Time Magazine Archive (1923–1930)

This is the complete pre-1930 archive of Time magazine, one of the world's most famous news magazines, founded by Henry Luce and Briton Hadden. **58,970 rows** of clean, structured text beginning with **Volume 1, Number 1, March 3, 1923**. **What this data does for your model:** - Your model learns authentic 1920s American journalism from the very first issue of Time magazine. - Your model retrieves original reporting on Harding, Coolidge, Congress, the Supreme Court, and national affairs as they unfolded. - Your model trains on actual banking news, bond offerings, veterans' bonus debates, and the "farm bloc" legislation of the Roaring Twenties. - Your model understands the voice of 20th‑century news writing; concise, opinionated, and influential. **Includes:** Volume 1, Number 1 (March 3, 1923) through 1930. **What's inside:** - Volume 1, Number 1 - the first issue of Time magazine - American politics - Harding, Hughes, Underwood, Bonar Law - Congress, Supreme Court, and national affairs - Banking, bonds, and financial news - Veterans' bonus, farm bloc, and legislation - "Uncle Joe" Cannon and the Old Guard - World Court and international relations **Perfect for:** - LLM fine-tuning on 20th-century American journalism - Political science and history research - Media studies and journalism history - Digital humanities and cultural history **Format:** Snowflake-native JSONL with columns: ISSUE, TITLE, AUTHOR, TYPE, TEXT. Fully cleaned, bias-audited, and ready for AI training. *From the first issue in 1923 through 1930 -the magazine that changed American journalism, now ready for AI.* <p><br/></p> **Sample Cortex Agent Prompts:** <p><br/></p> **Analyze the historical progression of global geopolitical events, presidential leadership, and 20th-century socio-cultural milestones as documented in this iconic weekly news archive to support the training of domain-specific AI models focused on historical reporting and international affairs.* <p><br/></p> *Extract longitudinal trends in narrative journalism, public opinion shaping, and the evolving coverage of key historical figures from this corpus to assist in the creation of RAG applications for historical trend analysis and political research. <p><br/></p> *Evaluate the expert discourse on international relations, economic shifts, and the advancement of mass media standards within this dataset to provide a foundational baseline for NLP tasks concerning the progression of American news culture throughout the 20th century. <p><br/></p> <p><br/></p>

原始信息汇总

Time Magazine Archive (1923–1930) 数据集概述

数据集基本信息

• 数据集名称：Time Magazine Archive (1923–1930)
• 提供商：Devin Media Corp.
• 数据量：58,970 行
• 时间范围：1923年至1930年
• 数据格式：Snowflake-native JSONL
• 数据列：ISSUE, TITLE, AUTHOR, TYPE, TEXT
• 数据状态：完全清洗、经过偏见审计、可用于AI训练的结构化文本
• 更新频率：每年
• 交付方式：安全共享

数据集内容描述

• 这是《时代》杂志1930年之前的完整档案，始于1923年3月3日的第1卷第1期。
• 内容涵盖美国政治（哈定、休斯、安德伍德、博纳·劳）、国会、最高法院、国家事务、银行、债券、金融新闻、退伍军人奖金、农业集团、立法、“乔大叔”坎农及保守派、世界法院和国际关系。

适用场景

• 针对20世纪美国新闻业的LLM微调
• 政治学和历史研究
• 媒体研究和新闻史
• 数字人文和文化史
• 机器学习：用于领域特定的LLM微调、政治术语提取和媒体NLP
• 真实世界数据：用于研究和分析的历史记录政治事件、国会进程和国家事务
• 生命科学商业化：支持新闻和政治科学研究

数据字典

• 表名：TIME_CORPUS
• 列包括：ISSUE, TITLE, AUTHOR, TYPE, TEXT

使用示例

1. 查看元数据文档 sql SELECT TITLE, TEXT FROM TIME_CORPUS WHERE TYPE = metadata LIMIT 5;

2. 搜索政治相关内容 sql SELECT ISSUE, TITLE FROM TIME_CORPUS WHERE TYPE = article AND TEXT ILIKE %president% OR TEXT ILIKE %congress% OR TEXT ILIKE %senate% LIMIT 10;

3. 按类型统计行数 sql SELECT TYPE, COUNT(*) FROM TIME_CORPUS GROUP BY TYPE;

定价信息

• 产品名称：The Time Century Collection
• 获取方式：通过“GetUnlock New Insights”获取

提供商其他数据集

• The Canadian Nurse Archive (Pre-1930)
• Medical Repository Archive (1797–1824)
• Medical and Surgical Reporter Archive (1858–1930)
• JAMA Surgery Dataset 1911 to 1930 Cleaned Structured AI Ready
• The Aeronautical Journal 1897-1930
• American Journal of Orthodontics (1915-1930)

分类标签

• AI & ML
• Life Sciences Commercialization
• Machine Learning
• Real World Data (RWD)

提供商信息

• 公司：Devin Media Corp.
• 专注领域：为AI训练提供优质历史数据
• 数据特点：全面、来源可追溯、经过偏见审计、1930年之前的出版物和档案，经过专业清洗和结构化，适用于机器学习应用。
• 数据领域：医学、金融、时尚、法律、文化
• 包含出版物：Journal of the American Medical Association (JAMA), Time Magazine, Billboard Magazine, Vogue, House & Garden, The New York Post等。
• 数据集通用特性：
  • 1930年之前，经验证为公共领域/无版权
  • 经过专业OCR和深度清洗
  • 来源可追溯且经过偏见审计
  • 格式化为JSONL，适合AI使用
  • 通过安全API交付（无文件下载）

联系方式

• 销售：[e-mail]
• 支持：[e-mail]

法律与条款

• 法律条款：标准条款

搜集汇总

背景与挑战

背景概述

该数据集包含《时代》杂志1923年至1930年的完整存档，共58,970行清洁结构化文本，覆盖美国政治、金融、国际关系等领域的原始报道。它适合用于大语言模型微调、政治学与历史研究，以及媒体与数字人文学科分析。