some drawing strategies

At some point during the semester, it started to seem strange to me that digital art curation didn’t also mean a trail of audio / visual / moving-image process documents. I used GIFs and video as shorthand on the blog, trying to illustrate or punctuate a point here and there, but didn’t synthesize anything through visual production.

Backing slowly away from Plan A — a “lessons learned” post composed entirely of Hamilton GIFs and lyrics — I’m instead taking some space here to speculate: What are some alternative ways to represent the products and processes involved in digital art curation? Here’s a look at some of the drawings I made while grappling with @mothgenerator.

1_Authenticity-access grid
With thanks and apologies to Dragan Espenschied.

Authenticity-Access Grid for preserving @mothgenerator. Inspired by <a href="">Geocities</a>
Authenticity-Access Grid for preserving @mothgenerator. Inspired by Geocities

I used this diagram as a working tool while writing my statement of significance for Moth Generator. It was a useful way to start looking ahead to the kinds of experiences and characteristics different stakeholder groups might value and expect. Because the grid was designed to express authenticity and access from the users’ (not creators’) perspective(s), mapping my project to it was a natural fit for an overall shift towards a more user-centered preservation strategy. It’s also a sneaky example of how techniques for visualization can shape the content, purpose, and management of information. So, nice work, Dragan — your nefarious grid convinced me!

2_Distributions of significance

Plotting the concept-to-AIP transformation.
Plotting the concept-to-AIP transformation.

As a way to trace the evolution of this project from conceptual (identifying significance) to somewhat-less conceptual (declaring preservation intent, assembling a dummy AIP), I mocked up this rainbow circle mapping Moth Generator’s components, stakeholders, and significant characteristics to the contents of the eventual AIP. It’s interesting to see how conceptual elements converge around certain parts of the AIP, but I wouldn’t drawn any conclusions from that about priorities or complexity. It’s not as though more connecting lines means more value (maybe mo’ problems). I mocked this up without much of an agenda beyond, “Let’s draw some lines and see what happens,” and am at least pleased with how it represents the project’s trajectory.

3_Tool-lifecycle grid

Digital curation tools and the digital art curation lifecycle.
Digital curation tools and the digital art curation lifecycle.

This grid diagrams the range of tools considered, tested, and ultimately used to capture, describe, and package material into an AIP. I tried to represent the overlapping functions of many of these tools, where they address the digital art curation lifecycle, and the degree of success I had with each. In choosing shades and ordering the vertical axis, I tried to avoid designating things as “failures” or “bad tools,” since success or failure in digital curation is often a matter of mismatch (right tool / wrong purpose, or vice versa) rather than of quality. One early idea for the vertical axis was to sort tools on a spectrum from ideal to contingency to NO, but in the end I chose to list them by earliest point of intersection with the lifecycle. Interestingly, making this diagram called my attention to how actually useful DataAccessioner (one of the “contingency” tools) really was.

There is so much digital preservation software out there — check COPTR or the POWRR tool grid if you doubt it. With this little drawing, I mostly want to convey the value of diversifying and experimenting. The grid has been a useful way for me to track what I’ve done and what to try next. Rhetorically, it says, “Keep trying!”

UPDATE (5/5/16): Images now give the actual course number. Sleep-deprived regrets.

Visualizing Your Data With IBM’s Many Eyes

Many Eyes is a powerful tool that enables a user to create visualizations from any kind of data set.

Here’s where it gets fun: while a user can upload their own data set, Many Eyes is a community-powered tool. There are over 150,000 data sets to choose from, and many are pre-visualized.

Another (seemingly underused) feature are Topic Centers. Topic Centers allow teams of people to collaborate on visualizations. Topic Centers are organized around certain topics (makes sense, right?), as well as teams of people at organizations and classes (like this one).

Here are some examples:

Average Time Spent Commuting by State Many Eyes
Average Time Spent Commuting by State

Number of arrests by age and type of crime Many Eyes
Number of arrests by age and type of crime

News Blogs Dominated By A Few Startups Many Eyes
News Blogs Dominated By A Few Startups

But selecting a dataset from the community is not always the best option: the metadata associated with many of the datasets is inaccurate or incomplete. Rest assured, because what makes Many Eyes such a versatile tool is that any type of data is accepted, so long as it is in a structured format. Data needs to be pre-formatted in Microsoft Excel (or similar spreadsheet software), then pasted into Many Eyes’ Web interface.

Then the user is presented with an array of visualization options, from tag clouds and word trees to assorted graphs and even maps.

A couple of potential uses for historians:

  • Take a historical text or speech (i.e. the Gettysburg Address) and create a tag cloud from it, where the more frequently a word is used, the larger it will appear.
  • Create a network diagram to visualize a historical figure’s family tree.
  • Use a map to show population trends over time.

Over the summer, I took air traffic control data and visualized it using Many Eyes, for fun. It was easy to use every step of the way. In fact, it’s so easy to use, the hardest part should be finding the data in the first place.

It is beyond imperative to have good visuals when working on the Web, since readers hate long blocks of static text. Bringing a history project to the Web calls for the use of visualizations like those that can be generated using Many Eyes. It will make your work more attractive, and will certainly help your readers understand things better. At the end of the day, it’s all about them!