diff --git a/README.md b/README.md
index 4e97bd7..8f8c8d4 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,38 @@
-# Partitura Tutorial
-A quick introduction to symbolic music processing with partitura:
+# Partitura Tutorials
 
-## Installation
+Welcome to a quick introduction to symbolic music processing with partitura!
 
-The easiest way to install the package is via ``pip`` from the `PyPI (Python
+This repo was originally developed for a tutorial session at ISMIR 22 and is now maintained as hands-on documentation for the [partitura library](https://github.com/CPJKU/partitura).
+Partitura is a library for symbolic music processing, that is, it reads, writes, and manipulates musical scores in a variety of formats and MIDI files and aims at:
+- file I/O for all things symbolic music; scores, performances, and alignments of the two.
+- feature extraction: creating note arrays, piano rolls, and custom features.
+- minimal reference implementations of automatic music analysis tools.
+
+### What's in the tutorials?
+
+In the directory `notebooks` there are four tutorials:
+- 01_introduction: how to read and write scores and performances in partitura, how to manipulate musical material, and how to extract features like note arrays and piano rolls.
+- 02_alignment: how to read, process, and create symbolic (note-to-note) music alignments with partitura and Daynamic Time Warping (fastdtw, Vienna4x22 dataset)
+- 03_mlflow: automatic pitch spelling as an example of a machine learning pipeline for automatic music analysis (pytorch, lightning, LSTM, ASAP dataset)
+- 04_generation: a small drum beat generator as an example of a machine learning pipeline for automatic music generation (pytorch, Transformer Encoder, Groove Midi Dataset)
+
+### How to use the tutorials?
+
+The tutorials consist of jupyter notebooks for you to run **locally** or on **google colab** (by clicking the link at the top of each notebook).
+To run the notebooks locally, be sure to install the dependencies (or create an environment) based on `environment.yml`.
+We aim to keep this tutorials updated with the current **version** of partitura, for prior versions of partitura, check the versions on this github repository.
+
+### More information?
+
+The partitura **documentation** and API reference is on [readthedocs](https://partitura.readthedocs.io/en/latest/).
+Do you have **questions?** You're welcome on the [partitura discussions](https://github.com/CPJKU/partitura/discussions).
+Encountered a **bug?** You're welcome to raise an [issue](https://github.com/CPJKU/partitura/issues).
+
+
+
+### Partitura Installation
+
+The easiest way to install the partitura package is via ``pip`` from the `PyPI (Python
 Package Index) <https://pypi.python.org/pypi>`_::
 
 ```shell
@@ -23,7 +52,7 @@ pip install git+https://github.com/CPJKU/partitura.git@develop
 
 
 
-## QuickStart
+### Partitura QuickStart
 
 The following code loads the contents of an example MusicXML file included in
 the package:
@@ -31,35 +60,27 @@ the package:
 ```python
 import partitura
 my_xml_file = partitura.EXAMPLE_MUSICXML
-part = partitura.load_musicxml(my_xml_file)
+score = partitura.load_musicxml(my_xml_file)
 ```
 
-### Import other formats
-
-For **MusicXML** files do:
+For **MusicXML**, **Kern**, or **MEI** files do:
 
 ```python
 import partitura
-my_xml_file = partitura.EXAMPLE_MUSICXML
-part = partitura.load_musicxml(my_xml_file)
+score = partitura.load_score(my_file)
 ```
 
+### Citation
 
+If you find Partitura useful, we would appreciate it if you could cite us!
 
-For **Kern** files do:
-
-```python
-import partitura
-my_kern_file = partitura.EXAMPLE_KERN
-part = partitura.load_kern(my_kern_file)
 ```
-
-For **MEI** files do:
-
-```python
-import partitura
-my_mei_file = partitura.EXAMPLE_MEI
-part = partitura.load_mei(my_mei_file)
+@inproceedings{partitura_mec,
+  title={{Partitura: A Python Package for Symbolic Music Processing}},
+  author={Cancino-Chac\'{o}n, Carlos Eduardo and Peter, Silvan David and Karystinaios, Emmanouil and Foscarin, Francesco and Grachten, Maarten and Widmer, Gerhard},
+  booktitle={{Proceedings of the Music Encoding Conference (MEC2022)}},
+  address={Halifax, Canada},
+  year={2022}
+}
 ```
 
-