diff --git a/visualize_catalog_content.qmd b/visualize_catalog_content.qmd
index 78065c1..2f3641a 100644
--- a/visualize_catalog_content.qmd
+++ b/visualize_catalog_content.qmd
@@ -1,6 +1,5 @@
 ---
-title: "What is in HTR-United Catalog?"
-author: "Alix Chagué"
+title: "Plotting HTR-United's catalog"
 format:
   html:
     code-fold: true
@@ -9,6 +8,10 @@ format:
     toc-title: "Contents"
     toc-location: left
     theme: lumen
+    other-links:
+      - text: HTR-United's website
+        href: https://htr-united.github.io
+    code-links: repo
 project:
   type: default
   output-dir: ./computed
@@ -16,11 +19,15 @@ jupyter: python3
 ---
 
 
-# HTR-United
+This page offers an overview of the latest content of the [HTR-United catalog](http://htr-united.github.io). The visualizations are normally updated frequently. Feel free to check that the HTR-United's catalog version listed below does indeed correspond to the latest version available for the catalog ([here](https://github.com/HTR-United/htr-united)).
 
-```txt
-TODO: Paragraphs introducing HTR-United in a few words.
-```
+### What is HTR-United?
+
+HTR-United is a catalog that lists highly documented training datasets used for automatic transcription or segmentation models. HTR-United standardizes dataset descriptions using a schema, offers guidelines for organizing data repositories, and provides tools for quality control and continuous documentation. It's an open and transparent ecosystem hosted on GitHub, designed for easy maintenance. HTR-United was created to help projects quickly access diverse ground truth data for training models on smaller collections.
+
+### What is shown here?
+
+This page is only dedicated to a generic oversight of the content of the catalog, mainly in the form of plots. If you want to browse the datasets listed in the catalog, there is a more suitable interface for that [here](https://htr-united.github.io/catalog.html).
 
 <!-- library import -->
 ```{python}
@@ -101,7 +108,7 @@ if r.status_code == 200:
             # now let's download the yaml file
             with open("htr-united.yml", "w", encoding="utf8") as fh:
                 fh.write(r_yml.text)  
-            print("We are currently computing the content of HTR-United's catalog", htr_united_version)
+            print("We are currently computing \nthe content of HTR-United's \ncatalog", htr_united_version)
 
         else:
             print("Couldn't connect to", github_url, "got status code", r_yml.status_code)
@@ -122,6 +129,8 @@ if os.path.exists(yaml_file_path):
 ## Language coverage
 
 ```{python}
+#| fig-cap: "A bar-plot figuring the different languages covered in HTR-United's catalog"
+
 languages = []
 for entry in data:
     if entry.get("language"):
@@ -140,6 +149,8 @@ make_bar_plot(counted_lgges, title='Language Distribution', xlabel="Languages")
 ## Script coverage
 
 ```{python}
+#| fig-cap: "A bar-plot figuring the different writing systems (scripts) covered in HTR-United's catalog"
+
 scripts_dict = []
 for entry in data:
     if entry.get("script"):
@@ -159,19 +170,23 @@ for cs in counted_scripts.most_common(5):
 make_bar_plot(counted_scripts, title='Script Distribution', xlabel="Scripts")
 ```
 
-## Script type coverage
+## Writing type type coverage
 
 ```{python}
+#| fig-cap: "A bar-plot figuring the different writing categories covered in HTR-United's catalog ('script-type')"
+
 script_types = [entry.get("script-type") for entry in data if entry.get("script-type")]
 counted_script_types = Counter(script_types)
 
 pprint(counted_script_types)
-make_bar_plot(counted_script_types, title='Script type Distribution', xlabel="Script Type")
+make_bar_plot(counted_script_types, title='Writing type Distribution', xlabel="Writing Type")
 ```
 
 ## Software variety
 
 ```{python}
+#| fig-cap: "A bar-plot figuring the different software used to create the datasets listed in HTR-United's catalog"
+
 softwares = [entry.get("production-software") for entry in data if entry.get("production-software")]
 counted_softwares = Counter(softwares)