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Hello, THATCampers! In the spirit of one session/one tool THATCamp production, this document is the outcome of a discussion about how to prep data and choose a visualization tool for humanists who don’t have direct access to high-level database and programming skills.

We welcome your comments, suggestions and feedback. Hopefully, you’ll find this step-by-step planning document useful as a hand-out to communicate with colleagues and/or students who are just getting started in the research process for projects that include data visualization. Additionally, there is a list of visualization tools collected in the Google Doc that this session’s attendees produced.

Thanks to all of the session attendees for lively discussion and great contributions!

Steps to take for managing data to make visualization easier:

1. Start with the argument you’re making and how that argument could look on paper
   1. Start thinking about the visualization before you start taking notes
   2. Find visualization tools to match your paper mockup
   3. Determine feasibility of collecting data for that visualization tool/type of visualization
      1. This will depend on your skill level and comfort with technology.
   4. Understand the limitations of the visualization tool (e.g. single date required when data is often in date ranges)
2. Consider end result
   1. Data exploration? Out of the box software is best used for exploring data
   2. Data presentation in argument form? Building an argument in graphic form will probably require (but check with @tjowens about facets in Recollection)
   3. There will be a $0, $1,000, $10,000, $50,000, $100,000 and $1,000,000 version of this. First make the one that costs nothing and think about how you would scale up if it turned out to be particularly interesting.
      1. If you compromise and go a low-cost version, don’t forget the idealized version you wanted in the first place.
3. Find the least complex tool you need for the job of data collection
   1. Excel is a useful tool, but data with a lot of repetition is ideally expressed in a relational database.
      1. Excel can auto-complete entries, but auto-complete can also create inaccurate data
4. Start with small data sets, and iterate often
   1. Simplify data w/ data dictionary
   2. Use visualization as data remediation

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Teacher-scholars unite! I’ve been testing some possible applications of Omeka archives and Zotero as collaborative tools organizing the development of literary research methodologies classes, and I’d like to take the wonderful opportunity of THATcamp to begin developing the structure and content of project I see as The Next Step. I’d like your help to discuss, plan, and/or block out a template for a full-class, full-term student project that works toward researching, annotating, and encoding a small number (perhaps just one per term?) of thematically-selected texts in our shamefully neglected special collections room. Ideally, this project would therefore include study of the texts themselves, research about their material and digital existences (using the ESTC, Google Books, and something like Eighteenth-Century Book Tracker)  a basic practical/theoretical framework for DH, collaboratively writing a useful and accessible overview and producing an XML version of the text. Each term or year, students and faculty would work together to select, create, and grow the entries according to a broader thematic logic that can expand over time, based on the strengths of the collections. I’d like to use this template as a basis for a grant application that would allow the project to grow and, ultimately, link faculty, students, and resources at area institutions.

I think this would be a viable model for an advanced undergraduate seminar, and it has the benefit of drawing together a variety of practical and theoretical facets of the digital humanities. Some questions to consider include how we can best design the arc of the class? What specific parts of the project would have as their goal which practical or conceptual outcomes? What are the technological hurdles to be 1.) aware of, 2.) avoided, or 3.) embraced? What should the Omeka site look like/allow, in order to help the project grow over time? How might faculty help students approach the text encoding portion of the project? What are the most useful introductory text-based sources providing a theoretical framework for such a practical project? And what might steps after The Next Step look like?