【转译器】数字人文研究方法

英文系Derek Miller教授哈佛大学2016秋季学期

课程简介

这门课程既是一门数字人文内涵的研究课程，也是探索其学习方法的研讨会。我们将着重探讨数字人文在人文问题研究上的应用，而非该领域内教育学或档案学的创新。开这门课程的一个重要信念是，我们并非为了肯定数字人文的合理性——事实上，我们还会安排不少时间对其进行批判研究——而是由于从知识的角度解读数字人文理念及其实践的必要性。为此，我们关于数字人文方法的探索过程将包含相当一部分计算机编程内容（但你并不需要事先掌握这些技能）。我们的目的不是培养学生的编程能力，而是向你们展示当人们在思考与计算机相联系的人文问题人们的思维过程经历了哪些变化。

教学目标

在课程结束时，学生将能够：

 • 了解人们如何利用计算机组织、处理和分析信息

 • 运用数字人文领域重要的技巧和方法，并能做出评论

 • 明确并探索适合数字化方法的研究问题 • 向广大学界阐述数字人文工作的成果

教学计划

9.7 | 导论：我们都是数字人文人主题：课程概论；课程任务和技术性要求的讨论；课堂实验资料说明
9.14 | 数字人文的历史与意义主题：数字人文过去至现在内涵的演变；数字人文的契机；对数字人文的一些批判演示：虚拟设备；UNIX命令行；Jupyter Notebook（交互式笔记本）；Git（分布式版本控制系统）；SSH（安全外壳协议） 【专题】数据：9.21 | “数据”意味着什么？/ 建立档案主题：人文学中的数据；探索数字人文档案；数字人文主题中的元素展示：网络与Twitter式数据采集；以BeautifulSoup解析HTML的语法
9.28 | 数据库和本体论主题：人文数据管理；本体论的重要性；数据库的形式演示：阅读Twitter数据；使用MySQL
10.5 | 数据处理和展示主题：统计学与其它谎言；视觉展示的原理演示：用Python（Pandas）的数据结构编程；Python（Matplotlib）图表绘制 【专题】从文本到数据再到分析：10.12 | 文本的语法分析与标记主题：文本处理；语料库构造；标记语言演示：用Tesseract进行光学字符识别（OCR）；用正则表达式（regular expressions）处理文本；文本编码规范（TEI）
10.19 | 文本和语料库分析主题：文本分析的用途；向量空间；主题模型构建中的数学演示：Python中的NLTK（自然语言工作包）；Python中的Gensim（语料库）；Mallet算法
10.26 | 立足于文本的数字人文主题：数字人文中的文本；远读（distant reading） 【专题】地理区域与网络：11.2 | 空间分析              主题：空间的历史；空间定位演示：QGIS（桌面地理信息系统）
11.9 | 网络分析主题：网络理论与分析演示：使用Gephi制作网络可视化
11.6 | 做不做数字人文主题：数字人文目前存在的问题、局限、潜力与优势

阅读资料

 * Debates 2012: Matthew K. Gold, ed. Debates in the Digital Humanities. Minneapolis, MN: University of Minnesota Press, 2012.
 * Debates 2016: Lauren F. Klein and Matthew K. Gold, eds. Debates in the Digital Humanities. Minneapolis, MN: University of Minnesota Press, 2016.
 * CDH: Susan Schreibman, Ray Siemens, and John Unsworth, eds. A Companion to Digital Humanities. Malden, MA: Blackwell Publishing, 2004.
 * NCDH: Susan Schreibman, Ray Siemens, and John Unsworth, eds. A New Companion to Digital Humanities. Malden, MA: Blackwell Publishing, 2016.

附英文通知

Methods in Digital HumanitiesENGL 298dh | Fall 2016
Professor Derek MillerBarker [e-mail]
This course is both a seminar exploring the Digital Humanities and a workshop on methods in DH. We will focus primarily on DH’s applications to research questions in the humanities rather than on the field’s pedagogical or archival innovations. The course is designed with a firm belief not in the righteousness of DH—indeed, we will devote considerable time to critiques of the field—but in the necessity of grappling with its ideas and practices from a position of knowledge about those ideas and practices. To that end, our exploration of DH methods will involve considerable work in computer programming (though you need have no prior knowledge of those skills). We will develop not coding expertise but rather knowledge about what happens to our thinking when we think about the humanities with computers.
COURSE OBJECTIVES
By the end of the course, students will be able to:
•     understand how people use computers to organize, manipulate, and analyze information;•     practice and critique major techniques and approaches in DH;•     define and investigate research questions suited to digital methods;•     explain the results of DH work to a broad scholarly audience.
COMPENDIA ABBREVIATED BELOW
•     Debates 2012: Matthew K. Gold, ed. Debates in the Digital Humanities. Minneapolis, MN: University of Minnesota Press, 2012.•     Debates 2016: Lauren F. Klein and Matthew K. Gold, eds. Debates in the Digital Humanities. Minneapolis, MN: University of Minnesota Press, 2016.•     CDH: Susan Schreibman, Ray Siemens, and John Unsworth, eds. A Companion to Digital Humanities. Malden, MA: Blackwell Publishing, 2004.•     NCDH: Susan Schreibman, Ray Siemens, and John Unsworth, eds. A New Companion to Digital Humanities. Malden, MA: Blackwell Publishing, 2016.  SCHEDULE
9/7 | Introduction: We’re All Digital HumanistsTopics: Course overview; discussion of assignments and technical requirements; introduction to our in-class experimental material.9/14 | Histories and Meanings of DHTopics: What DH was and is; possibilities of DH; some critiques.Demonstration: Virtual Machine; UNIX on the Command Line; Jupyter Notebook; Git; SSH Data9/21 | What does “data” mean?/Making an ArchiveTopics: Data in the humanities; exploring digital humanities archives; elements of a DH project.Demonstration: Web and Twitter Scraping; HTML Parsing with BeautifulSoup9/28 | Databases and OntologiesTopics: Organizing humanities data; importance of ontologies; database formats.Demonstration: Reading Twitter Data; Using MySQL10/5 | Manipulating and Displaying DataTopics: Statistics and other lies; principles of visual display.Demonstration: Working with data structures in Python (Pandas); plotting in Python (Matplotlib).  From Text to Data to Analysis10/12 | Text Parsing and MarkupTopics: Processing texts; generating a corpus; markup languages.Demonstration: Optical Character Recognition with Tesseract; manipulating text with Regular Expressions; Text Encoding Initiative.10/19 | Text and Corpus AnalysisTopics: Uses of text analysis; vector spaces; the mathematics of topic modeling.Demonstration: NLTK in Python; Gensim in Python; Mallet10/26 | On the Status of the Text in DHTopics: Texts in the Digital Humanities; distant reading.  Geographies and Networks11/2 | Spatial AnalysisTopics: Spatial history; geo-referencing.Demonstration: QGIS11/9 | Network AnalysisTopics: Network theory and analysis.Demonstration: Network visualizations in Gephi11/16 | On Doing and Not Doing DHTopics: Problems, limits, potentials, prevalence of DH.
From : canvas.harvard.edu/courses/16573

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