diff --git a/README.md b/README.md
index 7ec697d5ccf5ca6233c6156a1913cbc9a16dc6f6..052ae0cb238e8b4b2d2b41a7f035822f943b177d 100644
--- a/README.md
+++ b/README.md
@@ -42,25 +42,34 @@ The main software used (and configured in the file `default.nix`) are:
 - python3, pandas, jupyter, matplotlib etc. for the data analysis
 
 Enter a shell with all dependencies managed. This will take some time (~5mn) to download and compile everything the first time you launch it, but then all the environment is cached for future use.
+
 ```bash
 nix-shell -A exp_env --pure
 ```
 
 ### 2. Prepare input workload
 Inside the nix shell, start a notebook and follow the steps presented in `prepare_workload.ipynb` (~5mn):
+
 ```bash
-jupyter notebook 0_prepare_workload.ipynb
+jupyter notebook prepare_workload.ipynb
 ```
 
 ### 3. Launch the campaign
-Still inside the nix shell, launch the python script `campaign.py`. It will prepare and launch in parallel the 105 experiments. Each experiment corresponds to one instance of `instance.py`. 
+Still inside the nix shell, launch the python script `campaign.py`.
+It will prepare and launch in parallel the 105 experiments.
+Each experiment corresponds to one instance of `instance.py`. 
+
 ```bash
 python3 campaign.py
 ```
+
 This step took 89 minutes on an Intel Xeon E5-2630 v3 2x8 cores CPU.
 
-### 4. Analyse the results
-Finally and still in the nix shell (otherwise you just need to install with `pip` some python libraries like pandas, evalys, matplotlib...), run this Jupyter notebook to plot the graphs displayed in the article (~10mn):
+### 4. Analyze the results
+Finally and still in the nix shell 
+(otherwise you just need to install with `pip` some python libraries like pandas, evalys, matplotlib...),
+run this Jupyter notebook to plot the graphs displayed in the article (~10mn):
+
 ```bash
 jupyter notebook analyse_campaign.ipynb
 ```