diff --git a/vignettes/articles/phytools.Rmd b/vignettes/articles/phytools.Rmd
index 76c5aa7..c15b989 100644
--- a/vignettes/articles/phytools.Rmd
+++ b/vignettes/articles/phytools.Rmd
@@ -180,7 +180,7 @@ m3 <- m3[tree$tip.label,]
 
 
 ```{r}
-pruned_tree <- ape::keep.tip(phy = tree, tip = rownames(m1))
+# pruned_tree <- ape::keep.tip(phy = tree, tip = rownames(m1))
 ```
 
 
diff --git a/vignettes/articles/propagation_workflow.Rmd b/vignettes/articles/propagation_workflow.Rmd
deleted file mode 100644
index a4f6bb2..0000000
--- a/vignettes/articles/propagation_workflow.Rmd
+++ /dev/null
@@ -1,174 +0,0 @@
----
-title: "Propagation workflow for categorical/logical attributes"
-output:
-    html_document:
-        toc: true
----
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  message = FALSE
-)
-```
-
-```{r setup, message=FALSE}
-library(taxPPro)
-library(bugphyzz)
-library(data.tree)
-library(dplyr)
-library(purrr)
-```
-
-# Import bugphyzz data
-
-Import a bugphyzz physiology
-
-Below, we need to normalize the score so that the total score of all
-attributes per taxon is equal to 1:
-
-```{r import bugphyzz data, message=FALSE}
-phys <- physiologies('aerophilicity', full_source = FALSE)[[1]] |> 
-  prepareDataForPropagation() |> 
-  mergeOriginalAndEarlyASR() |> 
-  group_by(NCBI_ID) |>
-  mutate(Score = Score / sum(Score)) |> # Normalize scores
-  ungroup() |> 
-  distinct()
-```
-
-# Create input table
-
-This is the table with data that will be mapped to the data.tree object.
-
-```{r}
-input_tbl <- phys |>
-  select(NCBI_ID, Attribute, Score, Evidence) |>
-  distinct() |> 
-  tidyr::complete(
-    ## Note that Evidence is empty, not NA
-    NCBI_ID, Attribute, fill = list(Score = 0, Evidence = '')
-)
-```
-
-# Import tree from taxPPro package
-
-```{r}
-data("tree_list")
-tree <- as.Node(tree_list)
-tree$totalCount
-```
-
-# Map bugphyzz annotations to the nodes in the tree
-
-```{r tree mapping}
-l <- split(input_tbl, factor(input_tbl$NCBI_ID))
-tree$Do(function(node) {
-    if (!is.null(l[[node$name]])) {
-        node[['table']] <- l[[node$name]]
-    }
-})
-```
-
-```{r, include=FALSE, echo=FALSE, eval=FALSE}
-tree$d__2$p__1224$c__1236$o__91347$f__543$g__561$s__562$table
-tree$d__2$p__1224$c__1236$o__91347$f__543$g__561$table <- l[['g__561']]
-tree$d__2$p__1224$c__1236$o__91347$f__543$g__561$table
-tree$d__2$p__1224$c__1236$o__91347$f__543$g__561$table <- NULL
-tree$d__2$p__1224$c__1236$o__91347$f__543$g__561$s__1182732$table
-```
-
-# Propagate annotations
-
-```{r propagation}
-tree$Do(asr, traversal = 'post-order')
-tree$Do(inh, traversal = 'pre-order')
-```
-
-# Final table
-
-Step1. Convert the data.tree object to a data.frame:
-
-```{r}
-data_tree_tbl <- tree$Get(function(node) node[['table']], simplify = FALSE) |> 
-    purrr::discard(~ all(is.na(.x))) |>
-    dplyr::bind_rows() |> 
-    relocate(NCBI_ID) 
-dim(data_tree_tbl)
-```
-
-Step 2. Some nodes were might have been lost above. We need to recover them from the
-tree:
-
-```{r}
-attrs <- unique(phys$Attribute)
-all_node_names <- tree$Get(function(node) node$name, simplify = FALSE) |> 
-  unlist(recursive = TRUE, use.names = FALSE) |> 
-  unique()
-missing_nodes <- all_node_names[which(!all_node_names %in% unique(data_tree_tbl$NCBI_ID))]
-if (length(missing_nodes) > 0) {
-  empty_df <- data.frame(
-    NCBI_ID = sort(rep(missing_nodes, length(attrs))), 
-    Attribute = rep(attrs, length(missong_nodes)), 
-    Score = 0,
-    Evidence = NA
-  )
-  inferred_values <- bind_rows(data_tree_tbl, empty_df)
-} else {
-  inferred_values <- data_tree_tbl  
-}
-dim(inferred_values)
-```
-
-Step 3. Let's merge the data from steps 1 and 2, and the original data in 
-bugphyzz
-
-```{r}
-other_ids <- phys$NCBI_ID[which(!phys$NCBI_ID %in% inferred_values$NCBI_ID)]
-other_ids <- unique(other_ids)
-other_phys <- filter(phys, NCBI_ID %in% other_ids)
-final_table <- bind_rows(inferred_values, other_phys)
-dim(final_table)
-```
-
-# Check if all scores add up to 1 or 0 per taxon
-
-```{r}
-final_table |> 
-  group_by(NCBI_ID) |> 
-  summarise(score = sum(Score)) |> 
-  ungroup() |> 
-  pull(score) |> 
-  table()
-```
-
-
-# Clean the tree
-
-This is much faster than loading the tree every time with a function call:
-
-```{r}
-tree$Do(function(node) {
-    node[['table']] <- NULL
-})
-```
-
-# Export table
-
-```{r}
-# write.table(
-#   x = final_table, file = 'propagated_data.tsv', sep = '\t', quote = TRUE
-# )
-fname <- "full_dump.csv.bz2"
-unlink(fname)
-con <- bzfile(fname, "w")
-write.csv(final_table, file = con, quote = TRUE, row.names = FALSE)
-close(con)
-```
-
-# Session information
-
-```{r}
-sessioninfo::session_info()
-```