This video helps put things into perspective.

Not bad. “We can’t fool them for much longer. We’ll have to work for a living.” is pretty good, but the line that one woman whispers to another is priceless:

“We’ll always have informal learning networks.”

Be safe everyone.


The Apologist

I just wanted to talk a little about some of my comments in class, or better my attitude and thoughts in the subject of the Digital Humanities.  

I want to defend the humanities.  I believe in the project of reading and writing and analyzing and in the project of qualitative reading and writing and expression.  I think the crisis in the humanities – which digital humanities is a quantitative-centered reaction to – is pretty real, and comes from our culture, here in the US anyways, de-valuing value judgements made by authorities or officials, and really valuing numbers.  Data.  Market-driven value.  My language and comments in class usually had me asking “Well, why would anyone pay for this [kind of work]?”  “What of value does this produce?”  And the traditional humanities, in this regard, these days, comes up short.  So how can we quantify humanities work, apply quantitative-style analysis to what was until now much more qualitative?  With computers.  Tack on that DH is NEW (which everyone likes) and we’ve maybe got something to sell to Provosts across the academic land (and something to publish, thank god).  I sympathize.  I really do.

But on the flip side, some of these tool do seem to me to be genuinely useful.  I just now saw what Concordance can do to a long text, like a super long poem – it can act like a super index that you have on hand as you read.  You can ask and find out if a thought or idea (word) exists in other sections.  You’d have to be smart enough to search for synonyms, but this would work.  It’d certainly make writing a paper on such a thing easier.  And certainly a bunch of this stuff skews closer to the social sciences, which is great!  Anyone who’s enjoyed a good table or graph on, say, The Atlantic, has to appreciate the new technologies that make interesting representations of data possible.

So, in the end, I can see that the kind of things we’ve been studying do have applications, and I also do want to continue to ask tough questions – like why should we care? and what would the Provost think?  Because these matter.  Students pay lots of money to go to college and get something we like to call an education and we’d better have something good to show them when they get here.


Bamboo Dirt 3 – Concordance

Hey Team.

So, the final writing / text program I’ll talk about is Concordance.  Apparently this tool will give you a list of words in a text in alphabetical order.  Their site has a pretty neat graphical image showing you how this looks.

concordance 1


I tried to upload a document to try this tool out, but I got this:

concordance 2

Concordance works with Concordance files.  Go fig.  So I went to their website to try to find some helpful-looking files to use.  But I got a little side-tracked with this:

concordance 3

This is a concordance of William Blake’s Songs of Innocence and Experience.  Presumably this was done with Concordance, the program.  And if I were studying this poem as an academic, this would indeed be a useful tool.  It lets me essentially jump right to any word in the text – like an index on steroids – and see if Blake had something to say about it.  Of course, the term that caught my eye was ale-house.  He had little to say on this subject in this poem, but, as you can see, his view was positive.  Revelatory!

Be safe everyone!




Bamboo Dirt 2 – Scrivener

Part two of our 3 part series.  


Scrivener is a program that’s designed to help with long form writing.  If you were writing a novel, Scivener wants to be your main deal.  So saith the organization: “Scrivener is ring-binder, a scrapbook, a corkboard, an outliner and text editor all rolled into one.”  It also has a cute cork board look:


There are some pretty advanced organizational features and the program even incorporates metadata.  What’s nice about a program like this is that it keeps a lot of things in one place, and those things can pop up much quicker than if you were to just keep them in files on your computer.  It’s a good tool to keep large projects organized.


When you’re ready, you can even export text out to Word and other office-style programs.

I’m told that this program was initially a program for the Mac OSX that’s now also got a Windows version.  At least one person at my office has been using this program for her work for years and tells me she loves it.  I’ve played around with it a little, and it seems a little overwhelming, feature-wise, but even the Scrivener people admit that the user probably won’t use a whole lot of the functions.  Rather, they’ll use a handful and really appreciate them.


All in all, a good tool for serious writers to try out.


Bamboo Dirt 1 – Google Drive

Drive, folks.  Google re-named it’s suite Drive, and not ‘Docs’.  But it’s pretty much the same thing.

Along with something like Dropbox, I can’t think of a tool – I guess this counts as a DH tool – that I use more and which works so well.  Google Drive, if you have a Google account for mail and the like (I also use calendar for just about everything), is a great way to have documents, excel files, and just a bunch of your stuff available anywhere there’s a computer with internet access.  I suppose you wouldn’t want to put sensitive info up on this, as it technically belongs to Google – and can be seen by the NSA (but they can see everything, right?  Hi Team!  Nothing to see here!).  It’s an alternative to e-mailing yourself a paper you need to print out as you can upload documents – they do get converted into Google Drive docs, but these are compatible with Microsoft Office suite programs, so fret not.  



Just like a computer in the cloud / ether / someone else’s server, you have files and folders and documents in them.

The other just essential aspect of Google Drive is the sharing thing.  You can share documents or even whole folders with multiple other Google users.  This makes collaboration super easy.  Each person can write and edit – and explain their reasoning with notes in the margins – whenever they have time.  To create a group presentation, say, you don’t really have to meet.  You can just do it all on Google Drive, each when she or he has time.



Here you can see I’m sharing a presentation with three other people.  What’s also amazing, it that you can chat with all of the people working on a document within the interface of the document.  That floored me when I first discovered that feature.  


So, if you’re ok with having some information on the web – again, Hi NSA! – then Google Docs is a GREAT DH tool.  For sure.


Project Management




So, I thought I’d write about a real-life thing I do for work that is I think technically a DH project.  Might be better than whining about how I don’t understand text mining.

I manage a sheet music digitization project for the music library that I work at.  It was designed and set up by my predecessor, so for me it’s a legacy project I inherited (I often wish I could spend my time doing other things, but that’s a different matter).  This is, for me, a decent chance to get my hands dirty with project management.

Here’s the site, if you want to take a gander:

Basically, we have lots of old sheet music in boxes.  Before my time, most all article metadata was created and uploaded to a paid site platform saved to a local (WRLC) server.  From there, a sheet music consortium out of UCLA extracts and harvests metadata to share with the consortium.  

The consortium is here:

Thus, anyone interested in looking at old sheet music has access to tons of digitized scores from numerous institutions across the nation.  The metadata and thumbnails exist on the consortium’s website, and the real images stay on our server.  

My task has been to scan and upload – with beefier metadata – sheet music that isn’t already in the consortium.  No reason to scan and upload a sheet if another school has already done it.  

In terms of project management, it’s been a bit of a bear understanding all this.  I had no experience with this sort of thing.  My predecessor left me with plenty to read over, but there was still a hefty learning curve on my part.  Part time staff who have been doing the work have been helpful, but often they just understood one narrow aspect, and didn’t understand how that aspect fit in with the whole project.  I’m also pretty dense, and get frustrated easily – neither of which has helped.  

But recently I have had the time and person-power to really sink my teeth into it, and I feel better about the whole thing.  It helps that there’s really no timetable for it’s completion – no pressure to get it done fast.

Speaking of which, re-reading over the Cult of Done Manifesto has reminded me to breathe and relax about this project.  I especially appreciate the laughing at perfection part – it’s good to want to get it right, but not at the expense of never moving forward.  

While this does count as a Digital Humanities project, it also just seems to me like a project, with the humanities aspect left to someone else.  I personally don’t feel all that invested in the thing, but it is my job, so I do it.  Hopefully someone somewhere is getting something out of these scans.

Have a look at the site’s I’ve linked above, and let me know if you have thoughts or questions. 




Team.  TAPoR.

TAPoR is a website that hosts lots of text mining tools, free to use on the open web.  It doesn’t have any tools itself, but is rather a place someone can go to explore different kinds of text rending.  The benefit here is that you can compare different tools that do the same thing.  Better: you can discover tools that you didn’t know existed, that do things that you might not have thought of, but which could be really valuable for whatever project you’re doing.  

The site groups similar tools together for easy browsing.  Two groupings stand out.  One is popular.  The site doesn’t seem to say how these tools have been deemed popular, but presumably this is a good place to go to see what others are finding useful.  The other is reviewed.  Tools are briefly reviewed, letting new users get a better sense of what each tool can do and if it might be relevant for them.  That’s a nice feature, if you ask me.

The site also functions like a community hub for tool users and developers.  Presumably, you could share information on your experience with a tool, and ask questions.  

So this is a pretty nice site to know about if you have a text and want to know different ways you could view it.  






Team.  This is where I talk about Juxta.

Juxta seems to be an online tool for comparing texts.  You put in two different versions of the same text, and Juxta helps you to visualize all the different ways that the texts are dissimilar.  You can upload text files, and before you can compare the two files, you have to do something called turning them into witnesses.  I have no idea what this means.  Maybe there’s some kind of digital authentication that happens behind the sense whereby a Hal 9000 blesses each text to be used in public comparisons on the open web.  Maybe Juxta is simply checking with their legal team.  It’s anyone’s guess.

I’m not a literary scholar, but I suppose the point of this is to help those so inclined see how an author’s vision changed over time, or perhaps how an editor changed things around to make a book, poem, essay, etc. more marketable.

To try to test this tool out, I used the first bit of Leaves of Grass.  I used this text mainly because it was given in class, but also it makes sense, as the 1855 and 1897 versions are very, very different from one another (at least how they look from the downloads – on 2nd viewing, there’s something fishy about the ’55 version that I can’t quite put my finger on).  Also, I haven’t read Whitman since I smugly dismissed him as too wide-eyed in college, and it was kinda nice to see these lines again, taking in the trademarked exuberance.

I’ll talk about how Juxta is different from Voyant and the art-rock-band-name-like TAPoR.

It’s hard for me to talk about what results where received from this tool.  I’m happy I finally got it to work.  And, again, I’m not a Whitman scholar and have no intention of becoming one.  In short, I was able to compare two texts in a way that had more digitized blue highlighter-esque graphics than if I had printed out the texts and just sat them beside one another on the kitchen table.  For example, I can very clearly see, thanks this this blue stuff, that the 2nd two stanzas in the first part of the poem where entirely added way after 1855.  After reading the famous opening salvo again, I’m happy that those stanzas made it in.  Certainly makes for a better read.




“Facts all come with points of view / Facts don’t do what I want them to.” – Mr. Byrne

Read the introduction to Raw Data Is an Oxymoron just now and it’s put a few things into my head, and maybe it’ll get me to take this concept of digital humanities more seriously.  Of course, data and raw data are just two out of many concepts related to this course, so I shouldn’t jump the gun.

I appreciate how Gitelman and Jackson seem to be asking more questions than providing answers, seem to be introducing the subject matter and the essays in the they’re editing as jumping-off-points for further discussion.  That seems like a good way to go about it.  We prefer questions, right?  Answers are so off-putting.

The authors stress that data is never raw – it never just exists on it’s own.  It’s always “cooked” (p.2).  Just as in phenomenology thingness comes into being by what we choose to perceive out of our notion of where said thing begins and ends, data is just as much, if not more, about what we’re choosing not to look at.  A cat is a cat insofar as it doesn’t exhibit dog qualities or behaviors or features, at least to how we see them.  If I am choosing to count red white and blue jelly-beans in a jar, I’m also choosing not to count the other colors.  Data are sets of things we want to see, and we use technology to see every larger – or smaller? – sets of like or related datum.

Which is great.  And fine.  And helpful, very often, because I’m sure these kinds of processes are behind some very important science.  But the authors here do reveal that data is a choice.  We choose to have data in our lives.  Or we don’t, insofar as the information technologies we use every day are often not thought of a things we choose to interact with.  I mean, we’d rather not deal with that inbox, right?  But again I’m reminded that values drive activities, and we probably value data and it’s use and interpretation… because it makes our masters richer!  Ha, no.  Not quite.

The pull-quote from this piece for my money is “Data need to be imagined as data to exist and function as such, and the imagination of data entails an interpretive base” (p.3).  Bullseye.  Data as the result of our imaginative doing, certainly not as objective as we like to think it is.

For me, maybe, this turns things right back to my central question for this course: what are the digital humanities, and do these kinds of questions or approaches have any real relevance for the humanities?  If data is something we imagine, couldn’t we do our authors and creators a better service by imagining what they might have hoped and wished for?  Or better, why can we imagine ourselves pleasantly reading or experiencing humanistic works for their own sakes?  Why imagine data, when it’s so much more enjoyable to imagine characters, scenes, and, I don’t know, beauty?




As a starting exercise, I’ll take the professor’s kind bait and compare two online text toys.  These would be Voyant, which I have not seen before the professor suggested it, and Wordle, which I’d had the misfortune of discovering in a class last fall.  For anyone who hasn’t come across either site / toy, these are essentially text boxes where you can copy and paste a spate of verbiage, and the toy will produce for you an image of the words in the text, arranged haphazardly in a blob, with the words that appear most frequently written larger than those that appear maybe once or twice.  I suppose like other toys, it’s all for good fun.  And as I enjoy fun, I thought I’d put in a choice bit of the old MD (Moby Dick) and see what happens.

It seems, not much.  With either site you get essentially what I’ve described: a blob or words. You play by taking what a thoughtful human mind took hours to assemble and instantly disassemble it, prioritizing words as you would integers in a crude code.  Ah yes, that big one in the middle there appears… the most.  Glad we checked.

Wordle lost me the moment I tried to use it and ran into issues with Java.  Crash, bang, install, reboot, fail, try-on-another-browser, and I got the toy to finally work.  I was rewarded by big words in the middle: ‘whenever’ ‘get’ ‘nothing’ ‘little’ ‘time’ ‘find’.  So glad I didn’t bother reading the text as sentences with subjects, verbs, and predicates, never mind voice, tone, pace, etc.  There are options to change the look of your word cloud, if you didn’t initially appreciate the text vomit the algorithm gave you.  I like mine in blue.

Voyant adds a nice level of importance by referring to their word cloud as ‘cirrus’.  Cute.  Voyant on the whole does have a more utilitarian feel to it, as if the user is expected to put in lots and lots of text for analysis.  It’s probably the case that my one paragraph yields unimpressive results because it’s so small.  Maybe I should try whole chapters of Moby Dick, or the whole novel.  While Wordle seems content to be a toy, Voyant dares you to really take it for a spin.

So I put in the whole chapter.  This is where Voyant really shines.  You can click on a word in the text, and instantly get a frequency line graph to show you how often and where your chosen word appears.  I’ll admit it’s fancy.  It looks like I could use this toy more like a tool, to, as the tag read, “see through your text”.  Tempting.

Or I could read it.  I can read the words in the order in which the author intended them to fall.  I could pleasurably speak the sentences softly as I read, noticing how different American english was back in the mid 19th century.  I could dive deep into the gloom of this massive paean to American genius and hubris.

I know, I know.  I should give this stuff a chance.  It’s easy to poke fun, and for all I know there’s a lot to unpack by analyzing text like it was computer code.  It’s a bit like holding and x-ray machine to a airline passenger and seeing his flab: it’s a neat trick, but it’s not really necessary, is it?