diff --git a/journal/Readme.md b/journal/Readme.md
index 79ebc0d35d7052f45d4947da1722ab3e7dd0324c..14dd76a6d26ef7db8cb9b5e1008061595fe49639 100644
--- a/journal/Readme.md
+++ b/journal/Readme.md
@@ -63,7 +63,8 @@ Next, I practiced using **Matplotlib** to create two types of plots:
 2. **Histogram**: A graphical representation of the distribution of the dataset.
 
 Code used (Python):
-```python
+```
+python
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -75,4 +76,36 @@ plt.show()
 plt.figure()
 plt.hist(data, color='blue', edgecolor='black')
 plt.show()
+```
 
+
+# Extended Analysis of Réseau Sentinelles Data (Module 3)
+
+## Overview
+
+We had to do exercises for two diseases—**influenza-like illness (ILI)** and **chickenpox (varicella)**—using weekly incidence data from the French public health surveillance system, **Réseau Sentinelles**. The focus was to learn how to import and analyse data.
+
+
+## 1. Working with Local Data Copies
+
+To ensure robustness and reproducibility:
+
+- **Large downloads are cached locally** (only once), preserving the original URL for traceability.
+- Dataset examples:
+  - ILI: `incidence-PAY-3.csv`
+  - Chickenpox: `incidence-PAY-1.csv`
+
+This setup avoids broken links and version drift in automated analyses.
+
+## 2. Seasonal Aggregation of ILI and Chickenpox
+
+Both datasets follow this standard pipeline:
+
+1. **Load** the CSV, skip comment rows.
+2. **Parse** the `'week'` column into ISO-week periods.
+3. **Clean** by dropping missing values.
+4. **Index** the data by weekly periods and **sort** chronologically.
+5. **Aggregate** weekly incidence counts to compute **annual totals**.
+6. **Identify the year with the strongest or weakest epidemic**.
+
+**Note**: at the end of every modification on Jupyter it is important to Run line by line to check the code and then first update in Jupyter before the final commit to Gitlab.