Add metadata, paper/project links and usage instructions

Hi, I'm Niels, part of the community science team at Hugging Face. I've updated the dataset card to include:
- Relevant `task_categories` (`image-text-to-text`) and `language` tags.
- Links to the paper, project page, and GitHub repository.
- A sample usage section showing how to download the dataset via the CLI as documented in the official repository.
- The BibTeX citation for the paper.

README.md

@@ -36,10 +36,46 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+task_categories:
+- image-text-to-text
+language:
+- en
+tags:
+- visual-reasoning
+- physics-prediction
+- sketching
 ---
 
+# SketchVLM: Physics Ball Drop Dataset
 
-# Dataset Name
+This dataset is part of the **SketchVLM** project, introduced in the paper: [SketchVLM: Vision language models can annotate images to explain thoughts and guide users](https://arxiv.org/abs/2604.22875).
 
-This dataset is associated with the paper:
-[SketchVLM: Vision Language Models Can Annotate Images to Explain Thoughts and Guide Users](https://arxiv.org/abs/2604.22875)
+[**Project Page**](https://sketchvlm.github.io/) | [**GitHub**](https://github.com/Brandon-Collins7/sketchvlm) | [**Interactive Demo**](https://sketch-vlm-demo.vercel.app/)
+
+## Description
+
+SketchVLM is a training-free, model-agnostic framework that enables vision-language models (VLMs) to produce non-destructive, editable SVG overlays on input images to visually explain their reasoning. 
+
+The **Physics Ball Drop** dataset (based on PHYRE) is one of the benchmarks developed to evaluate visual reasoning. In this task, models must predict the trajectory of a ball through various obstacles and provide a visual explanation of the path.
+
+## Usage
+
+As documented in the official GitHub repository, you can download the images and metadata locally using the Hugging Face CLI:
+
+```bash
+huggingface-cli download loganbolton/sketchvlm-physics-ball-drop --repo-type dataset --local-dir datasets/ball_drop
+```
+
+## Citation
+
+```bibtex
+@misc{collins2026sketchvlmvisionlanguagemodels,
+      title={SketchVLM: Vision language models can annotate images to explain thoughts and guide users},
+      author={Brandon Collins and Logan Bolton and Hung Huy Nguyen and Mohammad Reza Taesiri and Trung Bui and Anh Totti Nguyen},
+      year={2026},
+      eprint={2604.22875},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.22875},
+}
+```