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William Still’s Underground Railroad Data, 1853-1861

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DataCite Commons2024-10-24 更新2025-04-15 收录
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From 1853 to 1861, nearly 1,000 enslaved freedom seekers came to Philadelphia, Pennsylvania, to seek refuge with the Black abolitionist William Still in his capacity as a leading member of the Pennsylvania Anti-Slavery Society. Although the Underground Railroad is popularly remembered as a secret communications network between enslaved people and their abolitionist allies, Still bucked this trend by maintaining extensive records of the freedom seekers who crossed paths with him. Documenting extensive data such as names, age, dates of escape, method of escape, names of enslavers, and hometowns, Still complied what is arguably the most comprehensive record of Underground Railroad activities in the United States before the Civil War. While scholars James McGowan and William C. Kashatus produced an appendix of this data in their 2021 book William Still: The Underground Railroad and the Angel at Philadelphia, Still’s data has never been made freely available online as a comprehensive dataset. This dataset updates, digitizes, and makes accessible William Still’s Underground Railroad data for scholars and students alike.

1853年至1861年间,近千名寻求自由的受奴役者(enslaved freedom seekers)前往宾夕法尼亚州费城,投奔身为宾夕法尼亚反奴隶制协会(Pennsylvania Anti-Slavery Society)核心成员的黑人废奴主义者威廉·斯蒂尔(William Still),寻求庇护。尽管大众普遍将地下铁路(Underground Railroad)视作受奴役者与废奴主义盟友间的秘密通信网络,但斯蒂尔却打破这一惯例,详细记录了每一位与他产生交集的逃亡者的相关信息。其记录涵盖姓名、年龄、逃亡日期、逃亡方式、奴隶主姓名及家乡等多类详实数据,编纂出了美国内战前堪称最全面的地下铁路活动记录。尽管学者詹姆斯·麦高恩(James McGowan)与威廉·C·卡沙图斯(William C. Kashatus)在2021年出版的著作《威廉·斯蒂尔:地下铁路与费城的天使》(William Still: The Underground Railroad and the Angel at Philadelphia)中为该数据整理了附录,但斯蒂尔的相关数据此前始终未以完整数据集的形式免费上线公开。本数据集对威廉·斯蒂尔的地下铁路相关数据进行了数字化更新与整理,面向学者与学生群体开放免费访问权限。

提供机构:
Harvard Dataverse
创建时间:
2024-09-04
搜集汇总
数据集介绍
William Still’s Underground Railroad Data, 1853-1861 数据集图片
背景与挑战
背景概述
该数据集记录了1853年至1861年间近1,000名寻求自由的被奴役者通过地下铁路逃往费城的信息,由黑人废奴主义者William Still详细收集,包括姓名、年龄、逃跑日期和方法等关键数据,是内战前美国地下铁路活动最全面的记录之一,现已数字化并免费在线提供。
以上内容由遇见数据集搜集并总结生成
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