dataset exploration usage scenario added

README.md

@@ -127,6 +127,8 @@ it can be either rounded by the researchers to full years, or converted from mon
 
 ## Usage Guidelines
 
+### Benchmark a new aging clock
+
 First install our nice [library](https://github.com/ComputationalAgingLab/ComputAge) for convenient operation with datasets and other tools for aging clock design:
 
 `pip install computage`
@@ -158,6 +160,26 @@ bench = run_benchmark(models_config,
 #upon completion, the results will be saved in the folder you specified
 ```
 
+### Explore the dataset
+
+In case you want just to explore our dataset locally, use the following commands for downloading.
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id='computage/computage_bench', 
+    repo_type="dataset",
+    local_dir='.')
+```
+Once downloaded, the dataset can be open with `pandas` (or any other `parquet` reader).
+```python
+import pandas as pd
+#let's choose a study id, for example `GSE100264`
+df = pd.read_parquet('data/computage_bench_data_GSE100264.parquet').T 
+#note we transpose data for more convenient perception
+#Don't forget to explore metadata (which is common for all datasets):
+meta = pd.read_csv('computage_bench_meta.tsv', sep='\t', index_col=0)
+```
+
 ## Additional Information
 
 ### Licensing Information