I’m thinking about a talk session that explores the nature of faculty development programming in digital humanities and pedagogy, comparing the standard workshop model (presentation/facilitation of content/training by one or more “experts”) with a THATCamp style unconference model. Have any of you tried/experienced “unconference” style faculty development? The question is raised in part by the arguments presented by Maha Bali and Lee Skallerup Bessette in “Toward A Critical Approach to Faculty Development.” Maha and Lee suggest that

“institutional and professional expectations often dictate that the workshop model remain predominant even when it is not the most appropriate choice. This leads to a complicated balance between what faculty developers know works … and what faculty and administrators expect. We are not suggesting that workshops necessarily use poor pedagogy; just that they, in all their diversity, should not be the prominent form of faculty development, with one or two experts as facilitators.”

So, what would an “unconference” approach to faculty development, particularly with introduction to DH at a small liberal arts college, look like? 

After initially flourishing, many wonderful digital humanities projects languish. There are many reasons for this. Maybe the funding dries up. Maybe key project participants move away. Maybe the technology underpinning the project changes or becomes unavailable. Maybe the initial research question has changed.

Some of these projects merit renewal, continued support and/or preservation. Others may not. Some may benefit from upgraded or new technology. Some may benefit from another round of funding, or an infusion of new/different expertise.

Inspired by my experiences running the Austin Fanzine Project, and participating in a couple of institutionally-based projects, Althea Logan and I are working to define the questions that need to be asked when considering paths forward for these kinds of projects.

Have you been involved with a project of this kind? We want to hear about challenges and opportunities you’ve experienced or considered. What stands in the way of your project’s progress? How could these roadblocks be cleared?

Talk: Metadata

I have been learning about metadata as I get more involved with DH, I know  Dublin Core gets used a lot  (esp because of Omeka) , but what comes after?

(Maybe) Make a list of crash course reading for people who are getting more involved in DH?

What would a DH for humanists class look like?

@roopikarisam that's a recurring theme of this conference over the years; there needs to be a "metadata for humanists" class.

— Spence(r) Keralis (@hauntologist) September 23, 2016

Let’s talk.  In reality, there are numerous obstacles and failures that we encounter along our DH journey.  What can we learn from them, and how can we benefit (and prevent) them in the future?  Sharing these, we can inform and educate others on how ‘not’ to do things.

Talk session: Related to DF talk on “Of Institutions, Initiatives, and the Importance of Regional Academic Communities: Building NYCDH”

After listening to this presentation,  wanted to discuss their methods of keeping their Regional Academic Communities going, and what methods/strategies/structures people are interested in for the Texas Regional DH Community. Link to TXDHC site 

Over the past five years, “data science” has become a major force, as companies strive to gain insight into customer behavior, researchers look for patterns in large collections of data, and educational institutions aim to train the next generation of data scientists. (Rice has recently launched its own data science initiative.) Humanists have rich data to analyze (such as collections of texts, images, media objects, cultural information, etc) and are developing significant data-intensive research projects, but they are also raising important questions about ethics, how to handle absence and ambiguity, and the risk of reductionism.

So what are we to make of data science in the (digital) humanities, and what can humanists and cultural heritage professionals contribute? What do humanities scholars and cultural heritage professionals need to know about data science? What would a humanities data science course look like? In this Talk/Make session, I’d like to collectively sketch out a humanities data science syllabus in order to articulate key questions/themes and get started imagining a potential course. Potential models include Lauren Klein’s LMC 3206: Studies in Communication and Culture: Data and Miriam Posner’s DH 101.

Talk session proposal

As we do more and more long term, large scale projects I am curious about how different institutions are managing them.

Bring an example of a project you participated in (in planning stages, in process or completed) and what strategies you use to facilitate them. What strategies/workflows policies worked? What did not? How critical  is Project Management to DH?

(possible Teach) I co-presented at DF on using MOUs, and would be happy to discuss further

This goes in the Talk about Teaching category. I’m interested in discussing and seeking suggestions on a behind-the-scenes lesson from my corpus linguistics class, which involves students working on an interactive OCR task. The assignment has evolved as I’ve worked on ways for them to understand why OCR works and doesn’t work, and how to better know the tools they use for digitizing texts. In our departmental computer lab we use ReadIRIS in the “learning” mode, which is meant to help the OCR learn the characteristics of your particular text, but it can be also be deployed to help humans see how the computer makes its recognition choices. I like to provide a particularly messy scanned text for them to work on, in conjunction with an easier text page of their choice. The intended outcome is for students to have hands on time OCRing, and then produce a reflective write-up comparing their OCR experiences and giving their hypotheses about what helps digitization work best. The goal is for them to not just learn to use a piece of software, but to speculate on how different tools give the output that they do. How do your colleagues or students play with OCR? What’s worked well or poorly as you’ve all learned to capture texts? Bring along your tales and teaching tips!