try({
  ## Example 01: retrieve data in TXT format
  dami_query_string <- "Damiano Fantini[AU]"
  dami_on_pubmed <- get_pubmed_ids(dami_query_string)
  dami_papers <- fetch_pubmed_data(dami_on_pubmed, format = "abstract")
  dami_papers[dami_papers == ""] <- "\n"
  cat(paste(dami_papers[1:65], collapse = ""))
  #
}, silent = TRUE)

## Not run: 
## Example 02: retrieve data in XML format
library("easyPubMed")
dami_query_string <- "Damiano Fantini[AU] AND 2018[PDAT]"
dami_on_pubmed <- get_pubmed_ids(dami_query_string)
dami_papers <- fetch_pubmed_data(dami_on_pubmed)
titles <- sapply(dami_papers, custom_grep, tag = "ArticleTitle", format = "char", USE.NAMES = FALSE)
print(titles)
#
## Example 03: retrieve data from PubMed and save as XML file
ml_query <- "Machine Learning[TI] AND 2016[PD]"
out1 <- batch_pubmed_download(pubmed_query_string = ml_query, batch_size = 180)
x <- paste(readLines(out1[1], n = 10), collapse = "\n")
cat(x)
#
## Example 04: retrieve data from PubMed and save as TXT file
ml_query <- "Machine Learning[TI] AND 2016[PD]"
out2 <- batch_pubmed_download(pubmed_query_string = ml_query, batch_size = 180, format = "medline")
x <- paste(readLines(out1[1], n = 30), collapse = "\n")
cat(x)
#
## Example 05: extract information from a single PubMed record 
ml_query <- "Machine Learning[TI] AND 2016[PD]"
out3 <- batch_pubmed_download(pubmed_query_string = ml_query, batch_size = 180)
PM_data <- articles_to_list(out3[1])
PM_record_df <- article_to_df(PM_data[[80]])
print(PM_record_df[1,])
print(PM_record_df[,"address"])
#
## Example 06: query PubMed and extract information from multiple records in one step 
ml_query <- "Machine Learning[TI] AND 2016[PD]"
out4 <- batch_pubmed_download(pubmed_query_string = ml_query, batch_size = 180)
PM_tab <- table_articles_byAuth(out4[1], autofill = TRUE, included_authors = "last")
PM_tab$address <- substr(PM_tab$address, 1, 12)
PM_tab[50:70,c("pmid", "jabbrv", "year", "lastname", "address")]

## End(Not run)