Add dataset description and paper link

README.md

@@ -1,4 +1,11 @@
 ---
+task_categories:
+- other
+tags:
+- medical
+- hemodynamics
+- physics-ml
+license: other
 dataset_info:
   features:
   - name: vessel_id
@@ -70,3 +77,30 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# Single-Vessel Coronary Hemodynamics Dataset
+
+This repository contains the synthetic benchmark dataset presented in the paper [From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries](https://huggingface.co/papers/2605.27578).
+
+## Dataset Description
+
+This dataset consists of 4,200 single-vessel coronary artery geometries with controlled anatomical variations. Each geometry is paired with steady-state Computational Fluid Dynamics (CFD) simulations performed using OpenFOAM to provide ground-truth hemodynamic metrics, including pressure and Wall Shear Stress (WSS).
+
+The data is designed to support the training and evaluation of models (like the transformer-based encoder and anisotropic RBF decoder described in the paper) for fast, non-invasive coronary hemodynamics prediction from 1D vessel centerlines and inlet flow rates.
+
+### Dataset Summary
+- **Total Geometries**: 4,200 synthetic single-vessel cases.
+- **Split**: 3,600 training, 400 validation, and 200 test examples.
+- **Inputs**: 1D vessel centerlines and inlet flow rates.
+- **Outputs**: Paired steady-state pressure and Wall Shear Stress (WSS) fields.
+
+## Citation
+
+```bibtex
+@article{jin2024centerlines,
+  title={From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries},
+  author={Jin, Zhuoran and Yuan, Hongbang and Men, Tianyi and Cao, Pengfei and Chen, Yubo and Liu, Kang and Zhao, Jun},
+  journal={arXiv preprint arXiv:2605.27578},
+  year={2024}
+}
+```