Bundling Nunez et al. 2010 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:

Núñez MA, I Torres, J Garcia, A. Averza (2010) Comparación de la diversidad y abundancia de peces en los esteros Boca Grande y Río San Juan en el PN Coiba, Provincia de Veraguas, Republica de Panama. Tecnociencia.

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 <- "coiba_national_park_and_its_special_zone_of_marine_protection"
dataset_dir_name <- "Nunez_et_al_2010"
original_pdf <- "tecnociencia.pdf"
short_name <- "coiba-nunez-2010"

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.

#conda environment
condaenv <- "mwhs-data-mobilization"

# Path to the Python script
script <- paste(path_to_project_root, "scripts_data/pdf_to_tables/pdf_to_table.py", sep="/")

# Input PDF file path
input_pdf <- paste(path_to_project_root, "datasets", site_dir_name, dataset_dir_name, "raw", original_pdf, sep="/")

# Output directory for OCR/table files
output_dir <- paste(path_to_project_root, "datasets", site_dir_name, dataset_dir_name, "processed", sep="/")

# Define page numbers and table areas (see documentation)
page_args <- c(
  "-a 154.913,325.508,659.048,476.213 -p 7",
  "-a 114.368,330.098,258.953,478.508 -p 8"
)

# Define run parameters (see documentation)
run_parameters <- "-l -c -nh"

# Combine page arguments and execute
page_args_combined <- paste(page_args, collapse = " ")
command <- paste("conda run -n", condaenv, "python", script, "-i", input_pdf, run_parameters, page_args_combined, "-o", output_dir)
system(command, intern=TRUE)
##  [1] ""                                                                                                                                                                
##  [2] "Script Execution Summary"                                                                                                                                        
##  [3] "Date and Time: 2023-10-03 14:24:16"                                                                                                                              
##  [4] "------------------------------"                                                                                                                                  
##  [5] ""                                                                                                                                                                
##  [6] "PDF input: ../../../datasets/coiba_national_park_and_its_special_zone_of_marine_protection/Nunez_et_al_2010/raw/tecnociencia.pdf"                                
##  [7] "Perform Table Parsing: TRUE"                                                                                                                                     
##  [8] "Selected Areas:"                                                                                                                                                 
##  [9] "  Area 1: [154.913, 325.508, 659.048, 476.213]"                                                                                                                  
## [10] "  Area 2: [114.368, 330.098, 258.953, 478.508]"                                                                                                                  
## [11] "Pages: 7, 8"                                                                                                                                                     
## [12] "Concatenate: True"                                                                                                                                               
## [13] "Concatenate across headers: True"                                                                                                                                
## [14] "Stream Extraction: False"                                                                                                                                        
## [15] "Lattice Extraction: True"                                                                                                                                        
## [16] ""                                                                                                                                                                
## [17] "Parsing Tables"                                                                                                                                                  
## [18] "------------------------------"                                                                                                                                  
## [19] ""                                                                                                                                                                
## [20] ""                                                                                                                                                                
## [21] "Saving to CSV"                                                                                                                                                   
## [22] "CSV file: ../../../datasets/coiba_national_park_and_its_special_zone_of_marine_protection/Nunez_et_al_2010/processed/tecnociencia_tables_parsed_concatenated.csv"
## [23] "------------------------------"                                                                                                                                  
## [24] ""                                                                                                                                                                
## [25] ""                                                                                                                                                                
## [26] "Run Details: ../../../datasets/coiba_national_park_and_its_special_zone_of_marine_protection/Nunez_et_al_2010/processed/tecnociencia_parameters.txt"             
## [27] "Finished"                                                                                                                                                        
## [28] ""

Read source data

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

processed_csv <- "tecnociencia_tables_parsed_concatenated.csv"

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

#to preview pretty table
knitr::kable(head(input_data))
X0 X1 X2
Especies BG RSJ
SECUNDARIO
Oxyzygonectes dovii *
Poeciliopsis elongata *
PERIFERALES
Ctenogobius sugittula *

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

#change header row
names(input_data) <- input_data[1,]
input_data <- input_data[-1,]
input_data[37, 2] <- ""

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 %>%              
  filter(str_count(especies, "\\S+") > 1)

#to preview pretty table
knitr::kable(head(cleaned_data))
especies bg rsj
Oxyzygonectes dovii *
Poeciliopsis elongata *
Ctenogobius sugittula *
Bathygobius andrei *
Bathygobius lineatus *
Bathygobius ramosus *

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[,1])

#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.especies CleanedData.bg CleanedData.rsj CanonicalFull WormsIDs
Oxyzygonectes dovii * Oxyzygonectes dovii 281963
Poeciliopsis elongata * Poeciliopsis elongata 282304
Ctenogobius sugittula * Ctenogobius sugittula NA
Bathygobius andrei * Bathygobius andrei 277620
Bathygobius lineatus * Bathygobius lineatus 277629
Bathygobius ramosus * Bathygobius ramosus 277636

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[3,4:5] = c("Ctenogobius sagittula", 276489)
combined_dataframe[7,4:5] = c("Awaous transandeanus", 310701)
combined_dataframe[17,4:5] = c("Caranx caballus", 273271)
combined_dataframe[18,4:5] = c("Caranx caninus", 273272)
combined_dataframe[24,4:5] = c("Lutjanus novemfasciatus", 276544)
combined_dataframe[27,4:5] = c("Mugil cephalus", 126983)
combined_dataframe[28,4:5] = c("Haemulon sexfasciatum", 275736)
combined_dataframe[31,4:5] = c("Bairdiella ensifera", 276070)
combined_dataframe[33,4:5] = c("Stellifer oscitans", 276166)
combined_dataframe[37,4:5] = c("Pseudophallus starksii", 1026989)
combined_dataframe[39,4:5] = c("Lile stolifera", 281353)
combined_dataframe[43,4:5] = c("Sphoeroides annulatus", 275272)

Locality data

Locality data was included in the paper, namely:

La estación #1 el estero Boca Grande localizada en la coordenadas 07º 22’ 25” de latitud Norte y 81º 40’ 06” de longitud Oeste La estación #2 el estero Río San Juan ubicado en las coordenadas 07º 27’ 57” de latitud Norte y 81º 43’ 59” de longitud Oeste

However, these coordinates are either incorrect or recorded in a datum that is not WGS84 (points are on land and do not match the given map). Therefore, the map was georeferenced and these coordinates were taken instead.

occ_data <- data.frame(
  canonicalFull = character(),
  wormsIDs = numeric(),
  locality = character(),
  fieldNumber = character(),
  decimalLongitude = numeric(),
  decimalLatitude = numeric(),
  coordinateUncertaintyInMeters = numeric()
)

for (i in 1:nrow(combined_dataframe)) {
  if (combined_dataframe[i, 2] == "*") {
    fieldNumber = "BG"
    locality = "Estero Boca Grande"
    decimalLatitude = "7.3382317"
    decimalLongitude = "-81.5963628"
    coordinateUncertaintyInMeters = "50"
  } else if (combined_dataframe[i, 3] == "*"){
    fieldNumber = "RSJ"
    locality = "Estero Rio San Juan"
    decimalLatitude = "7.4631989"
    decimalLongitude = "-81.6998092"
    coordinateUncertaintyInMeters = "50"
  }
    
  new_row <- data.frame(
    canonicalFull = combined_dataframe[i, "CanonicalFull"],
    wormsIDs = combined_dataframe[i, "WormsIDs"],
    locality = locality,
    fieldNumber = fieldNumber,
    decimalLongitude = decimalLongitude,
    decimalLatitude = decimalLatitude,
    coordinateUncertaintyInMeters = coordinateUncertaintyInMeters
  )
  occ_data <- rbind(occ_data, new_row)
}

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 <- occ_data %>%
  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

These specimens were collected between June 2003 - December 2003

eventDate <- "2003-06-01/2003-12-31"
occurrence %<>% mutate(eventDate)

decimalLongitude/decimalLatitude

Locality data was retrieved via georeferencing the included site maps from the paper. These maps have been saved as TIFs and points saved as a csv. First we will use obistools::calculate_centroid to calculate a centroid and radius for WKT strings. This is useful for populating decimalLongitude, decimalLatitude and coordinateUncertaintyInMeters. See above.

The calculations below are used to calculate the boundaries for the EML file.

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()

#Coiba National Park and its Special Zone of Marine Protection
ind_shape <- shapes_processed$geom[which(shapes_processed$name == "Coiba National Park and its Special Zone of Marine Protection")]

occurrenceStatus

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

basisOfRecord

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

Extra Terms

geodeticDatum

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

country

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

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, fieldNumber, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, 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: 0 × 0

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 <- "Comparación de la diversidad y abundancia de peces en los esteros Boca Grande y Río San Juan en el PN Coiba, Provincia de Veraguas, Republica de Panama."

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

#Abstract
abstract <- eml$abstract(
  para = "De junio a diciembre de 2003, se realizaron seis muestreos ictiológicos en los esteros Boca Grande y río San Juan en el Parque Nacional Coiba, Provincia de Veraguas, con el objetivo de comparar la diversidad y abundancia de las especies de peces existentes. Se determinaron los parámetros ambientales que los regulan. Los datos obtenidos indican que estos esteros insulares, constituyen áreas de crianza de juveniles de diferentes especies de peces comerciales. Se capturaron un total de 582 peces, 290 en el estero Boca Grande y 292 en el estero río San Juan. Se reportaron un total de 6 ordenes, correspondientes a 20 familias, incluidas en 30 géneros y 44 especies, distribuidas de la siguiente manera: 18 familias, 24 géneros y 30 especies para el estero Boca Grande y para el estero río San Juan un total de 15 familias, 21 géneros y 29 especies. Quince especies fueron comunes para ambos esteros con un 59% de especies consideradas de interés comercial y un 90% de especies juveniles. En el estero Boca Grande la especie más abundante fue Atherinella argentea y en el estero Río San Juan fue Lile stolifera."
)

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 = "Marcos", 
      surName = "Núñez"),
    organizationName = "Universidad de Panamá"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Iritza", 
      surName = "Torres"),
    organizationName = "Universidad de Panamá"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Jorge", 
      surName = "García"),
    organizationName = "Universidad de Panamá"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Aramís", 
      surName = "Averza"),
    organizationName = "Universidad de Panamá"
  )
)

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 = "Núñez MA, I Torres, J Garcia, A. Averza (2010) Comparación de la diversidad y abundancia de peces en los esteros Boca Grande y Río San Juan en el PN Coiba, Provincia de Veraguas, Republica de Panama. Tecnociencia.")
    )
  )
)

citationdoi <- "https://revistas.up.ac.pa/index.php/tecnociencia/article/view/874/743"

Coverage

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

#Coverage
coverage <- eml$coverage(
  geographicCoverage = eml$geographicCoverage(
    geographicDescription = "Coiba National Park and its Special Zone of Marine Protection",
    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$beginDate(
        calendarDate = "2003-06-01"
      ),
      endDate = eml$endDate(
        calendarDate = "2003-12-31"
      )
    )
   )
)

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 <- "esp"

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_occurrence_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/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)
}