Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: gpl-3.0
 viewer: false
 ---
 # ChaosBench
@@ -13,35 +13,36 @@ We establish two tasks: full and sparse dynamics prediction.
 🔗: [https://github.com/leap-stc/](https://github.com/leap-stc/)
 
 📚: [https://arxiv.org/](https://arxiv.org/)
-   
-## Table of Content
-- [Getting Started](#getting-started)  
-- [Dataset Overview](#dataset-overview) 
-- [Evaluation Metrics](#evaluation-metrics) 
-- [Leaderboard](#leaderboard) 
 
 ## Getting Started
-1. Set up the project...
-- Clone the `ChaosBench` Github repository
-- Create local directory to store your data, e.g., `data/`
-- Navigate to `chaosbench/config.py` and change the field `DATA_DIR = <YOUR_WORKING_DATA_DIR>`
+**Step 1**: Clone the [ChaosBench](https://github.com/leap-stc/ChaosBench) Github repository
 
-2. Download the dataset...
-- First we perform initialization:
-  ```
-  cd data/
-  wget https://huggingface.co/datasets/juannat7/ChaosBench/blob/main/process.sh
-  chmod +x process.sh
-  ```
-- And finally: (__NOTE__: you can also run each line _one at a time_ to retrieve each dataset)
-  ```
-  ./process.sh era5            # For input ERA5 data
-  ./process.sh ukmo            # For simulation from UKMO
-  ./process.sh ncep            # For simulation from NCEP
-  ./process.sh cma             # For simulation from CMA
-  ./process.sh ecmwf           # For simulation from ECMWF
-  ./process.sh climatology     # For climatology
-  ```
+**Step 2**: Create local directory to store your data, e.g., 
+```
+cd ChaosBench
+mkdir data
+```
+
+**Step 3**: Navigate to `chaosbench/config.py` and change the field `DATA_DIR = /<YOUR_WORKING_DIR>/ChaosBench/data` (_Provide absolute path_)
+
+**Step 4**: Initialize the space by running
+```
+cd /<YOUR_WORKING_DIR>/ChaosBench/data/
+wget https://huggingface.co/datasets/juannat7/ChaosBench/blob/main/process.sh
+chmod +x process.sh
+```
+**Step 5**: Download the data 
+
+```
+# NOTE: you can also run each line one at a time to retrieve individual dataset
+
+./process.sh era5            # Required: For input ERA5 data
+./process.sh climatology     # Required: For climatology
+./process.sh ukmo            # Optional: For simulation from UKMO
+./process.sh ncep            # Optional: For simulation from NCEP
+./process.sh cma             # Optional: For simulation from CMA
+./process.sh ecmwf           # Optional: For simulation from ECMWF
+```
 
 ## Dataset Overview
 
@@ -72,6 +73,7 @@ We establish two tasks: full and sparse dynamics prediction.
         - [x] ViT/ClimaX
         - [x] PanguWeather
         - [x] Fourcastnetv2
+        - [x] GraphCast
 
 ## Evaluation Metrics
 We divide our metrics into 2 classes: (1) ML-based, which cover evaluation used in conventional computer vision and forecasting tasks, (2) Physics-based, which are aimed to construct a more physically-faithful and explainable data-driven forecast.