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--- |
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license: mit |
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task_categories: |
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- question-answering |
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- text-generation |
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tags: |
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- data-science |
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- data-analysis |
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- bioinformatics |
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- statistical-analysis |
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- machine-learning |
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language: |
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- en |
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pretty_name: DSGym-Tasks |
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--- |
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# DSGym |
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DSGym is a unified benchmark and execution framework for evaluating and training |
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data science agents. It provides standardized, executable tasks that require |
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agents to plan, implement, and validate data analyses through interaction with |
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real data files in isolated environments. |
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## Attribution |
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If you find our work useful, please cite our paper: |
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``` |
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@misc{nie2026dsgym, |
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title={DSGym: A Holistic Framework for Evaluating and Training Data Science Agents}, |
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author={Fan Nie and Junlin Wang and Harper Hua and Federico Bianchi and Yongchan Kwon and Zhenting Qi and Owen Queen and Shang Zhu and James Zou}, |
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year={2026}, |
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eprint={2601.16344}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI}, |
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url={https://arxiv.org/abs/2601.16344}, |
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} |
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``` |
