Bundling Gupta et al. 2016 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:

Gupta, Sandipan & Dubey, Sourabh & Trivedi, Raman & Chand, Bimal & Banerjee, Samir. (2016). Indigenous ornamental freshwater ichthyofauna of the Sundarban Biosphere Reserve, India: Status and prospects. Journal of Threatened Taxa. 8. 9144.

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 <- "the_sundarbans"
dataset_dir_name <- "Gupta_et_al_2016"
original_pdf <- "ojsadmin,+188826viii169144-9154.pdf"
short_name <- "indian-sundarbans-gupta-2016"

Parsing PDF table to CSV

The data for this reference is formatted as an text-based table inside a PDF across multiple sheets. First, we use pdf_to_table to 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 109.754,325.542,754.884,463.199 -p 7",
  "-a 111.986,88.175,748.932,219.136 -p 7",
  "-a 88.175,85.199,689.404,219.136 -p 8"
)

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

# 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: 2023-09-12 12:37:26"                                                                                                  
##  [4] "------------------------------"                                                                                                      
##  [5] ""                                                                                                                                    
##  [6] "PDF input: ../../../datasets/the_sundarbans/Gupta_et_al_2016/raw/ojsadmin,+188826viii169144-9154.pdf"                                
##  [7] "Perform Table Parsing: TRUE"                                                                                                         
##  [8] "Selected Areas:"                                                                                                                     
##  [9] "  Area 1: [109.754, 325.542, 754.884, 463.199]"                                                                                      
## [10] "  Area 2: [111.986, 88.175, 748.932, 219.136]"                                                                                       
## [11] "  Area 3: [88.175, 85.199, 689.404, 219.136]"                                                                                        
## [12] "Pages: 7, 7, 8"                                                                                                                      
## [13] "Concatenate: True"                                                                                                                   
## [14] "Concatenate across headers: False"                                                                                                   
## [15] "Stream Extraction: True"                                                                                                             
## [16] "Lattice Extraction: False"                                                                                                           
## [17] ""                                                                                                                                    
## [18] "Parsing Tables"                                                                                                                      
## [19] "------------------------------"                                                                                                      
## [20] ""                                                                                                                                    
## [21] ""                                                                                                                                    
## [22] "Saving to CSV"                                                                                                                       
## [23] "CSV file: ../../../datasets/the_sundarbans/Gupta_et_al_2016/processed/ojsadmin,+188826viii169144-9154_tables_parsed_concatenated.csv"
## [24] "------------------------------"                                                                                                      
## [25] ""                                                                                                                                    
## [26] ""                                                                                                                                    
## [27] "Run Details: ../../../datasets/the_sundarbans/Gupta_et_al_2016/processed/ojsadmin,+188826viii169144-9154_parameters.txt"             
## [28] "Finished"                                                                                                                            
## [29] ""

Read source data

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

processed_csv <- "ojsadmin,+188826viii169144-9154_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))

Order..Family.and.scientific.name
Perciformes: Nandidae
Nandus nandus (Hamilton, 1822)
Badidae
Badis badis (Hamilton, 1822)
Channidae
Channa barca (Hamilton, 1822)

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()                         # Have sensible (lowercase) column names

# Remove lines with colons, lines with only one word and lines that start with "("
cleaned_data <- input_data %>%
  filter(!str_detect(order_family_and_scientific_name, ":"),
         str_count(order_family_and_scientific_name, "\\S+") > 1,
         !str_starts(order_family_and_scientific_name, "^\\("))

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

order_family_and_scientific_name
Nandus nandus (Hamilton, 1822)
Badis badis (Hamilton, 1822)
Channa barca (Hamilton, 1822)
Channa marulius (Hamilton, 1822)
Channa gachua
Channa punctata (Bloch, 1793)

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
Nandus nandus (Hamilton, 1822)	Nandus nandus	281633
Badis badis (Hamilton, 1822)	Badis badis	NA
Channa barca (Hamilton, 1822)	Channa barca	NA
Channa marulius (Hamilton, 1822)	Channa marulius	NA
Channa gachua	Channa gachua	NA
Channa punctata (Bloch, 1793)	Channa punctata	280129

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('Badis badis', 1026697)
combined_dataframe[3,2:3] = c('Channa barca', 1026847)
combined_dataframe[4,2:3] = c('Channa marulius', 1026844)
combined_dataframe[5,2:3] = c('Channa gachua', 743378)
combined_dataframe[21,2:3] = c('Chitala chitala', 1026796)
combined_dataframe[26,2:3] = c('Amblypharyngodon mola', 1012854)
combined_dataframe[27,2:3] = c('Bengala elanga', 1534503)
combined_dataframe[30,2:3] = c('Cirrhinus mrigala', 314143)
combined_dataframe[31,2:3] = c('Danio rerio', 1026595)
combined_dataframe[32,2:3] = c('Devario devario', 1026569)
combined_dataframe[34,2:3] = c('Labeo bata', 1022798)
combined_dataframe[38,2:3] = c('Puntius chola', 1026489)
combined_dataframe[39,2:3] = c('Pethia conchonius', 991302)
combined_dataframe[40,2:3] = c('Pethia gelius', 1022796)
combined_dataframe[41,2:3] = c('Pethia phutunio', 1026592)
combined_dataframe[44,2:3] = c('Puntius terio', 1026600)
combined_dataframe[46,2:3] = c('Salmostoma acinaces', 1020821)
combined_dataframe[48,2:3] = c('Salmostoma phulo', 1026591)
combined_dataframe[49,2:3] = c('Securicula gora', 957049)
combined_dataframe[56,2:3] = c('Trichogaster fasciata', 315877)
combined_dataframe[57,2:3] = c('Trichogaster lalius', 1027008)
combined_dataframe[58,2:3] = c('Trichogaster chuna', 1022769)
combined_dataframe[62,2:3] = c('Mystus tengara', 1022781)
combined_dataframe[67,2:3] = c('Ompok pabda', 1026982)
combined_dataframe[78,2:3] = c('Glyptothorax telchitta', 1022776)
combined_dataframe[79,2:3] = c('Gogangra viridescens', 1020257)
combined_dataframe[81,2:3] = c('Clarias magur', 314146)
combined_dataframe[84,2:3] = c('Microphis deocata', 1022775)

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()

#Sundarbans National Park
ind_shape <- shapes_processed$geom[which(shapes_processed$name == "Sundarbans National Park")]


#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 <- "India"
occurrence %<>% mutate(country)

locality

locality <- "Indian Sundarbans"
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: 84 × 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 74 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 <- "Indigenous ornamental freshwater ichthyofauna of the Sundarban Biosphere Reserve, India: status and prospects: Fishes Checklist"

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

#Abstract
abstract <- eml$abstract(
  para = "Ornamental fishes are the most popular pet throughout the world and figh demand for these fishes has made them an important component of the world fish trade. India contrfibutes a very meager percentage to the world ornamental fish trade; but considering the high ichthyofaunal diversity it has the potential to compete with the world’s leading ornamental fish producers in the near future. Sundarban Biosphere Reserve has abundant waterbodies with rich fish diversity. Although some research has been carried out on ichthyofaunal resources off the Sundarban; detailed documentafion on freshwater indigenous ornamental ichthyofaunal resources off this region is sill not available. To fill this knowledge gap, the present study has been conducted to list the indigenous ornamental ichthyofaunal resources of the Sundarban Biosphere Reserve along with their conservation status and their prospective utilization for improved livelihood of local communities. Eighty four species belonging to 11 orders, 28 families and 59 genera were collected from the study area with species representing the order Cypriniformes dominating the ichthyofauna. Nine species have been listed as Near Threatened in the IUCN Red List of Threatened Species. Indigenous fish species of the Sundarban having great potential to support domestic as well as the international ornamental fish trade from India in near future. The ornamental fish species would also be able to generate alternate livelihood options for the impecunious communities of the Sundarban. However, serious concern must also be paid to the conservation of these fish species as some of them are under near threatened categories of IUCN Red list.")

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 = "Sandipan", 
      surName = "Gupta"),
    organizationName = "University of Calcutta"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Sourabh Kumarr", 
      surName = "Dubey"),
    organizationName = "West Bengal University of Animal & Fishery Science"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Raman Kumar", 
      surName = "Trivedi"),
    organizationName = "West Bengal University of Animal & Fishery Science"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Bimal Kinkar", 
      surName = "Chand"),
    organizationName = "West Bengal University of Animal & Fishery Science"
  ), eml$creator(
    individualName = eml$individualName(
      givenName = "Samir", 
      surName = "Banerjee"),
    organizationName = "University of Calcutta"
  )
)

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 = "Gupta S, Dubey S, Trivedi R, Chand B, & Banerjee S. (2016). Indigenous ornamental freshwater ichthyofauna of the Sundarban Biosphere Reserve, India: Status and prospects. Journal of Threatened Taxa. 8. 9144.")
    )
  )
)

citationdoi <- "https://doi.org/10.11609/jott.1888.8.9.9144-9154"

Coverage

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

#Coverage
coverage <- eml$coverage(
  geographicCoverage = eml$geographicCoverage(
    geographicDescription = "Indian Sundarbans",
    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$beginDate(
        calendarDate = "2011-01-01"
      ),
      endDate = eml$endDate(
        calendarDate = "2012-12-31"
      )
    )
  )
)

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 <- "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)
}