fix what validations errors are produced. You will commonly need to set `packageId`, `system`, and provide `attributeList` elements for each `dataTable`.
#> dataset:
#> title: Compiled annual statewide Alaskan salmon escapement counts, 1921-2017
#> creator:
#> - individualName:
#> givenName: Jeanette
#> surName: Clark
#> organizationName:
#> - Organization
#> electronicMailAddress: jclark@nceas.ucsb.edu
#> userId: ~
#> - individualName:
#> givenName: Rich
#> surName: Brenner
#> organizationName:
#> - Organization
#> electronicMailAddress: richard.brenner.alaska.gov
#> userId: ~
#> abstract: The number of mature salmon migrating from the marine environment to freshwater
#> streams is defined as escapement. Escapement data are the enumeration of these
#> migrating fish as they pass upstream, and are a widely used index of spawning
#> salmon abundance. These data are important for fisheries management, since most
#> salmon harvest occurs near the mouths of rivers where salmon spawn during this
#> migration. Escapement data are collected in a variety of ways. Stationary projects
#> utilize observers stationed along freshwater corridors who count salmon as they
#> pass upriver through weirs or past elevated towers. Sonar equipment placed in
#> the river can also give a stationary escapement count. These counts usually represent
#> a sample, and are expanded to represent a 24h period. Escapement data can also
#> be collected using aerial surveys, where observers in an aircraft provide an index
#> to estimate escapement. In general, escapement counts do not represent total abundance,
#> but instead an index of abundance. Surveys are usually timed to coincide with
#> peak spawning activity, generally in the summer, but in the case of Coho salmon
#> in the fall as well. Some data about non-salmon species are also included. This
#> dataset contains compiled annual data from multiple sources. The .Rmd merges all
#> datasets, identifies and flags duplicate records, and performs quality assurance
#> checks by filtering and graphing results.
#> pubDate: ~
#> coverage:
#> temporalCoverage:
#> rangeOfDates:
#> beginDate:
#> calendarDate: '1921-01-01'
#> time: '08:00:00'
#> endDate:
#> calendarDate: '1921-01-01'
#> time: '08:00:00'
#> geographicCoverage:
#> geographicDescription: Placeholder
#> boundingCoordinates:
#> westBoundingCoordinate: '-171'
#> eastBoundingCoordinate: '-131'
#> northBoundingCoordinate: '78'
#> southBoundingCoordinate: '47'
#> contact:
#> - individualName:
#> givenName: Jeanette
#> surName: Clark
#> organizationName:
#> - Organization
#> electronicMailAddress: jclark@nceas.ucsb.edu
#> userId: ~
#> - individualName:
#> givenName: Rich
#> surName: Brenner
#> organizationName:
#> - Organization
#> electronicMailAddress: richard.brenner.alaska.gov
#> userId: ~
#> dataTable:
#> - entityName: StockInfo.csv
#> physical:
#> objectName: StockInfo.csv
#> dataFormat:
#> externallyDefinedFormat:
#> formatName: CSV
#> distribution:
#> online:
#> url: http://example.com
#> - entityName: BroodTables.csv
#> physical:
#> objectName: BroodTables.csv
#> dataFormat:
#> externallyDefinedFormat:
#> formatName: CSV
#> distribution:
#> online:
#> url: http://example.com
#> - entityName: SourceInfo.csv
#> physical:
#> objectName: SourceInfo.csv
#> dataFormat:
#> externallyDefinedFormat:
#> formatName: CSV
#> distribution:
#> online:
#> url: http://example.com
# We can also create dataspice metadata from scratch and crosswalk it to EML
myspice <- list(
name = "My example spice",
creator = "Me",
contact = "Me"
)
spice_to_eml(myspice)
#> Warning: Failed to crosswalk creator Me because it needs a type.
#> Warning: Failed to crosswalk creator Me because it needs a type.
#> dataset:
#> title: My example spice
#> creator:
#> - []
#> abstract: ~
#> pubDate: ~
#> coverage:
#> temporalCoverage: []
#> geographicCoverage: []
#> contact:
#> - []
#> dataTable: []