2020 Lib/Lab Fellows Syllabus

Now in its fifth year, the LibLab Fellows program is an experiment in library-based learning guided by a critical consideration of just what we mean by “the digital.” Fellows engage theory and practice of digital scholarship through open lab hours and weekly discussion meetings during the fall semester.

We will be making use of the data visualization platform Observable, working on what might be called the “Computational Essays” during the course of the semester. You can explore some of past work along with notebooks that inspired us here.

Week 1: Pshhhkkkkkkrrrrkakingkakingkakingtshchchchchchchchcchdingding*ding

Post It note wall

An introduction to the terrain.

Observable notebook for this week’s discussion.

Week 2: Do Artifacts Have Politics?

What can things do? Considering the perspective of Science and Technology Studies.

Observable notebook for this week’s discussion .

Week 3: Considering Infrastructure

National ARPA Network Map

Before we can go further into networked technologies, we ought to have a starting point – What do we mean when we say internet?

Week 4: Getting used to Observable + Intro to Data

No readings this week. We will spend our time getting to know Observable and some of its quirks. Also, since our projects require us to get our hands dirty with data, we will chat about how we use data in our daily life and what we will need to consider to choose a dataset for our project.

To that end, Lisa Gitelman and Virginia Jackson write in the introduction for “Raw Data” Is an Oxymoron,

“Data need to be imagined as data to exist and function as such, and the imagination of data entails an interpretive base.”

For next week:

  • What do the authors mean when they write that “data need to be imagined”?
  • Find an Observable notebook that is data-driven and appeals to you. You can always begin in the explore section of Observablehq.com but feel free to look elsewhere.

Week 5: Language of Visualization

Bertin, Semiology of Graphics. 1983. p. 43.

What are we doing exactly when we render data visual or legible? Public health, natural catastrophe, electoral politics, and social inequality. Chart, map, and graph. Increasingly, our understanding of the world is mediated by dynamic representations of data attempting to model real world phenomena. In the process, what do we gain access to and, oppositely, what is effaced or made invisible?

Week 6: On Clouds

Circling back to our earlier conversation about how the web works, when so much of infrastructure involves the effort to ensure it remains invisible, what does it take to make sense of infrastructure, that is, make infrastructure sensible and perceptible?

Week 6: Algorithms

Logical NAND alogrithm

The question of “What is an Algorithm?” is as important as the question of “What does an Algorithm do?” There is a tension at play in what these authors are writing about and as you read and watch, pay attention to their answers to both questions. How would you answer?

Week 7: Interlude (Designing for Democracy)

How can we intervene or coopt the tools/discourse of commercial tech to approach pressing social and political problems? What limitations or problems might arise in doing so? This week we took a break from our regularly scheduled programming to collaborate on a mini design sprint, a tiny gesture towards electoral catharsis.

Week 8: Surveillance & Privacy

Is the internet listening? Is the internet listening to everybody? What if, by design, we can never know for sure? This week we will focus on the porous border between technical, social, and personal implications of continuous data collection.

  • The New Organs Watch the 10 minute video and explore the landing page.

“The New Organs is a project to gather, archive and investigate the theories and realities of corporate surveillance.”

Week 9: Machine Learning

Over the last twenty years give or take, the fabric of our lives has been interwoven with a special class of algorithms: Algorithms that use dynamic statistical weighting plus training data to generate novel outputs that may not have been explicitly programmed. Algorithms that, with more data and more iterations, self-modify. Spam filtering. Suggested text. Recommendations. Siri. Facial detection/recognition. Self-driving cars. This is not new!

In thinking about our last readings for the semester, take stock of the stack we’ve built so far during this semester: :turtle: artifacts + politics + HTML/CSS + internet infrastructure + clouds + data collection :turtle:

How might machine learning leverage the whole stack to ask pressing questions about not only our pasts but also possible futures?