Update README.md

README.md

@@ -106,4 +106,41 @@ The data is stored in a single parquet file which has the following fields.
 | `log2_selected_timecourses` | Cleaned timecourses hard-thresholded based on single observations passing noise model and impulse evaluation of biological feasibility                                                                     |
 | `log2_shrunken_timecourses` | Selected timecourses with observation-level shrinkage based on local FDR (false discovery rate). **Most users of the data will want to use this column.**                                                       |
 
+
+## Usage
+
+I recommend using `huggingface_hub.snapshot_download` to pull the repository. After that, use your favorite
+method of interacting with `parquet` files (eg duckDB, but you could use dplyr in R or pandas, too).
+
+```python
+from huggingface_hub import snapshot_download
+import duckdb
+
+repo_id = "BrentLab/hackett_2020"
+
+# Download entire repo to local directory
+repo_path = snapshot_download(
+    repo_id=repo_id,
+    repo_type="dataset"
+)
+
+print(f"Repository downloaded to: {repo_path}")
+
+# Construct path to the parquet file
+parquet_path = os.path.join(repo_path, "hackett_2020.parquet")
+print(f"Parquet file at: {parquet_path}")
+
+# Connect to DuckDB and query the parquet file
+conn = duckdb.connect()
+
+query = """
+SELECT DISTINCT time, mechanism, restriction, date
+FROM read_parquet(?)
+WHERE regulator_symbol = 'ACA1'
+"""
+result = conn.execute(query, [parquet_path]).df()
+print(f"Found {result}")
+```
+
+
 **Dataset Author and Contact**: Chase Mateusiak [@cmatKhan](https://github.com/cmatkhan/)