README update, moving shards to separate directory

README.md

@@ -39,10 +39,15 @@ The dataset contains in total **80,898** samples, divided into two splits:
 Each sample consists of 4 different files:
 |Type|Description|
 |---|---|
-|optical.png|The monochromatic optical image patch. Despite being monochromatic, the image still has 3 channels, with all channels being the same|
-|elevation.tiff|The elevation data patch in meters w.r.t. the Martian datum|
-|deviation_mask.png|Binary mask with locations that were identified as elevation artifacts during dataset generation and were replaced with interpolated values|
-|initial_nan_mask.png|Binary mask with locations that contained missing values in the Day et al. data samples and were imputed during processing|
+|***optical.png***|The monochromatic optical image patch. Despite being monochromatic, the image still has 3 channels, with all channels being the same|
+|***elevation.tiff***|The elevation data patch in meters w.r.t. the Martian datum|
+|***deviation_mask.png***|Binary mask with locations that were identified as elevation artifacts during dataset generation and were replaced with interpolated values|
+|***initial_nan_mask.png***|Binary mask with locations that contained missing values in the Day et al. data samples and were imputed during processing|
+
+### Sample naming
+Each sample follows the following naming convention:
+
+<img src="./.images/sample_naming.png" alt="Naming convention of each sample" style="border-radius:15px;">
 
 ## Data source
 
@@ -51,6 +56,44 @@ by [Day et al. 2023](https://faculty.epss.ucla.edu/~mday/index.php/mars-dems/).
 
 <img src="./.images/sankey_processing.png" alt="Sankey diagram of processed samples" style="width:50%;"/>
 
+## Typical usage
+The simplest way to use MCTED is by using the `load_dataset` function from HuggingFace's `datasets` python package:
+```python
+from datasets import load_dataset
+
+# Download and load the dataset 
+mcted = load_dataset("ESA-Datalabs/MCTED", num_proc=8)
+```
+
+## Example of accessing sample data
+```python
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+import numpy as np
+
+mcted = load_dataset("ESA-Datalabs/MCTED", num_proc=8)
+
+# Load one sample from the validation split
+sample = mcted["validation"][0]
+
+plt.figure(figsize=(15, 5))
+plt.subplot(1, 4, 1)
+plt.imshow(sample["optical.png"])
+plt.title("Optical image")
+
+plt.subplot(1, 4, 2)
+plt.imshow(np.array(sample["elevation.tif"]), cmap="terrain")
+plt.title("DEM")
+
+plt.subplot(1, 4, 3)
+plt.imshow(sample["deviation_mask.png"], cmap="gray")
+plt.title("Elevation outlier mask")
+
+plt.subplot(1, 4, 4)
+plt.imshow(sample["initial_nan_mask.png"], cmap="gray")
+plt.title("Initial invalid values mask")
+```
+
 ## Citation
 ```bibtex
 @misc {}