Apply to the Digital Humanities at Berkeley Summer Institute. Applications will be accepted on a rolling basis.
Introduction to Digital Humanities *new*
This course provides an introduction to digital humanities (“DH”), where humanistic inquiry meets digital technologies. Taking a hands-on approach to the tools, techniques, processes and basic understandings of the digital humanities, we will collaboratively explore ways in which digital methods can integrate with scholarly inquiry, under what assumptions, and to what effects on our reading and writing practices. Discussions and workshops assume no prior knowledge of the field or preexisting technical competencies. Students are asked to bring a laptop.
Specific focus areas:
- DH history, principles, key concepts, issues
- Markup languages and scholarship on the web
- Text analysis and encoding
- Data structures, ontologies, metadata
- Visualization and digital mapping
- Game-based models
Qualitative Data Analysis *new*
- Social science v. humanist uses of qualitative data
- What is qualitative data analysis?
- The basics of MaxQDA
- Taking a content analysis approach
- Taking a discourse analysis approach
- Data visualizations with qualitative data
Data Workflows and Network Analysis
This workshop will discuss methods of data retrieval, cleaning, and visualization. Students will have the option of working with either a course data set or their own data. Students will collect data via webscraping, clean and structure it appropriately with Open Refine, and visualize it using Gephi, an open source network analysis tool. Finally, students will prepare their network graphs for publication using Inkscape. No prior knowledge is needed. Christopher Church, Assistant Professor of History at the University of Nevada, Reno, will return to UC Berkeley to lead this track.
Database Development Using Drupal
In this course students will learn how to create and maintain databases using Drupal, a prominent open source content management system. Drupal databases can be used to build a variety of digital humanities projects, such as digital collections and interactive web maps. This course will cover some important preliminary considerations, such as data structures, storage, and the advantages and disadvantages of various database tools and platforms. Students will study data modeling and other considerations for structuring and storing data, and will discuss the advantages and disadvantages of various database tools and platforms. Working with workshop instructors, students will begin to to build databases for their own projects. Students will have the opportunity to work closely with instructors to develop their data model. Learn more about Drupal.
Geospatial Analysis
This workshop will cover the basics of ArcGIS, a geospatial tool used in both industry and academic research in various environmental and social sciences. Students will be introduced to georeferencing (“spatializing” scanned maps or aerial imagery so that they can be used in a geospatial tool) and geocoding data (the process of determining the geographic location of a place name, zipcode or address).. Students will also meet with the Berkeley GIS & Maps Librarian to discuss working with different geodata formats. This workshop will also preview tools and methods for publishing maps on the web. Students will have ample opportunity to receive one-on-one consultation on their project. With sufficient student interest, exercises may also be available in QGIS, the free open-source equivalent to ArcGIS.
Computational Text Analysis
This series of workshops will focus on quantitative approaches to analyzing language in a large body of text. An example body of text, provided by the instructor, will be used to illustrate several types of analysis. Participants will begin with descriptive statistics (i.e. word frequencies, co-occurrence of terms) and then expand to methods such as content analysis (i.e. identifying topics and themes across texts) and dictionary methods (approaching the text with a related series of terms). We will discuss some of the methodological concerns that accompany these computational methods. We will consider case studies from the disciplines of literature, political science, and sociology.
To apply these methods to your own collection of texts, you may need to consider digitization, OCR, and data cleaning. Please consult with us to discuss the feasibility of applying these methods to your own research.
- Acquiring and Preprocessing Texts
- Dictionary Methods: Measuring Weighted Word Usage
- Methods for Finding Discriminating Words
- The Vector Space Model and the Geometry of Text (Principal Components, Multi-Dimensional Scaling, Most Similar Texts)
- Clustering Methods
- Topic Models
- Supervised Learning
- Quantifying Style: Grammar, Alliteration, and other Poetic Concerns
- Verification
- Regular Expressions and Word Searching
- Ted Underwood. “Seven ways humanists are using computers to understand text.” The Stone and the Shell, June 4, 2015
- Grimmer, Justin, and Brandon M. Stewart. "Text as data: The promise and pitfalls of automatic content analysis methods for political texts." Political Analysis 21.3 (2013): 267-297.