Held August 3-4, 2017
Loyola Notre Dame Library
Loyola University Maryland
Slides
http://bit.ly/loyola-omeka-2017
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.
- 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?
- 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?
- 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?
- 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?
- 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?
- Does the tool allow for publishing interactive versions online? Is an interactive display necessary to communicate the relevant ideas or conclusions?
- 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?
- If the tool is online:
- Can multiple people collaborate on a project? Or does the tool require a single account?
- How will the account(s) be maintained over time?
- What is the long-term viability of the organization that offers the tool?
Building and Working with a Dataset
Preparing to build a dataset
- 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.
- Identify the parts of the text or data set you will gather information from.
- Identify what aspects of context you and potential audiences need to know.
- Consider principles of selection and use—either your own or the data creator’s principles.
- Are there any historical or present-day factors that limit the use, “wholeness,” or viability of the data set?
- Is the data fact-based or speculative?
- Identify and familiarize yourself with philosophical or political leanings connected to the data. What are the underlying assumptions and exclusions of the data?
- Create an editorial log to document any of your decisions about what to include, change, or introduce.
Building and working with a tabular dataset
- Design your spreadsheet columns & rows. https://data.research.cornell.edu/content/tabular-data
- Add to the data to your spreadsheet.
- Create a strategy for revising the datasets to ensure consistency, integrity, and that it will support the relevant scholarly questions.
- Consider using a tool like OpenRefine.
- Cleaning Data with OpenRefine by Seth van Hooland, Ruben Verborgh, and Max De Wilde
- Getting Started with OpenRefine by Thomas Padilla
- Analyze data.
- Update editorial log with critical response about research outcomes.
- Rinse and Repeat.
Suggestions for Digital Humanities Tools & Resources
Content Management Systems
Wikis (ex. MediaWiki or Wikipedia)
Social media
Maps with points
Leaflet (steeper learning curve)
Maps with timelines
Convert addresses/place names to GPS coordinates
Graphs
D3.js (steeper learning curve)
Timelines
Text Analysis
Add’l Tool Directories
DH101 Resource Guide by Miriam Posner
Catalog of tools by Alan Liu
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