Ok, so not Piers at all, but a little digital quickie on the State of the Union address and responses by the president and a few others. I used the simple text-mining tool Voyant to check out some word frequencies and make a few basic comparisons: To begin, we’ll check out a word cloud of the full transcript of Obama’s speech: What you’ll notice is that most of the words with the highest frequency are, of course, basic words that make our language function: pronouns, conjunctions, articles, prepositions. In order to filter those out, we choose to “edit stop words” for English and we get a cloud that is more reflective of the substance of the speech: Continue reading A Little Digital Reading of SOTU
I sort of hate manuscript stemma. Don’t get me wrong, they have their uses, and they take some incredibly diligent and intelligent work (work that I’m really glad someone else is doing so that I don’t have to). But stemma are also one of those devices that are occasionally put to great evil, in my book. They are sometimes used for recensional editing (the worst of evils), used to abject and even reject certain manuscripts deemed worthless based on its distance from an authoritative text, and they’re just plain mind-numbing to try to consume intellectually.
In addition to being put to evil use, stemma are also sometimes misleading, laden with jargon tending to make them indecipherable to non-experts, and just plain confusing.
Take this A MSS stemma, reproduced (absolutely without permission) from A.V.C. Schmidt’s Parallel Text Edition.
Material Piers is back after a slight summer holiday. I know you weren’t pining because you were enjoying your own last opportunities to …. xxx… whatever it is you do in the summertime.
We last left off with a discussion of data aesthetics, in which I pointed out that the way you present your data is itself imposing interpretations, or at least interpretive structures, onto the “raw” data itself (whatever that means, amirite Lisa Gitelman??).
Equally important to presenting transparent information is defining the parameters of your data. In a science setting, data is only useful insofar as it is replicable by other scientists. They need to know not just the results (i.e. conclusions you draw from your data) of your experiment, but how you interpreted it, what it looked like pre-interpretation (“raw”), but also how you built the data-finding apparatus, and the question the apparatus was designed to answer. If, for example, you use a laser for something, you are only asking your experiment a question answerable through optical data collection.
The very way that data is “collected” (i.e. created, but more on that later) creates limitations to the kinds of answers you can get from your data.
THERE IS NOTHING INHERENTLY OBJECTIVE ABOUT DATA. Continue reading Defining Data Parameters: Pierscentricity
In a recent talk she gave for the Medieval Forum and the Anglo Saxon Studies Colloquium, Dorothy Kim discussed the importance of aesthetics in designing and implementing digital architectures that are not only “user-friendly,” but also that are inviting to the potential consumers of the information that the Archive of Early Middle English was trying to make available.
Kim’s talk got me to thinking about something inherent in the visual presentation of data that doesn’t get a lot of discussion. We (i.e. the people doing data visualizations and writing about them) are all so consumed with presenting information, that often discussion of the way information is presented and the choices involved gets left out of conversations about big data. Continue reading Aesthetics of Data Presentation: Piers A-B-C Graphs
In honor of the New Chaucer Society conference going on in Reykjavik right now, I’m going to bring back network graphs to see how Langland and Chaucer might network together.
Ok, well, if you’ve been reading this blog at all (which maybe you haven’t, and that’s ok–here’s what you’ve been missing), you already know that they really don’t. Nevertheless, let’s put some Piers and Chaucer network graphs side by side anyway to compare.
This week’s post is about synaesthetic data. Literally, it’s about visualizing the data we (the human instrument) collect by touching. To really show you what I mean, I’m going to jump right into our visualizations, which (as discussed last week) are a different style of data visualizations, but they are such nonetheless.
To put what I mean in perspective, I’m going to use something I know you’ve touched, and something you may have touched (the likelihood of which increases significantly if you’re a medievalist working on manuscripts). I’m going to compare the two materializations of post-calf and post-goat flesh we know as leather and (loosely) parchment.
We are going to take a tiny digression from Piers specific visualizations to get a sneak peak at visual data I’m presenting at the New Chaucer Society Congress in July.
What you are going to see here is a little different from the kind of data visualizations we’ve been looking at to date. So far, most of the data we’ve been highlighting here has been primarily data abstracted from a material phenomenon, and then reconfigured into slices meaningful to us. I’m going to talk more next week on what is “data,” so put a pin in those thoughts and we’ll come back to them.
This week, we have a series of images of parchment surfaces (to be featured in NCS panel 5F on Parchment, organized by Bruce Holsinger) that have been translated via a few very intense scientific apparatuses into something that we think we understand intuitively. There is so much at work in that intuitive leap, however, that I think it’s worth breaking it down step by step.