Bundling Villela et al. 2003 to a DwC Archive

This is an R Markdown Notebook for converting the species checklist found in the following reference to DarwinCore format for upload into OBIS as part of UNESCO’s eDNA Expeditions project:

Villela, P.C. & Sale, Peter & Gold-Bouchot, Gerardo & Kjerfve, Björn. (2003). MESOAMERICAN BARRIER REEF SYSTEMS PROJECT: MANUAL OF METHODS FOR THE MBRS SYNOPTIC MONITORING PROGRAM. Selected Methods for Monitoring Physical and Biological Parameters for Use in the Mesoamerican Region.

Setup

Call the necessary libraries and variables. Suppresses loading messages.

library(magrittr)                       # To use %<>% pipes
suppressMessages(library(janitor))      # To clean input data
suppressMessages(library(dplyr))        # To clean input data
library(stringr)                        # To clean input data
suppressMessages(library(rgnparser))    # To clean species names
suppressMessages(library(taxize))       # To get WoRMS IDs
library(worrms)                         # To get WoRMS IDs
library(digest)                         # To generate hashes
suppressMessages(library(obistools))    # To generate centroid lat/long and uncertainty
suppressMessages(library(sf))           # To generate wkt polygon
suppressMessages(library(EML))          # To create eml.xml file
library(xml2)                           # To create the meta.xml file
suppressMessages(library(zip))          # To zip DwC file

Input Parameters and Paths

path_to_project_root <- "../../.."
site_dir_name <- "belize_barrier_reef_reserve_system"
dataset_dir_name <- "Villela_et_al_2003"
original_pdf <- ""
short_name <- "belize-barrier-reef-villela-2003"

Parsing PDF table to CSV

The data for this reference is formatted as an image-based table inside a PDF across multiple sheets. First, we use pdf_to_table to OCR and parse out the table to a CSV. We don’t have to do this since we have the raw data.

Read source data

Now we’ll read in the csv table outputted from the previous step

processed_csv <- "BBRS_Species_List.csv"

input_data <- read.csv(paste(path_to_project_root, "datasets", site_dir_name, dataset_dir_name, "raw", processed_csv, sep="/"))

#to preview pretty table
knitr::kable(head(input_data))

Group	Common.Name	Scientific.Name	Confirmed.species.presence.per.reserve	X	X.1	X.2
			BCMR	SWCMR	GRMR	SCMR
Angelfish	Queen angelfish	Holacanthus cilaris	x	x	x	x
	Blue angelfish	Holacanthus bermudensis	x	x	x	x
	French angelfish	Pomacanthus paru	x	x	x	x
	Gray angelfish	Pomacanthus arcuatus	x	x	x	x
	Rock beauty	Holacanthus tricolor	x	x	x	x

Preprocessing

Here we tidy the data up, since OCR and table parsing errors are common and only take the list of species, since this is a checklist.

Tidy Data

input_data %<>%
  remove_empty(c("rows", "cols")) %>%       # Remove empty rows and columns
  clean_names()

# Remove Classes, Families and Orders and take first column only
cleaned_data <- input_data %>%
     select(c(scientific_name))

#to preview pretty table
knitr::kable(head(cleaned_data))

scientific_name

Holacanthus cilaris
Holacanthus bermudensis
Pomacanthus paru
Pomacanthus arcuatus
Holacanthus tricolor

Get WoRMS IDs

Auto matching

First we will try to do this automatically by first cleaning the species names using gnparser and then using the taxise library to call the WoRMS database.

#Parse author names out
parsed_names <- rgnparser::gn_parse(cleaned_data[,])

#Function to get WoRMS IDs. Search for accepted names first and if not found, search for unaccepted. If still not found, use the worrms package to search.
get_worms_id_from_element <- function(element) {
  worms_id <- get_wormsid(element$canonical$full, searchtype="scientific", fuzzy=TRUE, messages = FALSE, accepted = TRUE)
  if (attr(worms_id, "match") == "not found") {
    worms_id <- get_wormsid(element$canonical$full, searchtype="scientific", messages = FALSE, fuzzy=TRUE)
    if (attr(worms_id, "match") == "not found") {
      worms_id <- NA
    }
  }
  return(worms_id)
}

#Call the function
worms_ids <- lapply(parsed_names, function(element) {
  if (element$parsed) {
    return(get_worms_id_from_element(element))
  } else {
    return(NA)
  }
})

#combine original names, parsed data and WoRMS ID into one data frame
combined_dataframe <- data.frame()

for (i in 1:nrow(cleaned_data)) {
  cleaned_value <- cleaned_data[i,]
  canonical_value <- parsed_names[[i]]$canonical$full
  worms_id_value <- worms_ids[[i]][1]
  if (is.null(canonical_value)){
    canonical_value <- NA
  }
  temp_row <- data.frame(CleanedData = cleaned_value, CanonicalFull = canonical_value, WormsIDs = worms_id_value)
  combined_dataframe <- rbind(combined_dataframe, temp_row)
}

knitr::kable(head(combined_dataframe))

CleanedData	CanonicalFull	WormsIDs
	NA	NA
Holacanthus cilaris	Holacanthus cilaris	NA
Holacanthus bermudensis	Holacanthus bermudensis	159284
Pomacanthus paru	Pomacanthus paru	276025
Pomacanthus arcuatus	Pomacanthus arcuatus	159287
Holacanthus tricolor	Holacanthus tricolor	276017

Human Verification

Sometimes there are misspellings in the original text or incorrect OCR that can be searched for and fixed by hand. To do this, view the combined dataframe, search for unmatched species in WoRMS and add the ID, and remove rows that were not autoremoved in the earlier cleaning steps

combined_dataframe[2,2:3] = c('Holacanthus ciliaris', 276012)
combined_dataframe[11,2:3] = c('Prognathodes aculeatus', 273369)
combined_dataframe[12,2:3] = c('Acanthurus coeruleus', 159581)
combined_dataframe[13,2:3] = c('Acanthurus bahianus', 159578)
combined_dataframe[14,2:3] = c('Acanthurus chirurgus', 159580)
combined_dataframe[26,2:3] = c('Hemiramphus brasiliensis', 159279)
combined_dataframe[99,2:3] = c('Halichoeres bivittatus', 158815)
combined_dataframe[100,2:3] = c('Halichoeres maculipinna', 275772)
combined_dataframe[104,2:3] = c('Halichoeres radiatus', 275793)
combined_dataframe[107,2:3] = c('Myripristis jacobus', 159385)
combined_dataframe[116,2:3] = c('Gnatholepis thompsoni', 277493)
combined_dataframe[121,2:3] = c('Malacanthus plumieri', 277261)
combined_dataframe[136,2:3] = c('Canthidermis sufflamen', 127399)
combined_dataframe[137,2:3] = c('Sanopus greenfieldorum', 282659)
combined_dataframe[139,2:3] = c('Cantherhines pullus', 159493)
combined_dataframe[140,2:3] = c('Canthidermis macrolepis', 275237)
combined_dataframe[167,2:3] = c('Equetus punctatus', 276097)

combined_dataframe <- combined_dataframe[-c(1, 157),]

Darwin Core mapping

Required Terms

OBIS currently has eight required DwC terms: scientificName, scientificNameID, occurrenceID, eventDate, decimalLongitude, decimalLatitude, occurrenceStatus, basisOfRecord.

scientificName/scientificNameID

Create a dataframe with unique taxa only (though this should already be unique). This will be our primary DarwinCore data frame.

#rename and restructure WoRMSIDs to OBIS requirements
occurrence <- combined_dataframe %>%
  distinct(CanonicalFull, WormsIDs) %>%
  rename(scientificName = CanonicalFull) %>%
  rename(scientificNameID = WormsIDs) %>%
  mutate(scientificNameID = ifelse(!is.na(scientificNameID), paste("urn:lsid:marinespecies.org:taxname:", scientificNameID, sep = ""), NA))

occurrenceID

OccurrenceID is an identifier for the occurrence record and should be persistent and globally unique. It is a combination of dataset-shortname:occurrence: and a hash based on the scientific name.

# Vectorize the digest function (The digest() function isn't vectorized. So if you pass in a vector, you get one value for the whole vector rather than a digest for each element of the vector):
vdigest <- Vectorize(digest)

# Generate taxonID:
occurrence %<>% mutate(occurrenceID = paste(short_name, "occurrence", vdigest (scientificName, algo="md5"), sep=":"))

eventDate

This is NULL since this is technically a checklist and we do not know the collection date.

eventDate <- ""
occurrence %<>% mutate(eventDate)

decimalLongitude/decimalLatitude

Use obistools::calculate_centroid to calculate a centroid and radius for WKT strings. This is useful for populating decimalLongitude, decimalLatitude and coordinateUncertaintyInMeters. The WKT strings are from https://github.com/iobis/mwhs-shapes.

if (!file.exists(paste(path_to_project_root, "scripts_data/marine_world_heritage.gpkg", sep="/"))) {
  download.file("https://github.com/iobis/mwhs-shapes/blob/master/output/marine_world_heritage.gpkg?raw=true", paste(path_to_project_root, "scripts_data/marine_world_heritage.gpkg", sep="/"))
}

shapes <- st_read(paste(path_to_project_root, "scripts_data/marine_world_heritage.gpkg", sep="/"))

## Reading layer `marine_world_heritage' from data source 
##   `/mnt/c/Users/Chandra Earl/Desktop/Labs/UNESCO/mwhs-data-mobilization/scripts_data/marine_world_heritage.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 60 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -55.32282 xmax: 180 ymax: 71.81381
## Geodetic CRS:  4326

#For some sites, the GeoPackage has core as well as buffer areas. Merge the geometries by site.
shapes_processed <- shapes %>%
  group_by(name) %>%
  summarize()

#Belize Barrier Reef Reserve System
ind_shape <- shapes_processed$geom[which(shapes_processed$name == "Belize Barrier Reef Reserve System")]


#convert shape to WKT
wkt <- st_as_text(ind_shape, digits = 6)

localities <- calculate_centroid(wkt)

occurrence %<>% mutate(decimalLatitude = localities$decimalLatitude)
occurrence %<>% mutate(decimalLongitude = localities$decimalLongitude)

occurrenceStatus

occurrenceStatus <- "present"
occurrence %<>% mutate(occurrenceStatus)

basisOfRecord

basisOfRecord <- "HumanObservation"
occurrence %<>% mutate(basisOfRecord)

Extra Terms

footprintWKT

occurrence %<>% mutate(footprintWKT = wkt)

coordinateUncertaintyInMeters

occurrence %<>% mutate(coordinateUncertaintyInMeters = localities$coordinateUncertaintyInMeters)

geodeticDatum

geodeticDatum <- "WGS84"
occurrence %<>% mutate(geodeticDatum)

country

country <- "Belize"
occurrence %<>% mutate(country)

locality

locality <- "Belize Barrier Reef Reserve"
occurrence %<>% mutate(locality)

Post-processing

Check data

Use the check_fields command from obistools to check if all OBIS required fields are present in an occurrence table and if any values are missing.

#Reorganize columns
occurrence = occurrence %>% select(occurrenceID, scientificName, scientificNameID, eventDate, country, locality, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, footprintWKT, geodeticDatum, occurrenceStatus, basisOfRecord)

#Check fields
check_fields(occurrence)

## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## ℹ Please use `tibble()` instead.
## ℹ The deprecated feature was likely used in the obistools package.
##   Please report the issue to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## # A tibble: 165 × 4
##    level field       row message                                 
##    <chr> <chr>     <int> <chr>                                   
##  1 error eventDate     1 Empty value for required field eventDate
##  2 error eventDate     2 Empty value for required field eventDate
##  3 error eventDate     3 Empty value for required field eventDate
##  4 error eventDate     4 Empty value for required field eventDate
##  5 error eventDate     5 Empty value for required field eventDate
##  6 error eventDate     6 Empty value for required field eventDate
##  7 error eventDate     7 Empty value for required field eventDate
##  8 error eventDate     8 Empty value for required field eventDate
##  9 error eventDate     9 Empty value for required field eventDate
## 10 error eventDate    10 Empty value for required field eventDate
## # ℹ 155 more rows

Create the EML file

This is a file which contains the dataset’s metadata and is required in a DarwinCore-Archive.

emld::eml_version("eml-2.1.1")

## [1] "eml-2.1.1"

#Title
title <- "Selected Methods for Monitoring Physical and Biological Parameters for Use in the Mesoamerican Region: Fishes Checklist"

#AlternateIdentifier
alternateIdentifier <- paste("https://ipt.obis.org/secretariat/resource?r=", short_name, sep="")

#Abstract
abstract <- eml$abstract(
  para = "The Mesoamerican Barrier Reef System (MBRS), extending from Isla Contoy on the north of the Yucatan Peninsula to the Bay Islands of Honduras, includes the second longest barrier reef in the world. It is approximately 1,000 km long and spans over four countries and two trans-boundary areas: Chetumal Bay, between Belize and Mexico; and the Gulf of Honduras, between Belize, Guatemala and Honduras. The MBRS is unique in the Western Hemisphere due to its length, composition of reef types, and diverse assemblage of corals and related species. The MBRS contributes to the stabilization and protection of coastal landscapes, maintenance of coastal water quality, and serves as breeding and feeding grounds for marine mammals, reptiles, fish and invertebrates, many of which are of commercial importance. The MBRS is also of immense socioeconomic significance providing employment and a source of income to an estimated one million people living in adjacent coastal areas."
)

People

Here we add the people involved in the project:

The creator is the person or organization responsible for creating the resource itself.

The contact is the person or institution to contact with questions about the use, interpretation of a data set.

The metadataProvider is the person responsible for providing the metadata documentation for the resource.

The associatedParty (in this case the Data Curator) is the person who mobilized the data from the original resource.

creator <- list(eml$creator(
    individualName = eml$individualName(
      givenName = "P.C.", 
      surName = "Villela"),
    organizationName = ""
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Peter", 
      surName = "Sale"),
    organizationName = ""
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Gerardo", 
      surName = "Gold-Bouchot"),
    organizationName = ""
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Björn", 
      surName = "Kjerfve"),
    organizationName = ""
  )
)


contact <- eml$creator(
  individualName = eml$individualName(
    givenName = "OBIS", 
    surName = "Secretariat"),
  electronicMailAddress = "helpdesk@obis.org",
  organizationName = "OBIS",
  positionName = "Secretariat"
)

metadataProvider <- eml$metadataProvider(
  individualName = eml$individualName(
    givenName = "Chandra", 
    surName = "Earl"),
  electronicMailAddress = "c.earl@unesco.org",
  organizationName = "UNESCO",
  positionName = "eDNA Scientific Officer"
)

associatedParty <- eml$associatedParty(
  role = "processor",
  individualName = eml$individualName(
    givenName = "Chandra", 
    surName = "Earl"),
  electronicMailAddress = "c.earl@unesco.org",
  organizationName = "UNESCO",
  positionName = "eDNA Scientific Officer"
)

Additional Metadata

Here we add the additionalMetadata element, which is required for a GBIF-type EML file and contains information such as the citation of the dataset, the citation of the original resource and the creation timestamp of the EML.

#{dataset.authors} ({dataset.pubDate}) {dataset.title}. [Version {dataset.version}]. {organization.title}. {dataset.type} Dataset {dataset.doi}, {dataset.url}

additionalMetadata <- eml$additionalMetadata(
  metadata = list(
    gbif = list(
      dateStamp = paste0(format(Sys.time(), "%Y-%m-%dT%H:%M:%OS3"), paste0(substr(format(Sys.time(), "%z"), 1, 3), ":", paste0(substr(format(Sys.time(), "%z"), 4, 5)))),
      hierarchyLevel = "dataset",
      citation = "IPT will autogenerate this",
      bibliography = list(
        citation = "Villela, P.C. & Sale, Peter & Gold-Bouchot, Gerardo & Kjerfve, Björn. (2003). MESOAMERICAN BARRIER REEF SYSTEMS PROJECT: MANUAL OF METHODS FOR THE MBRS SYNOPTIC MONITORING PROGRAM. Selected Methods for Monitoring Physical and Biological Parameters for Use in the Mesoamerican Region.")
    )
  )
)

citationdoi <- ""

Coverage

Here we describe the dataset’s geographic, taxonomic and temporal coverage.

#Coverage
coverage <- eml$coverage(
  geographicCoverage = eml$geographicCoverage(
    geographicDescription = "Belize Barrier Reef Reserve",
    boundingCoordinates = eml$boundingCoordinates(
      westBoundingCoordinate = st_bbox(ind_shape)$xmax,
      eastBoundingCoordinate = st_bbox(ind_shape)$xmin,
      northBoundingCoordinate = st_bbox(ind_shape)$ymax,
      southBoundingCoordinate = st_bbox(ind_shape)$ymin)
    ),
  taxonomicCoverage = eml$taxonomicCoverage(
    generalTaxonomicCoverage = "Fishes",
    taxonomicClassification = list(
      eml$taxonomicClassification(
        taxonRankName = "Superclass",
        taxonRankValue = "Agnatha"),
      eml$taxonomicClassification(
        taxonRankName = "unranked",
        taxonRankValue = "Chondrichthyes"),
      eml$taxonomicClassification(
        taxonRankName = "unranked",
        taxonRankValue = "Osteichthyes")
      )
    
#  ),
#  temporalCoverage = eml$temporalCoverage(
#    rangeOfDates = eml$rangeOfDates(
#      beginDate = eml$beginDate(
#        calendarDate = "2019-05-01"
#      ),
#      endDate = eml$endDate(
#        calendarDate = "2016-05-06"
#      )
#    )
   )
)

Extra MetaData

These fields are not required, though they make the metadata more complete.

methods <- eml$methods(
  methodStep = eml$methodStep(
    description = eml$description(
      para = paste("See Github <a href=\"https://github.com/iobis/mwhs-data-mobilization\">Project</a> and <a href=\"https://iobis.github.io/mwhs-data-mobilization/notebooks/", site_dir_name, "/", dataset_dir_name, "\"> R Notebook</a> for dataset construction methods", sep="")
    )
  )
)

#Other Data
pubDate <- "2023-10-15"

#language of original document
language <- "eng"

keywordSet <- eml$keywordSet(
  keyword = "Occurrence",
  keywordThesaurus = "GBIF Dataset Type Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_type_2015-07-10.xml"
)

maintenance <- eml$maintenance(
  description = eml$description(
    para = ""),
  maintenanceUpdateFrequency = "notPlanned"
)

#Universal CC
intellectualRights <- eml$intellectualRights(
  para = "To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the <ulink url=\"http://creativecommons.org/publicdomain/zero/1.0/legalcode\"><citetitle>Public Domain (CC0 1.0)</citetitle></ulink>. Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction."
)


purpose <- eml$purpose(
  para = "These data were made accessible through UNESCO's eDNA Expeditions project to mobilize available marine species and occurrence datasets from World Heritage Sites."
)

additionalInfo <- eml$additionalInfo(
  para = "marine, harvested by iOBIS"
)

Create and Validate EML

#Put it all together
my_eml <- eml$eml(
           packageId = paste("https://ipt.obis.org/secretariat/resource?id=", short_name, "/v1.0", sep = ""),  
           system = "http://gbif.org",
           scope = "system",
           dataset = eml$dataset(
               alternateIdentifier = alternateIdentifier,
               title = title,
               creator = creator,
               metadataProvider = metadataProvider,
               associatedParty = associatedParty,
               pubDate = pubDate,
               coverage = coverage,
               language = language,
               abstract = abstract,
               keywordSet = keywordSet,
               contact = contact,
               methods = methods,
               intellectualRights = intellectualRights,
               purpose = purpose,
               maintenance = maintenance,
               additionalInfo = additionalInfo),
           additionalMetadata = additionalMetadata
)

eml_validate(my_eml)

## [1] TRUE
## attr(,"errors")
## character(0)

Create meta.xml file

This is a file which describes the archive and data file structure and is required in a DarwinCore-Archive. It is based on the template file “meta_occurrence_checklist_template.xml”

meta_template <- paste(path_to_project_root, "scripts_data/meta_occurrence_checklist_template.xml", sep="/")
meta <- read_xml(meta_template)

fields <- xml_find_all(meta, "//d1:field")

for (field in fields) {
  term <- xml_attr(field, "term")
  if (term == "http://rs.tdwg.org/dwc/terms/eventDate") {
    xml_set_attr(field, "default", eventDate)
  } else if (term == "http://rs.tdwg.org/dwc/terms/country") {
    xml_set_attr(field, "default", country)
  } else if (term == "http://rs.tdwg.org/dwc/terms/locality") {
    xml_set_attr(field, "default", locality)
  } else if (term == "http://rs.tdwg.org/dwc/terms/decimalLatitude") {
    xml_set_attr(field, "default", localities$decimalLatitude)
  } else if (term == "http://rs.tdwg.org/dwc/terms/decimalLongitude") {
    xml_set_attr(field, "default", localities$decimalLongitude)
  } else if (term == "http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters") {
    xml_set_attr(field, "default", localities$coordinateUncertaintyInMeters)
  } else if (term == "http://rs.tdwg.org/dwc/terms/footprintWKT") {
    xml_set_attr(field, "default", wkt)
  } else if (term == "http://rs.tdwg.org/dwc/terms/geodeticDatum") {
    xml_set_attr(field, "default", geodeticDatum)
  } else if (term == "http://rs.tdwg.org/dwc/terms/occurrenceStatus") {
    xml_set_attr(field, "default", occurrenceStatus)
  } else if (term == "http://rs.tdwg.org/dwc/terms/basisOfRecord") {
    xml_set_attr(field, "default", basisOfRecord)
  }
}

Save outputs

dwc_output_dir <- paste(path_to_project_root, "output", site_dir_name, dataset_dir_name, sep="/")

write.csv(occurrence, paste(dwc_output_dir, "/occurrence.csv", sep = ""), na = "", row.names=FALSE)
write_xml(meta, file = paste(dwc_output_dir, "/meta.xml", sep = ""))
write_eml(my_eml, paste(dwc_output_dir, "/eml.xml", sep = ""))

Edit EML

We have to further edit the eml file to conform to GBIF-specific requirements that cannot be included in the original EML construction. This includes changing the schemaLocation and rearranging the GBIF element, since the construction automatically arranges the children nodes to alphabetical order.

#edit the schemaLocation and rearrange gbif node for gbif specific eml file
eml_content <- read_xml(paste(dwc_output_dir, "/eml.xml", sep = ""))

#change schemaLocation attributes for GBIF
root_node <- xml_root(eml_content)
xml_set_attr(root_node, "xsi:schemaLocation", "https://eml.ecoinformatics.org/eml-2.1.1 http://rs.gbif.org/schema/eml-gbif-profile/1.2/eml.xsd")
xml_set_attr(root_node, "xmlns:dc", "http://purl.org/dc/terms/")
xml_set_attr(root_node, "xmlns:stmml", NULL)
xml_set_attr(root_node, "xml:lang", "eng")


#rearrange children nodes under the GBIF element
hierarchyLevel <- eml_content %>% xml_find_all(".//hierarchyLevel")
dateStamp <- eml_content %>% xml_find_all(".//dateStamp")
citation <- eml_content %>% xml_find_all("./additionalMetadata/metadata/gbif/citation")
bibcitation <- eml_content %>% xml_find_all("./additionalMetadata/metadata/gbif/bibliography/citation")
xml_set_attr(bibcitation, "identifier", citationdoi)

eml_content %>% xml_find_all(".//hierarchyLevel") %>% xml_remove()
eml_content %>% xml_find_all(".//dateStamp") %>% xml_remove()
eml_content %>% xml_find_all("./additionalMetadata/metadata/gbif/citation") %>% xml_remove()
eml_content %>% xml_find_all(".//gbif") %>% xml_add_child(citation, .where=0)
eml_content %>% xml_find_all(".//gbif") %>% xml_add_child(hierarchyLevel, .where=0)
eml_content %>% xml_find_all(".//gbif") %>% xml_add_child(dateStamp, .where=0)

write_xml(eml_content, paste(dwc_output_dir, "/eml.xml", sep = ""))

Zip files to DwC-A

output_zip <- paste(dwc_output_dir, "DwC-A.zip", sep="/")

if (file.exists(output_zip)) {
  unlink(output_zip)
}

file_paths <- list.files(dwc_output_dir, full.names = TRUE)
zip(zipfile = output_zip, files = file_paths, mode = "cherry-pick")

if (file.exists(output_zip)) {
  unlink(file_paths)
}