diff --git a/vignettes/getting-started.Rmd b/vignettes/scoringutils.Rmd
similarity index 96%
rename from vignettes/getting-started.Rmd
rename to vignettes/scoringutils.Rmd
index c8207df17..1b3d5e72a 100644
--- a/vignettes/getting-started.Rmd
+++ b/vignettes/scoringutils.Rmd
@@ -88,13 +88,15 @@ You can also visualise forecasts directly using the `plot_predictions()` functio
 
 ```{r, fig.width = 9, fig.height = 6}
 example_quantile %>%
-  make_NA(what = "truth",
-          target_end_date >= "2021-07-15",
-          target_end_date < "2021-05-22"
+  make_NA(
+    what = "truth",
+    target_end_date >= "2021-07-15",
+    target_end_date < "2021-05-22"
   ) %>%
-  make_NA(what = "forecast",
-          model != "EuroCOVIDhub-ensemble",
-          forecast_date != "2021-06-28"
+  make_NA(
+    what = "forecast",
+    model != "EuroCOVIDhub-ensemble",
+    forecast_date != "2021-06-28"
   ) %>%
   plot_predictions(
     x = "target_end_date",
@@ -183,7 +185,7 @@ We can also summarise one particular metric across different categories using a
 score(example_continuous) %>%
   summarise_scores(by = c("model", "location", "target_type")) %>%
   plot_heatmap(x = "location", metric = "bias") +
-    facet_wrap(~ target_type)
+  facet_wrap(~ target_type)
 ```
 
 ### Weighted interval score components
@@ -312,11 +314,11 @@ example_quantile %>%
   facet_wrap(~ target_type, scales = "free")
 ```
 
-## Tips and tricks - converting to sample-based forecasts
+## Converting to quantile-based forecasts
 
-Different metrics are available for different forecasting formats. In some cases, you may for example have forecasts in a sample-based format, but wish to make use of some of the functionality only available to quantile-based forecasts. For example, you may want to use the decomposition of the weighted interval score, or may like to compute interval coverage values. 
+Different metrics are available for different forecasting formats. In some cases, you may for example have forecasts in a sample-based format, but wish to make use of some of the functionality only available to quantile-based forecasts. For example, you may want to use the decomposition of the weighted interval score, or may like to compute interval coverage values.
 
-You can convert your forecasts into a sample-based format using the function `sample_to_quantile()`. There is, however, one caveat: Quantiles will be calculated based on the predictive samples, which may introduce a bias if the number of available samples is small. 
+You can convert your sample-based forecasts into a quantile-based format using the function `sample_to_quantile()`. There is, however, one caveat: Quantiles will be calculated based on the predictive samples, which may introduce a bias if the number of available samples is small.
 
 ```{r}
 example_integer %>%