DHBSI 2017 (August 14th - August 18th) detailed schedule!

Instructor profiles available!

Register for the Digital Humanities at Berkeley Summer Institute!

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Structuring and Annotating Data Archives for Analysis

Instructors: Josué Meléndez Rodríguez, Shelly Steward
 
This track is designed for humanists who are new to qualitative research. We will start with a basic introduction to different qualitative methods and methodologies. With that common footing, we will explore our own philosophical orientations to knowledge production, a process relevant to all types of research. A significant amount of time will then be dedicated to reviewing the steps of the qualitative research process, especially coding and analysis, with an emphasis on introducing qualitative data analysis (QDA) software. Participants will get an overview of different software packages and see how a project is carried through digital analysis from beginning to end. This track is meant to provide breadth rather than depth. Participants will leave with an understanding of the overall qualitative research process, familiarity with qualitative coding and analysis tools, and knowledge of how these can apply to their own research.
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Data Workflows and Network Analysis

Photo: Chris Church demonstrates GephiInstructor: Chris Church

This workshop will discuss methods of retrieval, data set creation, cleaning, and visualization for network data. This course will explore what network data is, how it is structured and created, and how visualizations can be used for analysis, with a particular focus on how network analysis can be used in the humanities. Participants will learn how to use tools like OpenRefine for cleaning and transforming data and then will visualize data using Gephi, an open source tool for network analysis. Finally, students will prepare their network graphs for publication using Inkscape. No prior knowledge is needed. Students will have the option of working with either a course data set or their own data. Christopher Church, Assistant Professor of History and Co-Director of the Nevada Center for Data and Design in the Humanities at the University of Nevada, Reno, will lead this track.

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Maps for Humanists: an introduction to geographic data, visualization, and analysis

Photo: Patty Frontiera teaching "Geospatial Analysis"Instructors: Patty Frontiera, Adam Anderson, and Susan Powell

This workshop will cover the basics of working with geospatial data and tools in digital humanities projects. Students will work through a sample project as a means for learning about and getting practice with:

  1. Geospatial data fundamentals - concepts, data types, tools, and methods

  2. Geospatial data management - data modelling and project design

  3. Geospatial data creation - georeferencing, geocoding, digitizing

  4. Geospatial data integration & processing - transformations and operations on the data to prepare it for analysis (data cleaning activities) and to answer basic questions. Creating linkages between data sets based on common attributes or location.

  5. Geospatial data visualization - static and interactive (web) map creation and sharing these maps on the web

  6. Geospatial data analysis - basic spatial queries and spatial overlay

Students are encouraged to bring their own data and will have ample opportunity to receive one-on-one consultation on their project.

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Computational Text Analysis

Photo: Teddy Roland teaching "Computational Text Analysis"Instructor: Laura Nelson and Teddy Roland

Increasingly, humanity’s cultural material is being captured and stored in the form of electronic text. From historical documents, literature and poems, diaries, political speeches, and government documents, to emails, text messages, and social media, students from the humanities now have access to immense amounts of rich, and diverse, text. This workshop will introduce students to cutting edge methods of analyzing and interpreting digitized text, in order to explore questions fundamental to the humanities. The ultimate goal is to encourage students to think about novel and creative ways they can apply these techniques to their own data and research questions, and to provide the skills necessary to apply the methods in their own research. We will use the open source (and free!) programming language Python and the Jupyter platform. We will also provide demonstration corpora.

Topics Covered:

  • Introduction to Python for text analysis
  • Principles of Natural Language Processing
  • Identifying important and distinguishing words
  • Dictionary methods
  • Text classification
  • Exploratory text analysis using topic modeling and/or word2vec

Students are strongly encourged to complete this brief tutorial to learn the basic syntax of the Python programming language prior to attending the Summer Institute.

Co-sponsored by the Berkeley Center for New Media.

 
 
 
 
 
 
 
 
 
 
 
 
 
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