diff --git a/README.md b/README.md
index deab007..6762fa1 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,20 @@
 # Neural Architecture Search Without Training
 
-**IMPORTANT** : our codebase relies on use of the NASBench-201 dataset. As such we make use of cloned code from [this repository](https://github.com/D-X-Y/AutoDL-Projects). We have left the copyright notices in the code that has been cloned, which includes the name of the author of the open source library that our code relies on.
+This repository contains code for replicating our paper on NAS without training. 
 
-The datasets can also be downloaded as instructed from the NASBench-201 README: [https://github.com/D-X-Y/NAS-Bench-201](https://github.com/D-X-Y/NAS-Bench-201).
+## Setup 
+
+1. Download the [datasets](https://drive.google.com/drive/folders/1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7).
+2. Download [NAS-Bench-201](https://drive.google.com/file/d/1OOfVPpt-lA4u2HJrXbgrRd42IbfvJMyE/view).
+3. Install the requirements in a conda environment with `conda env create -f environment.yml`.
+
+We also refer the reader to instructions in the official [NASBench-201 README](https://github.com/D-X-Y/NAS-Bench-201).
+
+## Reproducing our results 
 
 To reproduce our results:
 
 ```
-conda env create -f environment.yml
-
 conda activate nas-wot
 ./reproduce.sh 3 # average accuracy over 3 runs
 ./reproduce.sh 500 # average accuracy over 500 runs (this will take longer)
@@ -34,3 +40,7 @@ To try different sample sizes, simply change the `--n_samples` argument in the c
 Note that search times may vary from the reported result owing to hardware setup.
 
 The code is licensed under the MIT licence.
+
+# Acknowledgements
+
+This repository makes liberal use of code from the [AutoDL](https://github.com/D-X-Y/AutoDL-Projects) library. We also rely on [NAS-Bench-201](https://github.com/D-X-Y/NAS-Bench-201).