diff --git a/DESCRIPTION b/DESCRIPTION
index 7da8e9d..2906e60 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: tidync
 Title: A Tidy Approach to 'NetCDF' Data Exploration and Extraction
-Version: 0.3.0.9002
+Version: 0.4.0
 Authors@R: c(person("Michael", "Sumner", email = "mdsumner@gmail.com", role = c("aut", "cre")), 
              person("Simon", "Wotherspoon", role = "ctb"), 
              person("Tomas", "Remenyi", role = "ctb"), 
@@ -9,7 +9,8 @@ Authors@R: c(person("Michael", "Sumner", email = "mdsumner@gmail.com", role = c(
              person("Tim", "Lucas", role = "ctb"), 
              person("Hadley", "Wickham", role = "ctb"), 
              person("Adrian", "Odenweller", role = "ctb"), 
-             person("Patrick", "Van Laake", role = "ctb"))
+             person("Patrick", "Van Laake", role = "ctb"), 
+             person("Fabian", "Bernhard", role = "ctb"))
 Description: Tidy tools for 'NetCDF' data sources. Explore the contents of a 
  'NetCDF' source (file or URL) presented as variables organized by grid with a 
  database-like interface. The hyper_filter() interactive function translates the 
@@ -31,8 +32,8 @@ Imports:
     rlang,
     tibble,
     tidyr,
-    CFtime (>= 1.2.0)
-RoxygenNote: 7.2.3
+    CFtime (>= 1.4.0)
+RoxygenNote: 7.3.2
 Suggests: ggplot2,
     knitr,
     rmarkdown,
diff --git a/R/hyper_tibble.R b/R/hyper_tibble.R
index 0730fa4..2c4be2f 100644
--- a/R/hyper_tibble.R
+++ b/R/hyper_tibble.R
@@ -59,12 +59,20 @@ hyper_tibble.tidync<- function(x, ..., na.rm = TRUE, force = FALSE) {
   out <- tibble::as_tibble(lapply(slabs, as.vector))
   
   prod_dims <- 1
-  dn <- dimnames(slabs[[1]])
-  nm <- names(dn)
+  trans <- attr(slabs, "transforms")
 
-  for (i in seq_along(nm)) {
-    out[[nm[i]]] <- rep(dn[[i]], each = prod_dims, length.out = total_prod)
-    prod_dims <- prod_dims * length(dn[[i]]) 
+  for (i in seq_along(trans)) {
+    nm <- names(trans)[i]
+    nr <- sum(trans[[i]]$selected)
+    
+    out[[nm]] <- if ("timestamp" %in% colnames(trans[[i]]))
+      rep(dplyr::filter(trans[[nm]], .data$selected)[["timestamp"]],
+          each = prod_dims, length.out = total_prod)
+    else
+      rep(dplyr::filter(trans[[nm]], .data$selected)[[nm]], 
+          each = prod_dims, length.out = total_prod)
+
+    prod_dims <- prod_dims * nr
   }
   if (na.rm) out <- dplyr::filter(out, !all_na)
  out