标题： 2000年至2025年美国报告的云播种活动的结构化数据集，使用大型语言模型 显示英文标题

标题： Structured dataset of reported cloud seeding activities in the United States (2000 to 2025) using a large language model

摘要： 云催化是一种旨在局部增强降水的天气修改技术，自20世纪40年代以来一直在美国西部使用。 然而，目前尚无可用于大规模分析云催化工作的数据集。 为解决这一差距，我们提出了一个结构化的数据集，记录了2000年至2025年美国报告的云催化活动，包括年份、季节、州、催化剂、用于部署的设备和目的。 我们使用OpenAI的o4-mini大型语言模型（LLM），结合多阶段的PDF转文本转换和响应解析代码，处理了来自国家海洋和大气管理局（NOAA）的836份历史报告以提取数据。 该数据集在所有字段中的人工验证准确率达到94.72%，并且可在Zenodo上公开获取。 我们的结果解决了美国可访问的云催化数据的缺口，并展示了大型语言模型从历史文件中提取结构化环境数据的潜力。 更广泛地说，这项工作提供了一个可扩展的框架，用于从扫描文档中解锁跨科学领域的历史数据。 显示英文摘要

摘要： Cloud seeding, a weather modification technique aimed at locally enhancing precipitation, has been used in the Western United States since the 1940's. However, datasets to facilitate large-scale analysis of cloud seeding efforts are not currently available. To address this gap, we present a structured dataset of reported cloud seeding activities in the U.S. from 2000 to 2025, including the year, season, state, seeding agent, apparatus used for deployment, and purpose. Using OpenAI's o4-mini large language model (LLM), combined with multi-stage PDF-to-text conversion and response-parsing code, we processed 836 historical reports from the National Oceanic and Atmospheric Administration (NOAA) to extract the data. The resulting dataset achieved 94.72% human-verified accuracy across all fields and is publicly available on Zenodo. Our results address the gap in accessible cloud seeding data in the United States, and demonstrate the potential for LLMs to extract structured environmental data from historical documents. More broadly, this work provides a scalable framework for unlocking historical data from scanned documents across scientific domains.