diff --git a/src/download_griis_checklist.R b/src/download_griis_checklist.R
index f0563c5..58af7d6 100644
--- a/src/download_griis_checklist.R
+++ b/src/download_griis_checklist.R
@@ -1,4 +1,5 @@
 library(knitr)
+library(magrittr)
 
 # create temporary R file ####
 tempR <- tempfile(fileext = ".R")
@@ -14,7 +15,7 @@ file.copy(from = "./data/interim/data_input_checklist_indicators.tsv",
 file.remove("./data/interim/data_input_checklist_indicators.tsv")
 
 # add vernicular names
-checklist_raw <- read_delim("data/output/UAT_processing/data_input_checklist_indicators.tsv", 
+checklist_raw <- readr::read_delim("data/output/UAT_processing/data_input_checklist_indicators.tsv", 
                             delim = "\t", escape_double = FALSE, 
                             trim_ws = TRUE)
 
@@ -29,19 +30,19 @@ all_vernicular_names <- data.frame(
 )
 
 for(t in taxon_keys){
-  temp_name_usage <- name_usage(key = t, data = "vernacularNames")
+  temp_name_usage <- rgbif::name_usage(key = t, data = "vernacularNames")
   spec_vernicular_names <- temp_name_usage$data 
   
   if(nrow(spec_vernicular_names) > 0){
     spec_vernicular_names <- spec_vernicular_names %>% 
-      filter(language %in% c("eng", "fra", "nld")) %>% 
-      mutate(vernacularName = str_to_sentence(vernacularName),
-             language = str_sub(language, 0, 2)) %>% 
-      distinct(taxonKey, language, vernacularName) %>% 
-      group_by(language, taxonKey) %>% 
-      summarise(vernacular_name = paste(vernacularName, collapse = ", ")) %>% 
-      ungroup() %>% 
-      pivot_wider(id_cols = taxonKey,
+      dplyr::filter(language %in% c("eng", "fra", "nld")) %>% 
+      dplyr::mutate(vernacularName = stringr::str_to_sentence(vernacularName),
+             language = stringr::str_sub(language, 0, 2)) %>% 
+      dplyr::distinct(taxonKey, language, vernacularName) %>% 
+      dplyr::group_by(language, taxonKey) %>% 
+      dplyr::summarise(vernacular_name = paste(vernacularName, collapse = ", ")) %>% 
+      dplyr::ungroup() %>% 
+      tidyr::pivot_wider(id_cols = taxonKey,
                   names_from = language,
                   names_prefix = "vernacular_name_",
                   values_from = vernacular_name)
@@ -49,15 +50,31 @@ for(t in taxon_keys){
     if(nrow(all_vernicular_names) == 0){
       all_vernicular_names <- spec_vernicular_names
     }else{
-      all_vernicular_names <- bind_rows(all_vernicular_names, spec_vernicular_names)
+      all_vernicular_names <- dplyr::bind_rows(all_vernicular_names, spec_vernicular_names)
     }
   }else{
     warning(paste0("No vernicular names for ", t))
   }
 }
 
+missing_scientific_names <- checklist_raw %>% 
+  dplyr::filter(is.na(species)) 
+
+if(nrow(missing_scientific_names) > 0){
+  checklist_raw <- checklist_raw %>% 
+    dplyr::mutate(species = dplyr::case_when(is.na(species) ~ canonicalName,
+                                      TRUE ~ species))
+  
+  missing_scientific_names_recheck <- checklist_raw %>% 
+    dplyr::filter(is.na(species)) 
+  
+  if(nrow(missing_scientific_names_recheck) > 0){
+    stop(paste0("After the fix these taxa are still missing their scientific names: ", paste(missing_scientific_names_recheck$nubKey, collapse = ", ")))
+  }
+}
+
 checklist <- checklist_raw %>% 
-  left_join(all_vernicular_names, by = c("nubKey" = "taxonKey")) %>% 
-  write_delim("data/output/UAT_processing/data_input_checklist_indicators.tsv", 
+  dplyr::left_join(all_vernicular_names, by = c("nubKey" = "taxonKey")) %>% 
+  readr::write_delim("data/output/UAT_processing/data_input_checklist_indicators.tsv", 
               delim = "\t")