Since this is an introductory TEI tutorial, it's possible that this is your first introduction to digital humanities as a field, or that you are just beginning to understand the discipline as a whole. So let's take a minute to situate TEI within the broader range of digital scholarship in the humanities.

This is really a rudimentary cloud of terms and subdisciplines that you may come across in DH. They are separated out into two major categories. The red category towards the bottom, is Making, and the blue categories are those that are less obviously about Making. Many definitions of DH will maintain that if you're not making something, some product, that you're not doing DH. This is, obviously, controversial. Fields that, for instance, derive from New Media Studies, will institute reading practices that are aware of the technological and material underpinnings of the texts they work with, but will not necessarily be interested in producing new objects beyond the objects that their criticism is housed in. And, for that matter, we can make the argument that all work or all labour is a form of making, and that criticism can be the product of a critical labour.

It is also important to point out that the Making category also supports what can be seen as a reading practice. Digital Textual Studies encompasses reading approaches like computational analysis or algorithmic reading, both of which depend on finding patterns in text and producing data that can be analysed. But if we get narrower into Digital Textual Studies, we can see that it narrows into Digital Textual Editing, and this is really where we start to see the utility of something like the TEI.

Markup is a way of describing something that exists in text, and we all do markup all the time, whether we're aware of it or not. Any time you write in the margins of a book, it's a form of markup. Anytime someone publishes an editorial edition of a book, with footnotes, or information on textual variants, for instance, they rely on a markup system. XML is a descriptive markup language—that's a term that we'll return to, descriptive—that primarily supports technological understandings of editing. And TEI is a flavor of XML. So this small slice, which would be even smaller if we were to fill in the more granular concerns of other disciplines, is what we're dealing with today.

But why is this small slice the thing that marks a lot of people's first exposure to DH? Well, for a couple of reasons: the first is that TEI encoding is relatively accessible. You should be able to continue a coding practice from what you learn today; and markup, since we do it all the time, is pretty intuitive. It's a way that we're used to engaging with texts. The second, and maybe the more compelling reason, is that TEI is among the most well-established technologies in the field. It has a robust governing body around its usage, and it's been around long enough to produce some innovative uses of the technology. So what you're learning here is in many ways a standard practice in the discipline, and a way to start to understand DH approaches the kinds of data we come across in text.

This tutorial is about representing research objects, generally. Although our practical emphasis is on representing them in a digital form, our scope actually expands beyond just representing them digitally.

Instead, we are really trying to represent the salient informational features of the research materials that concern us. You can see in this image a whole range of features that a researcher might be concerned with in representing the document on the slide. For example, one may be interested in the references to people and the relationships between them, or in describing the physical document itself.

Additionally, we will interrogate how we understand those features and how we derive meaning from them. What aspects of their nature do we need to express in order to have a useful representation of the object? For example, If your set of documents records events that are described using three different calendar systems, how do you represent the recorded dates, while still creating a regularized version that allows for analysis?

The TEI is not an attempt at answering those questions, but is rather a tool for expressing our answers to those questions. This is why this tutorial should not be understood only as a discussion of the digital.

The digital model of our research materials sits within a larger ecology that also involves other kinds of digitally mediated activities, like publication.

In fact, we can create many different kinds of derived models, or different kinds of output, all from the same source. We do this using automated transformations (tools like XSLT), and we can control their appearance through CSS stylesheets and other formatting processes. As you can see, there are several different formats we can transform our TEI into, not just HTML.*

But it's important to remember that all of these different outputs depend on the existence of the original analytical model and its descriptive power. It is important to note that this original analytical model, represented with TEI, describes deep ideas about the structure and content of documents, not just facts about their appearance.

* To learn more about how to transform and publish the TEI, see our Transformation and Publication Primer.

(If you've already read through our "Introduction to XML" slide set, you can skip this slide)

In text encoding, all of the concepts that underlie our modelling come from us: our disciplines, theoretical frameworks, culture, etc.

XML defines a syntax for text encoding. It is a method for distinguishing our markup from the content that we are marking up.

The TEI provides the specifics of the actual encoding language. It translates our concepts into XML, and provides a controlled vocabulary that we use in marking up the text.

Let's take a look at the process for modelling and mapping our documents.

In creating a TEI document, we are identifying and selecting pieces of the source document. We then represent these pieces as distinct elements in our TEI file: that is, distinct chunks of data with distinct boundaries. The example on this slide shows the how the markup mirrors the structure of the original text. The structural relationships are similar as well. As you can see, we have scenes within acts, paragraphs within speeches, etc.

Drama is a perfect use-case for why TEI is useful for scholars. If we take a look at HTML, another common markup language, we see why the choice of markup systems is important for a project. HTML is a great markup language for publishing on the web. If the primary goal of your project is to get your editions published, you may be wondering, Why TEI? Wouldn't HTML be easier? This slide is the perfect illustration of how more complex markup systems allow for fuller analysis and representation.

As you probably noticed, we're mapping multiple TEI elements onto a single HTML element. For example, speeches, speakers, stage directions all get the catch-all p element (or the HTML paragraph). Nor is HTML modelling the nested structure of the play document in the same way the TEI is. So there is no way of associating a speaker tag with the speech, which we do in the TEI with the sp tag. The reason we lose so much information in HTML is because HTML isn't trying to model plays, it's trying to model a web page.

The TEI Guidelines provide a representational system—a system of notation through which we can create digital representations of our research materials.

The TEI exists to make it possible for us to represent research materials in a way that is sustainable, shareable, and above all, analytically rich.

It may be useful here to unpack those terms. The TEI is sustainable because it is standards-based and not dependent upon specific software or hardware. It is shareable because it provides conditions for mutual intelligibility; it serves as a lingua franca that is shared by the entire research community. And it is analytically rich because it provides a large vocabulary for describing and analysing humanities content.

Additionally, the TEI focuses our attention on translating our observations about texts into formal information. In this regard, the TEI allows us to do many things that a printed image or simple transcription of the page would not. When we do TEI encoding, different things about a text become legible (depending on how we encode). For instance, we can look at the array of genres a text contains, the people and places contained within a document, the way it quotes and cites other works, or how it has been revised over time. The TEI focuses our attention in a very particular way on the translation of observations about texts into formal information.

Languages like the TEI serve as a kind of intermediary between the brute stuff of the world and the formal analytics of a research process. The TEI recognizes that not all documents are going to exactly follow the models that we set up for them, so it navigates between creating a highly-constrained idealized model of the document, and a loose model in which anything is possible. Think of it as negotiating between prescriptivist and descriptivist grammar. So, based on research aims, we select the parts of the text that are of interest to us. We then describe them using a set of formal descriptors. Then we regulate and constrain (to some extent) our descriptions according to our expectations about genre and document structure.

So although it looks as if what we're doing here is reproducing a text, we are, in fact, selectively and strategically modelling it. We're creating a surrogate or proxy, which functions sort of like a different projections of a map: each with selective and useful omissions and distortions.

Here it may be useful to sum up what we've learned about the TEI thus far, and provide a little background on the nature of the TEI.

The TEI is a markup language, specifically a language for modelling digital information. In the case of the TEI, we are modelling information about humanities texts and other research materials.

The TEI is developed and maintained by an international consortium, and it is free and open-source. The community that uses the TEI also has the responsibility for developing and expanding the TEI (this includes you!!).

Even though it has been around for over 20 years, it is still growing and developing. It is both a standard and an ongoing research effort.