@inproceedings{yang-etal-2025-quest2dataagent,
    title = "{Q}uest2{D}ata{A}gent: Automating End-to-End Scientific Data Collection",
    author = "Yang, Tianyu  and
      Liu, Yuhan  and
      Alosious, Sobin  and
      Brown, Ethan A.  and
      Rohr, Jason R.  and
      Luo, Tengfei  and
      Zhang, Xiangliang",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.36/",
    pages = "500--514",
    ISBN = "979-8-89176-334-0",
    abstract = "Scientific research often requires constructing high-quality datasets, yet the current workflows remain labor-intensive, and dependent on domain expertise. Existing approaches automate isolated steps such as retrieval or generation, but lack support for the full end-to-end data collection process. We present Quest2DataAgent, a general-purpose multi-agent framework for automating scientific data collection workflows. Given a natural language research question, it decomposes tasks into structured subtasks, retrieves relevant data using hybrid strategies, evaluates dataset quality, and generates visualizations through a conversational interface. We demonstrate its flexibility in two domains: EcoData for ecological research and PolyData for polymer materials. Both systems share the same core architecture but operate over distinct datasets and user needs. Human evaluations show that Quest2DataAgent significantly improves data relevance, usability, and time efficiency compared to manual collection and tool-assisted baselines. The framework is open-source and extensible to other domains."
}