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On Saturday, November 9, Washington University in St. Louis hosted a lively (and successful?) THATCamp: THATCamp St. Louis, 2013. One of the first sessions combined two suggested topics: “Beginners in the Digital Humanities” and “Subject Librarian DH boot camp,” which I volunteered to co-facilitate with Twyla Gibson. I hope that discussing some of the broad outlines of digital humanities was helpful, but there was a lot to follow-up on. The thin list here leads to pages and resources with pointers to many, many more resources for exploring DH.

Overview of DH

In the “Beginners” session, we discussed some of the broad outlines of digital humanities: the dispute over “hack vs. yack”—the practical creation of tools and resources (especially scholarly digital editions) versus broad theoretical considerations. Within the “hack” camp of DH is another division—not less contentious, but with slightly differing aims and perspectives: scholars involved with scholarly editing (and “digital projects” more broadly) on the one hand, and scholars interested in text mining, on the other. The following are good resources for anyone getting started in DH:

• Patrik Svensson, “The Landscape of Digital Humanities“
• Lisa Spiro, “Getting Started in Digital Humanities“
• The CUNY Digital Humanities Resource Guide—exploding with links

TEI, XML, encoding

Not all XML that might be relevant for faculty members, archivists, librarians and others in cultural heritage organizations is necessarily DH, but certainly deserves mentioning, such as EAD (Encoded Archival Description) for encoding of finding aids, PREMIS for describing objects in a digital preservation environments, and many others! On the other hand, as prevalent as TEI (Text Encoding Initiative) is, it is is also not the only XML standard relevant to DH, as others have grown up out of it, such as the Charters Encoding Initiative (CEI) and EpiDoc, for Epigraphic Document Encoding.

• TEI: Text Encoding Initiative
• A very gentle introduction to the TEI markup language
• TEI Tools
• TEI By Example
• <oXygen/> XML Editor commercial XML editor, but economically priced and probably most widely-used

Text Mining

• Ted Underwood’s The Stone and the Shell blog on Where to start with text mining
• Steve Ramsay, Reading Machines: Toward an Algorithmic Imperative (theory, but also dealing with text mining)
• Drew Conway, Machine Learning for Hackers
• The Metadata Offer New Knowledge (MONK) project

—Andrew Rouner

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Thanks to all who attended and who organized, especially the thoughtful and thorough Olin Library staff.

There’s always some kind of magic at a THATCamp, including the mystery that there will never be enough information to know in advance about all the good sessions, everyone will always miss some good ones, yet that uncertainty is part of what makes it possible for the great sessions to be great. All the possibilities were not figured out in advance.

What I especially like about THATCamp is that the very form is a discovery process. We don’t know what we are collectively best suited to talk about or do until we all show up. So much tacit knowledge can be discovered and shared when people from different institutions, job descriptions, experiences, disciplines, and side interests try to have conversations with each other that are low stakes, speculative, practical, and motivated in the best way.

May there be another THATCamp in the St. Louis region again before too long!

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Relational databases (RBDMS) have a well defined role to play in the digital humanities. It is recognized that the rows and columns of a well-designed database can provide valuable insight to data. However, as datasets continue to move towards the realm of “big data”, the overhead cost of processing complex indexes needed to support defined relationships becomes cost prohibitive. Asking relational databases to generate multifaceted reports over large datasets and perform intensive analytics detracts from the strength of RDBMS. Numerous tools are available today for working with unstructured data. These tools can work in conjunction with your existing tools and provide new insights to data and capabilities for understanding unstructured data (tools such as NoSQL, Hadoop, Graph databases, crowd-sourcing data, etc…). As these tools rapidly evolve, it would be helpful to discuss what solutions may have application to the humanities moving forward.

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If Zane Gray wrote business textbooks:

Obviously, these standards enter into the valley lilies; he knew himself in this country in our ideals and our policies for educating future business executive requires those fine qualities of mind that Oldring’s intimidations of the herd jest as Lassiter did.

If Milton rhymed:

And do they only stand By ignorance?
O Goodness infinite, Goodness immense!
She fair, divinely fair, fit love for Gods!
This Hell then seemed A refuge from those wounds.

If we wanted to review wine, but didn’t actually want to drink it:

Chateau St.Paul Pinot Noir Jamaica Plain 1987 Select
Unless you can put up with ambiguity, you’ll be mortified by this old standby of a Pinot Blanc. Needs cellaring until at least 2001.

Automatic text generation is interesting on a couple of different levels.  First, there’s just the sheer joy of surprise (e.g., a rock-and-roll band name like, “helpless spender and the dented senators”).  And how texts deform under the pressure of (re)generation might say something about the texts themselves.  And lastly, these methods suggest that semantic coherence may be non-computable, or not easily so.

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Among the many cultural heritage materials that are now in digital form are things that were organized as “data” in some sense even before they were digital: reference works, indexes, directories, concordances, censuses, inventories, card catalogues, etc. I’ve worked on several projects that make some use of such materials, and they present interesting challenges, practical and conceptual. If we make a database now from resources created by a historical bureaucracy, how much of that bureaucracy do we still have to negotiate? If others are interested, I would enjoy a conversation that could range from questions of what we can learn from how and why people organized “data” before digital methods, to what we can learn from media studies, and not least to what kinds of practical projects we might imagine. It would be especially interesting to try to bridge archival, library, scholarly, and technological perspectives. Whenever I browse the Internet Archive, for example, I’m struck by how much past effort of predigital “data modeling” (we might call it) is both available and hidden in ways that full-text search and text mining tools don’t begin to do justice to. What else?