Under construction, 12 February 2017
This page is likely to be messy and incomplete. Check back later.
Some possible definitions:
To be honest, I have very little interest in discussions about what DH is. What interests me what the sorts of questions and approaches shared within the DH community allow me to do.
I’m also suspicious of supposed ‘revolutions’, particularly when they’re driven by technology. It’s important to remember that DH doesn’t supplant traditional critical skills, it offers a broader landscape in which to apply them.
So what makes DH different? For me it’s:
The tools, techniques, projects, and examples I’m going to introduce you to today all relate back to these ideas in some way. It won’t be a comprehensive overview of the Digital Humanities, but hopefully it’ll give you a taste of what’s possible and inspire you to explore further.
I like to start off workshops with a quick round of Headline Roulette both because it’s fun (I hope) and because it gets us thinking about different ways we might use and explore cultural heritage data. In this case, Headline Roulette is drawing information about digitised newspaper articles from the Trove API.
This page can be annotated using Hypothes.is. As we go along you can add notes, reminders, links or clarifications. It’s easy!
Just click on the tab that appears on the right of the screen and log in. You can either comment on the whole page or highlight a section of text to annotate.
Data is everywhere, and as librarians I probably don’t have to tell you about things like data.gov.au or Research Data Australia. And then there’s the growing number of APIs and datasets being released by cultural organisation – like the Metropolitan Museum of Art.
But even APIs require a bit of effort and expertise to get useful data out. And there’s a lot of great data locked in proprietary systems or published in unstructured form on web pages. I spend a lot of time figuring out how to get data out of unfriendly systems. I do it to open the data for reuse, but also because I believe that access is never simply ‘open’. It’s a struggle – and in the process we learn.
Sometimes the text is the data. Using digital tools we can break texts down into their component parts – words, phrases, and parts of speech – and manipulate them. How are certain words used within collections of texts? We can analyse things like occurrance, frequency, and context to better understand the layers of meaning within text.
Sometimes the application of these sorts of computational techniques to text sources is referred to as ‘distant reading’. By aggregating resources and extracting and analysing features (such as word frequencies) we can build big pictures (and big arguments). But what kind of knowledge claims can we really make?
I’m assuming that I don’t have to go on about filter bubbles, search personalisation, and the ways algorithms shape our perceptions of the world in a workshop full of librarians. Nonetheless, the ‘professional hair’ tweet-storm from last year is a useful reminder that search interfaces hide as much as they reveal.
This doesn’t just apply to Google, a large part of my research is focused on understanding what we can’t see – exploring the biases, limits and assumptions that shape our cultural heritage collections.
A number of digital humanities tools and projects have been concerned with how we create and preserve collections. Instead of waiting and hoping that material of historical significance will find its way to cultural institutions we can move quickly to capture it. By doing this we can also attempt to document a broader range of voices and perspectives.
You’ve probably heard of Omeka. It was developed by a DH centre at George Mason University and was inspired by the work they did to create an online archive documenting people’s experiences of the 9/11 attacks.
Computers are getting better at seeing. The things we take for granted, like the ability to recognise a face, are challenging tasks for computer vision. But recent years have brought great advances.
Computers can be taught to find shapes and patterns (like redactions!) within images. Facial detection (finding a face in a photo) is pretty straightforward. This offers interesting possibilities for historians, but the use of such technologies for surveillance also presents political and social challenges.
Why should our work be locked up in pdfs or just presented at conferences? Using digital tools we can create interventions that exist within public spaces, that inhabit social media. We can release our cultural collections into the online spaces people already inhabit.
What happens when we start to pit some of these things together? How can we create new infrastructures for research and collaboration without lots of money?