DataParasite 实现可扩展和可复用的在线数据整理
DataParasite Enables Scalable and Repurposable Online Data Curation
AI总结：
DataParasite通过轻量级配置和自然语言指令实现高效、可复用的在线数据收集，显著降低数据整理成本与技术门槛。
摘要：
许多计算社会科学问题依赖于从异构在线来源收集的数据集，这一过程通常劳动密集、成本高且难以复现。近年来，大语言模型的进展使网络搜索和结构化提取成为可能，但现有系统往往不透明、不灵活或不适合科学数据整理。本文介绍DataParasite，一个开源、模块化的可扩展在线数据收集管道。DataParasite将表格整理任务分解为独立的实体级搜索，通过轻量级配置文件定义，并通过共享的、任务无关的Python脚本执行。关键在于，相同的管道可以仅通过自然语言指令重新用于新任务，包括没有预定义实体列表的任务。我们对计算社会科学中的多个经典任务进行了评估，包括教职员工招聘历史、精英死亡事件和政治职业轨迹。在所有任务中，DataParasite实现了高精度，同时将数据收集成本降低了一个数量级，相对于手动整理。通过降低在线数据整理的技术和劳动力障碍，DataParasite为计算社会科学及其他领域的可扩展、透明和可重用的数据整理提供了实用基础。
英文摘要：
Many questions in computational social science rely on datasets assembled from heterogeneous online sources, a process that is often labor-intensive, costly, and difficult to reproduce. Recent advances in large language models enable agentic search and structured extraction from the web, but existing systems are frequently opaque, inflexible, or poorly suited to scientific data curation. Here we introduce DataParasite, an open-source, modular pipeline for scalable online data collection. DataParasite decomposes tabular curation tasks into independent, entity-level searches defined through lightweight configuration files and executed through a shared, task-agnostic python script. Crucially, the same pipeline can be repurposed to new tasks, including those without predefined entity lists, using only natural-language instructions. We evaluate the pipeline on multiple canonical tasks in computational social science, including faculty hiring histories, elite death events, and political career trajectories. Across tasks, DataParasite achieves high accuracy while reducing data-collection costs by an order of magnitude relative to manual curation. By lowering the technical and labor barriers to online data assembly, DataParasite provides a practical foundation for scalable, transparent, and reusable data curation in computational social science and beyond.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
