Explain datasource downloads and automatic image caching

README.md

@@ -44,6 +44,35 @@ kept = load_dataset("FWeindel/validated-tasks", split="train")
 archived = load_dataset("FWeindel/validated-tasks", "archive", split="train")
 ```
 
+## Download one datasource and its images
+
+To run one datasource, download its original Parquet. Replace `SOURCE` with
+its folder name and choose a local workspace:
+
+```python
+from huggingface_hub import hf_hub_download
+
+parquet = hf_hub_download(
+    repo_id="FWeindel/validated-tasks",
+    repo_type="dataset",
+    filename="SOURCE/tasks.parquet",
+    local_dir="/path/to/workspace/dataset",
+)
+```
+
+This downloads the tasks only. When you run that file with the
+[OT-next-data launcher](https://github.com/MLI-lab/OT-next-data/blob/main/validation/README.md),
+its [image downloader](https://github.com/MLI-lab/OT-next-data/blob/main/validation/publishing/image_release.py)
+automatically downloads the published Apptainer images needed by your selected
+tasks, verifies them, and caches them for later runs. You do not need to download
+other datasources' images.
+
+This works for releases with image references in their Parquet metadata.
+Keep the original file to preserve those references. Tasks without published
+images are built as usual. See the
+[download and cache guide](https://github.com/MLI-lab/OT-next-data/blob/main/validation/docs/image-publication.md)
+for details and Docker support.
+
 ## Columns
 
 | Column | Meaning |