Add paper link, GitHub link, and graph-ml task category

Hi! I'm Niels from the community science team at Hugging Face. I noticed this dataset card could be improved with additional metadata and links to the associated research.

This PR:
- Adds a link to the [accompanying paper](https://huggingface.co/papers/2605.15511).
- Adds a link to the [official GitHub repository](https://github.com/geometric-intelligence/ogbench).
- Updates the `task_categories` to include `graph-ml`.
- Adds a sample usage section with instructions on how to download the datasets and train models using the provided framework.

README.md

@@ -1,5 +1,10 @@
 ---
 license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- graph-ml
+pretty_name: OgBench — Omics Graph Benchmark
 tags:
 - biology
 - genomics
@@ -7,24 +12,17 @@ tags:
 - graph-neural-networks
 - benchmarking
 - omics
-pretty_name: OgBench — Omics Graph Benchmark
-task_categories:
-- tabular-classification
-size_categories:
-- n<1K
 ---
 
 # OgBench: Benchmarking Graph Neural Networks on Omics Data
 
-OgBench is the first benchmark suite for graph-level prediction in the 
-**n ≪ p regime** characteristic of omics data, where the number of 
-patient samples n is much smaller than the number of nodes (genes or 
-proteins) p per graph.
+[**Paper**](https://huggingface.co/papers/2605.15511) | [**GitHub**](https://github.com/geometric-intelligence/ogbench)
+
+OgBench is the first benchmark suite for graph-level prediction in the **n ≪ p regime** characteristic of omics data, where the number of patient samples *n* is much smaller than the number of nodes (genes or proteins) *p* per graph.
 
 ## Datasets
 
-This repository contains four preprocessed omics graph classification 
-datasets:
+This repository contains four preprocessed omics graph classification datasets:
 
 | Dataset | Modality | n | p | Task |
 |---|---|---|---|---|
@@ -33,21 +31,31 @@ datasets:
 | AddNeuroMed | Transcriptomics | 711 | 17,198 | Clinical diagnosis (3-class) |
 | BRCA | Epigenomics | 640 | 19,049 | Cancer subtype (4-class) |
 
+## Usage
+
+After installing the [OgBench framework](https://github.com/geometric-intelligence/ogbench), you can download and process the datasets using the provided script:
+
+```bash
+# Download a specific dataset (e.g., motrpac/HERITAGE)
+python scripts/download_datasets.py motrpac
+
+# Download all datasets
+python scripts/download_datasets.py all
+```
+
+To train a model (e.g., GATv2) on one of the datasets:
+
+```bash
+python ogbench/run.py dataset=motrpac model=gatv2
+```
+
 ## Source Data
 
-- **HERITAGE**: Robbins et al. (2021), *Nature Metabolism*. Available 
-  via MoTrPAC Data Hub (motrpac-data.org) under CC-BY 4.0.
-- **Parkinson's**: Shamir et al. (2017), *Neurology*. Available via 
-  NCBI GEO (GSE99039) under GEO public data access policy.
-- **AddNeuroMed**: Lovestone et al. (2009). Available via NCBI GEO 
-  (GSE63063) under GEO public data access policy.
-- **BRCA**: Yang et al. (2025), MLOmics, *Scientific Data*. Available 
-  on Figshare/Hugging Face under CC-BY 4.0.
+- **HERITAGE**: Robbins et al. (2021), *Nature Metabolism*. Available via MoTrPAC Data Hub (motrpac-data.org) under CC-BY 4.0.
+- **Parkinson's**: Shamir et al. (2017), *Neurology*. Available via NCBI GEO (GSE99039) under GEO public data access policy.
+- **AddNeuroMed**: Lovestone et al. (2009). Available via NCBI GEO (GSE63063) under GEO public data access policy.
+- **BRCA**: Yang et al. (2025), MLOmics, *Scientific Data*. Available on Figshare/Hugging Face under CC-BY 4.0.
 
 ## Preprocessing
 
-All datasets are preprocessed with a consistent pipeline including 
-probe-to-gene aggregation, normalization, and covariate adjustment. 
-Full preprocessing details are provided in Appendix B of the 
-accompanying paper. Graphs are split 70/15/15 (train/val/test) with 
-a fixed random seed.
+All datasets are preprocessed with a consistent pipeline including probe-to-gene aggregation, normalization, and covariate adjustment. Full preprocessing details are provided in Appendix B of the accompanying paper. Graphs are split 70/15/15 (train/val/test) with a fixed random seed.