diff --git a/README.md b/README.md
index 5fa4b7c2bc02d5325ce39955af61a0b817884876..77d8bcab7941ea25119e79fbcba27890bdfac129 100644
--- a/README.md
+++ b/README.md
@@ -1,68 +1,25 @@
-# Air quality mapping with the AQ-Bench dataset
+# Machine learning on the AQ-Bench dataset
 
-The goal of this project is to map metadata at station locations to air quality statistics.
+This repository enables a machine learning quickstart on the AQ-Bench dataset.
 
-These instructions will get you a copy of the project up and running on your PC.
+The AQ-Bench Benchmark dataset is described in Betancourt et al. (manuscript): "AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics" (link follows)
 
-## Structure of the project
+## Quickstart
 
-This project consists of two parts:
-* Obtaining the training dataset from TOAR-DB and JOIN. We call it AQ-Bench for now.
-* The mapping part
+Run it on binder!
 
-## Hyperparameter tuning "Hackathon"
+## Get the project running on your PC
 
-Get yourself up and ready:
-* Download the project from Git 
-* Run ```source prepare.sh``` for python environment
-* Start the Jupyter notebook ```cd source```, ```jupyter notebook```
+* Prerequisite: Conda or MiniConda with Python 3.6
+* Use ```environment.yml``` to create an environment, then activate it
+* Navigate to ```source``` and start the ```introduction_jupyter.ipynp``` by prompting ```jupyter notebook```
 
-Rules for the Game:
-* We provide you with training data (train/dev split as you like)
-* Try out hyperparameters
-* Submit your best hyper-parameters to be tested with our secret test set
-* Best hyper-parameters win the price!
+## Structure of the repository
 
-## Downloading the AQ-Bench dataset
+* ```resources``` contains the data
+* ```source``` contains the scripts
 
-* We provide the dataset in the data folder of this project. Nevertheless, you can also download it by yourself.
-* If would like to download the AQ-Bench dataset, turn on FZJ VPN for TOAR access.
-* Create a file ```dataset_dbaccess.py``` in the source directory which contains your credentials for TOAR-DB (if you do not have access to TOAR-db, then just leave '***' for username and password): 
 
-```
-db_user = '****'
-db_password = '****'
-db_host = 'zam10131.zam.kfa-juelich.de'
-db_port = '5432'
-db_name = 'surface_observations_toar'
-```
-## Resources to describe AQ-Bench
-
-The resources folder contains .csv files with necessary info to handle the dataset.
-
-* ```AQbench_variables.csv```: Info on all variables in the dataset
-* ```*_cols.csv```: Info for dataset retrieval
-* ```climatic_zone.csv```, ```htap_region.csv```, ```climatic_landcover.csv```: Info on decoded variables
-
-## Run Scripts
-
-Run ```source run.sh``` to start the interactive script starter. You may choose from various options:
-
-* ```prepare ```
-  * Creates folders for logs (where your log files are stored), data (where the dataset is stored) and plots (where your plots will be stored)
-  * Creates and activates the mapping environment 
-* ```test ```
-  * Starts all tests in the test folder
-* ```retrieval ```
-  * Starts the dataset retrieval from TOAR-DB and JOIN
-* ```sanitycheck ```
-  * Carries out a sanitycheck for your dataset
-* ```preanalysis ```
-  * Preliminary analysis of dataset statistics
-  * Visualisation of missing values
-* ```mapping ```
-  * Mapping of the dataset (multi layer perceptron)
-  * Mapping of the dataset (random forest)
 
 ## Authors