Held August 3-4, 2017
Loyola Notre Dame Library
Loyola University Maryland



Brief Schedule

Day 1

9:00 – 10:15 – Introductions and Example Projects

10:15 – 10:30 – Break

10:30 – 12:00 – How did they make that?

12:00 – 1:00 – Lunch

1:00 – 2:00 – Telling Stories with Personas

2:15 – 2:30 – Break

2:30 – 4:00 – Building Blocks of Omeka

Day 2

9:00 – 10:15 – Customizing and Expanding Omeka

10:15 – 10:30 – Break

10:30 – 12:00 – Teaching with Omeka

12:00 – 1:00 – Lunch

1:00 – 2:45 – Maps & graphs in Omeka

2:45 – 3:00 – Break

3:00 – 4:00 – Next Steps

Evaluating Tools

This activity asks you to critically survey a number of aspects of digital tools, including ownership, accessibility and ease-of-use.

  1. Who owns the tool? What is the name of the company, the CEO? What are their politics? What does the tool say it does? What does it actually do?
  2. What data are we required to provide in order to use the tool (login, e-mail, birthdate, etc.)? What flexibility do we have to be anonymous, or to protect our data? Where is data housed; who owns the data? What are the implications for in-class use? Will others be able to use/copy/own our work there?
  3. What materials does the tool require? What are the forms of data that it will accept and how are those represented? Are there any examples of the necessary data?
  4. Does the tool have any possible applications in the classroom? What is the community of practitioners around the tool? Has anyone created tutorials for humanists? How much time does a student/collaborator need to learn to use the tool?
  5. How much complexity does a tool present to online visitors? Is it intuitive or does a visitor need to develop a literacy to understand and use it? What are your support structures for elaborate or low-tech tools?
  6. Does the tool allow for publishing interactive versions online? Is an interactive display necessary to communicate the relevant ideas or conclusions?
  7. How accessible is the tool? For a blind student? For a hearing-impaired student? For a student with a learning disability? For introverts? For extroverts? Etc. Does the toolmaker provide any documentation of their efforts for accessibility?
  8. If the tool is online:
    1. Can multiple people collaborate on a project? Or does the tool require a single account?
    2. How will the account(s) be maintained over time?
    3. What is the long-term viability of the organization that offers the tool?

Building and Working with a Dataset

Preparing to build a dataset

  1. Choose a corpus, document or dataset to analyze. Draft a series of research questions or exploratory statements to direct the selection of your source materials.
  2. Identify the parts of the text or data set you will gather information from.
  3. Identify what aspects of context you and potential audiences need to know.
  4. Consider principles of selection and use—either your own or the data creator’s principles.
  5. Are there any historical or present-day factors that limit the use, “wholeness,” or viability of the data set?
  6. Is the data fact-based or speculative?
  7. Identify and familiarize yourself with philosophical or political leanings connected to the data. What are the underlying assumptions and exclusions of the data?
  8. Create an editorial log to document any of your decisions about what to include, change, or introduce.

Building and working with a tabular dataset

  1. Design your spreadsheet columns & rows. https://data.research.cornell.edu/content/tabular-data
  2. Add to the data to your spreadsheet.
  3. Create a strategy for revising the datasets to ensure consistency, integrity, and that it will support the relevant scholarly questions.
  4. Consider using a tool like OpenRefine.
    1. Cleaning Data with OpenRefine by Seth van Hooland, Ruben Verborgh, and Max De Wilde
    2. Getting Started with OpenRefine by Thomas Padilla
  5. Analyze data.
  6. Update editorial log with critical response about research outcomes.
  7. Rinse and Repeat.

Suggestions for Digital Humanities Tools & Resources

Content Management Systems




Wikis (ex. MediaWiki or Wikipedia)

Social media


Maps with points

Google Fusion Tables



Leaflet (steeper learning curve)

Maps with timelines





ESRI StoryMaps

Convert addresses/place names to GPS coordinates



Google Fusion Tables

RAW by Density


D3.js (steeper learning curve)



Timeline Storyteller

Text Analysis


Juxta Commons

Add’l Tool Directories

DH101 Resource Guide by Miriam Posner

Programming Historian Lessons

Catalog of tools by Alan Liu

Data Visualisation Survey

A Tour Through the Visualization Zoo by Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky

Resources for ‘Data Visualisation for Analysis in Scholarly Research’ by Mia Ridge

Tooling Up for Digital Humanities (Stanford)