diff --git a/README.md b/README.md
index 44395a2..86178c6 100644
--- a/README.md
+++ b/README.md
@@ -29,6 +29,16 @@ can be installed from the Github repo  with ``pip``:
 
 ```
 
+or alternatively, you can clone the repository and install it from the source:
+
+```bash
+
+    git clone https://github.com/yourrepository/pathways.git
+    cd pathways
+    pip install -r requirements.txt
+    
+```
+
 
 ## Usage
 
@@ -37,21 +47,56 @@ or in a Python interpreter.
 
 ### Python
 
+To use the Pathways class, you need to provide it with a datapackage that contains your scenario data, mapping information, and LCA matrices.
+The datapackage should be a zip file that contains the following files:
+
+- `datapackage.json`: a JSON file that describes the contents of the datapackage
+- a `mapping` folder containing a `mapping.yaml` file that describes the mapping between the IAM scenario and the LCA databases
+- a `inventories` folder containing the LCA matrices as CSV files
+- a `scenario_data` folder containing the IAM scenario data as CSV file
+
 ```python
 
 from pathways import Pathways
-p = Pathways(datapackage="some datapackage.zip")
+datapackage_path = "path/to/your/datapackage.zip"
+p = Pathways(
+    datapackage=datapackage_path,
+    debug=True # optional, if you want to see the logs
+)
+
+# Define your parameters (leave any as None to use all available values)
+methods = ["IPCC 2021", "ReCiPe 2016"]
+models = ["ModelA", "ModelB"]
+scenarios = ["Baseline", "Intervention"]
+regions = ["Region1", "Region2"]
+years = [2020, 2025]
+variables = ["Electricity", "Transport"]
+
+# Run the calculation
 p.calculate(
-    methods=[
-            "EF v3.1 - acidification - accumulated exceedance (AE)"
-        ],
-    years=[2080, 2090, 2100],
-    regions=["World"],
-    scenarios=["SSP2-Base", "SSP2-RCP26",]
+    methods=methods,
+    models=models,
+    scenarios=scenarios,
+    regions=regions,
+    years=years,
+    variables=variables,
+    characterization=True,
+    multiprocessing=True,
+    demand_cutoff=0.001,
+    use_distributions=0
 )
 
 ```
 
+The list of available LCIA methods can be obtained like so:
+
+```python
+
+    print(p.lcia_methods)
+
+```
+
+
 The argument `datapackage` is the path to the datapackage.zip file
 that describes the scenario and the LCA databases -- see dev/sample.
 The argument `methods` is a list of methods to be used for the LCA
@@ -68,13 +113,16 @@ time-consuming.
 Once calculated, the results of the LCA calculations are stored in the `.lcia_results`
 attribute of the `Pathways` object as an ``xarray.DataArray``. 
 
+You can display the LCA results with an optional cutoff parameter to filter insignificant data:
+
+
 ```python
 
-p.lcia_results
+    results = p.display_results(cutoff=0.001)
+    print(results)
 
 ```
 
-
 It can be further formatted
 to a pandas' DataFrame or export to a CSV/Excel file using the built-in
 methods of ``xarray``.
@@ -129,7 +177,7 @@ The best way to send feedback is to file an issue on the GitHub repository.
 ### Contributors
 
 * [Romain Sacchi](https://github.com/romainsacchi)
-* Alvaro Hahn Menacho (https://github.com/alvarojhahn)
+* [Alvaro Hahn Menacho](https://github.com/alvarojhahn)
 
 
 ### Financial Support