Improve dataset card: Add task categories, paper, project, and code links

README.md

@@ -1,21 +1,39 @@
 ---
 license: mit
+task_categories:
+- text-generation
+- code-generation
+- text-to-sql
+- question-answering
+tags:
+- data-intelligence
+- data-engineering
+- data-analysis
+- agents
+- sql
+- benchmark
+language:
+- en
 ---
 
 # DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle
 
+This repository contains **DAComp**, a comprehensive benchmark featuring 210 tasks that mirror complex real-world enterprise data intelligence workflows. It's designed to evaluate large language models (LLMs) and agents across two main dimensions: data engineering and data analysis. Data engineering (DE) tasks involve designing and building multi-stage SQL pipelines, while data analysis (DA) tasks pose open-ended business problems that demand strategic planning, iterative coding, and the synthesis of actionable recommendations.
 
+Paper: [DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle](https://huggingface.co/papers/2512.04324)
+Project page: https://da-comp.github.io/
+Code: https://github.com/anonymous/DAComp
 
-# ✍️ Citation
+## ✍️ Citation
 If you find our work helpful, please cite as
-```
+```bibtex
 @misc{lei2025dacompbenchmarkingdataagents,
-      title={DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle}, 
+      title={DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle},
       author={Fangyu Lei and Jinxiang Meng and Yiming Huang and Junjie Zhao and Yitong Zhang and Jianwen Luo and Xin Zou and Ruiyi Yang and Wenbo Shi and Yan Gao and Shizhu He and Zuo Wang and Qian Liu and Yang Wang and Ke Wang and Jun Zhao and Kang Liu},
       year={2025},
       eprint={2512.04324},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2512.04324}, 
+      url={https://arxiv.org/abs/2512.04324},
 }
 ```