diff --git a/R/prepare_response_variables.R b/R/prepare_response_variables.R
index 84165c9..d8f63eb 100644
--- a/R/prepare_response_variables.R
+++ b/R/prepare_response_variables.R
@@ -23,7 +23,7 @@ prepare_response_variables <- function(ManyEcoEvo,
   if (!is.null(dataset_standardise)) {
     stopifnot(is.character(dataset_standardise))
     stopifnot(length(dataset_standardise) >= 1)
-    stopifnot(length(dataset_standardise) == length(unique(ManyEcoEvo$dataset)))
+    stopifnot(length(dataset_standardise) <= length(unique(ManyEcoEvo$dataset)))
     match.arg(dataset_standardise, choices = ManyEcoEvo$dataset, several.ok = TRUE)
   }
   
@@ -84,10 +84,6 @@ prepare_response_variables <- function(ManyEcoEvo,
       ))
   } else {
     
-    process_response <- function(dat){
-      dat #TODO replace dummy function with actual function
-    }
-    
     datasets_to_standardise <- tibble(
       dataset = dataset_standardise,
       fns = list(standardise_response)
@@ -100,12 +96,12 @@ prepare_response_variables <- function(ManyEcoEvo,
     out <- out %>%
       ungroup() %>%
       left_join(datasets_to_standardise, by = "dataset") %>% 
-      mutate(fns = coalesce(fns, list(process_response)),
-             data = pmap(.l = ., 
+      mutate(fns = coalesce(fns, list(process_response))) %>%
+      mutate(data = pmap(.l = ., 
                          .f = pmap_prepare_response, 
                          estimate_type = estimate_type, 
                          param_table = param_table)) %>% 
-      select(-fns)
+      select(-fns) #TODO drop ci cols or not??
   }
   return(out)
 }
diff --git a/R/standardise_response.R b/R/standardise_response.R
index e34e815..be8edf7 100644
--- a/R/standardise_response.R
+++ b/R/standardise_response.R
@@ -16,6 +16,8 @@ NULL
 #' Standardise Response Variable
 #' @return A tibble of analyst data with standardised values contained in a list-column called 'back_transformed_data'
 #' @details
+#' # `standardise_response()`
+#'
 #' When the `estimate_type` is `"Zr"`, [standardise_response()] standardises 
 #' effect-sizes with [est_to_zr()], assuming that the `beta_estimate` and 
 #' `beta_SE` values have already been back-transformed to the appropriate scale. #TODO check this.
@@ -120,10 +122,18 @@ standardise_response <- function(dat,
 #' @description
 #' This function generates the response data for meta-analysis without standardising the effect sizes / out-of-sample predictions.
 #' @describeIn process_analyst_data Process response data for meta-analysis but do not standardise effect-sizes
-process_response <- function(dat,
-                             estimate_type = NULL,
-                             param_table = NULL,
-                             dataset = NULL){ #TODO what to do about args in pmap_prepare_response? allow ... args in fns(x,y,z, ...)?
+#' @details
+#' # `process_response()`
+#' 
+#' Formats tibbles in the list-column `back_transformed_data` to ensure that the 
+#' correct columns are present for meta-analysis, matching the outputs of
+#'  [standardise_response()]. For blue tit data `dat$back_transformed_data$fit` 
+#'  and for eucalyptus data, `dat$back_transformed_data$estimate` is renamed `Z`. 
+#'  `se.fit` is renamed `VZ`.
+#' @import dplyr
+#' @import purrr
+#' @import tidyr
+process_response <- function(dat, ...){
   
   Z_names_lookup <- c(Z = "estimate", #blue tit
                       Z = "fit", #eucalyptus
@@ -135,5 +145,4 @@ process_response <- function(dat,
                  rename, 
                  any_of(Z_names_lookup)), 
            params = NA)
-   #TODO replace dummy function with actual function
 }