Update dataset card with paper link, abstract, task category, tags, and sample usage

This PR significantly enhances the `nilshoehing/rocketsciencebench` dataset card by:

* **Adding `task_categories` and `tags` to metadata:**
* `task_categories: image-text-to-text` accurately reflects the dataset's focus on spatial relation understanding in Vision Language Models.
* `tags: spatial-reasoning`, `vlm`, `benchmark` improve discoverability for users looking for similar resources.
* **Updating the Dataset Description:** Replaced the placeholder with the comprehensive abstract from the paper "Understanding Space Is Rocket Science - Only Top Reasoning Models Can Solve Spatial Understanding Tasks" to provide a detailed overview of the dataset.
* **Updating the Paper Link:** Replaced the "coming soon" placeholder with the official Hugging Face paper URL: `https://huggingface.co/papers/2509.02175`.
* **Adding a Sample Usage section:** Included a basic code snippet demonstrating how to load the dataset using the `datasets` library, based on the information in the Github README.
* **Adding a BibTeX Citation:** Provided a BibTeX entry for the paper to facilitate proper academic attribution.

These changes make the dataset card more informative, discoverable, and user-friendly.

README.md

@@ -1,10 +1,21 @@
 ---
+language:
+- en
 license: cc-by-4.0
+size_categories:
+- n<1K
+pretty_name: Rocket Science
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
+task_categories:
+- image-text-to-text
+tags:
+- spatial-reasoning
+- vlm
+- benchmark
 dataset_info:
   features:
   - name: text1
@@ -25,19 +36,15 @@ dataset_info:
     num_examples: 241
   download_size: 771816767
   dataset_size: 772658832
-language:
-- en
-pretty_name: Rocket Science
-size_categories:
-- n<1K
 ---
+
 # Dataset Card for Rocket Science
 
 Rocket Science is a benchmark that tests for understanding of spatial relations in Vision Language Models.
 
 # Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+We propose RocketScience, an open-source contrastive VLM benchmark that tests for spatial relation understanding. It is comprised of entirely new real-world image-text pairs covering mostly relative spatial understanding and the order of objects. The benchmark is designed to be very easy for humans and hard for the current generation of VLMs, and this is empirically verified. Our results show a striking lack of spatial relation understanding in open source and frontier commercial VLMs and a surprisingly high performance of reasoning models. Additionally, we perform a disentanglement analysis to separate the contributions of object localization and spatial reasoning in chain-of-thought-based models and find that the performance on the benchmark is bottlenecked by spatial reasoning and not object localization capabilities.
 
 - **Curated by:** Nils Hoehing
 - **Language(s) (NLP):** English
@@ -45,15 +52,29 @@ Rocket Science is a benchmark that tests for understanding of spatial relations
 
 # Dataset Sources
 
-<!-- Provide the basic links for the dataset. -->
-
 - **Repository:** https://github.com/nilshoehing/rocketscience
-- **Paper:** coming soon
+- **Paper:** https://huggingface.co/papers/2509.02175
 
-# Citation
+# Sample Usage
+
+The dataset can be loaded using the Hugging Face `datasets` library:
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+```python
+from datasets import load_dataset
 
-**BibTeX:** coming soon
+dataset = load_dataset("nilshoehing/rocketsciencebench")
+```
+
+# Citation
 
+Please cite the following paper if you use this dataset:
 
+```bibtex
+@article{hoehing2025understanding,
+  title={Understanding Space Is Rocket Science - Only Top Reasoning Models Can Solve Spatial Understanding Tasks},
+  author={Hoehing, Nils and Likhitha, D and Woehler, Thomas and Beham, Michael and Kuehne, Axel},
+  journal={arXiv preprint arXiv:2509.02175},
+  year={2025},
+  url={https://huggingface.co/papers/2509.02175}
+}
+```