Data Art – ITP Fall 2019

• Instructor: Genevieve Hoffman
• Instructor e-mail: [email protected]
• Class Logistics: Tuesdays, 9 – 11:30am
• Office Hours: Thursdays, 4:30 – 6pm. Sign up on google calendar

Aims of Course

Fascinating and terrifying things are happening at the intersection of data and culture. Our lives are being constantly measured, and information about us is being surveilled, stolen, and commodified. Dialogue around this data revolution has been dominated by corporations, governments, and industry - but what about the arts? In this class, we’ll investigate the means by which artists can engage (and are engaging) in the collection, processing, and representation of data. Using a research-focused, prototype-based approach, we’ll build a series of collective and individual projects to interrogate the ‘new data reality’. Students will use Processing and P5.js, along with a variety of analog media or open-source data and visualization tools (such as D3.js, OpenRefine, MapBox, Mappa and THREE.js).

Course Themes & Section Projects

This course will be divided into four 'sections', each focusing on a different aspect of data art:

1. Data & Aesthetic
2. Text, Archives & Memory Stores
3. Data & Publics
4. Ethics, Humans & Responsibility

Each of these sections will begin with an introductory survey of work being done in this area, and a 'workshop' going over a few important technical points. The next class(es) will involve a discussion around assigned readings and a review of available tools, code libraries, and techniques. The last class in a section will feature a guest speaker, and brief (5 minute max) presentations of project work (see below).

For each of these sections, you will complete a small project, which will be assigned on the first day of the section and will be due on the last. The first assignment will be individual and realized. For the last three assignments, you have the choice of doing a conceptual project, or a realized one:

• Conceptual projects should be focused on possibility without constraint. What would or could you build if you were not restricted by materials, budget, technological possibility, or coding skills? The deliverable is a project proposal, much as you might submit for an art funding grant, or to a client pitching a potential project. Both written and visual descriptions of the project are required, and you may include audiovisual samples as well if that's helpful for getting the idea across.

• Realized projects will be built in the 2-3 weeks between assignment at the end of the section's first class and the due date. They should be posted to this repo with source code if applicable, and written up with a blog post.

For the last three assignments, each student will be required to complete one conceptual project, and two realized projects. It is your choice as to which of the last three sections you choose for which.

The first project is required to be an individual project. For the last three assignments, the project can be done as an individual project, or in groups of up to three students. Each student will be required to do at least one group project.

Expectations and Workload

You can expect to have 3-4 assigned readings for each thematic section. You must complete all readings prior to class, and come ready to participate in discussion. Projects links must be emailed to [email protected], along with source code (where applicable) before the start of class when the assignment is due. A brief blog post about the project is also required to be completed before the start of class.

Grading

If we were using a percentage-based grading system, the numbers would look something like this:

Class participation: 30% Semester Projects: 70% Since we’re not using a percentage-based grading system, let me put it another way: if you’re an active contributor to our discussions in class, and you complete your assignments, and you make something ambitious/excellent as a final project, you’ll pass this class. If you don’t, you won’t.

Class Rules

(i) Everyone shows up to class on time. If you’re going to be late, let me know in advance. If you need to miss a class for a real reason, you must also let me know in advance.

(ii) Everyone does the readings. For the most part, they’re short, fun, and useful. Students will be responsible for leading class discussion on readings on a rotating basis.

(iii) All assignment work is due at the beginning of class. Everyone gets a free pass for one late assignment. After that, any assignments not ready for the start of class will be counted as incomplete. A completed blog post about each assignment is required.

(iv) Everyone in the class must attend office hours at least once in the first three weeks of class.

(v) We’ll have a series of guest speakers coming into class over the course of the term. I will provide resources to learn about their work prior to their visits – everyone in class must do their homework and be prepared to learn from our guests.

(vi) I am 100% dedicated to an inclusive, harassment-free experience for everyone regardless of gender, race, sexual orientation, disability, background, appearance, or religion. I will not tolerate harassment of class participants in any form.

Touchstones

(i) Gary Shteyngart - Super Sad True Love Story

(ii) Brian K. Vaughan, Marcos Martin and Muntsa Vicente - The Private Eye - http://panelsyndicate.com/comics/tpeye

Syllabus

Data & Aesthetic

Readings:

• Raw Data is an Oxymoron Introduction Lisa Gitelman and Virginia Jackson
• What Would Feminist Data Visualization Look Like? Catherine D'Ignazio
• DataViz - The UnEmpathetic Art, Mushon Zer-Aviv
• Picturing the Self in the Age of Data, Dan Weiskopf
• A Concise Taxonomy for Describing Data as an Art Material, Julie Freeman, Geraint Wiggins, Gavin Starks and Mark Sandler

Watch:

• Subtle Data, Stefanie Posavec speaking at the 2013 Eyeo Festival
• How I Learned to Love Data Visualization (Again), Jon Schwabish speaking at the 2015 Visualized Conference

September 3rd – Week 1. The lay of the land & introductions - slides shown in class

Assignment

• Data Sketches: Visualize a data set in at least three ways. Choose to work with a different visual element, drawing inspiration from gestalt principles.
• Three students prepare to present the readings for next week:
  • Raw Data is an Oxymoron Introduction Lisa Gitelman and Virginia Jackson
  • What Would Feminist Data Visualization Look Like? Catherine D'Ignazio
  • DataViz - The UnEmpathetic Art, Mushon Zer-Aviv

September 10th – Week 2. Topic Survey & technical overview (data translation) - slides shown in class

Assignment

• Data (Self)Portrait: Create a self-portrait or portrait of someone else. The portrait must be derived from a data set, or use data as a material.

• Due Week 4, September 24th. Documentation should be posted and a link emailed before class begins

• Two students prepare to present the readings for next week:

  • Picturing the Self in the Age of Data, Dan Weiskopf
  • A Concise Taxonomy for Describing Data as an Art Material, Julie Freeman, Geraint Wiggins, Gavin Starks and Mark Sandler

September 17th – Week 3. Discussion of readings & technical workshop (Data Transformation and Analysis Intro) - slides shown in class

Assignment

• Develop Data (Self)Portrait Project

September 24th - Week 4. Guest Speaker Scott Kildall & Project presentations

Text, Archives & Memory Stores

Readings:

• Consider the Boolean, Jacob Harris
• "facts and FACTS": Abolitionists’ Database, Ellen Gruber Garvey
• A Sea of Data: Apophenia and Pattern (Mis-)Recognition, Hito Steyerl
• Abundant Images and the Collective Sublime, Kate Palmer Albers

October 1st – Week 5. Topic survey & technical workshop (RiTA) - slides shown in class

Assignment

• Archival Annotation: Using an archive of your choosing, create a piece that calls attention to the underlying logic behind the archive
• Due Week 7, October 22nd. Documentation should be posted and a link emailed before class begins
• Four students prepare to present the readings for next week:
  • Consider the Boolean, Jacob Harris
  • "facts and FACTS": Abolitionists’ Database, Ellen Gruber Garvey
  • A Sea of Data: Apophenia and Pattern (Mis-)Recognition, Hito Steyerl
  • Abundant Images and the Collective Sublime, Kate Palmer Albers

October 8th – Week 6. Discussion of readings & overview of other resources (puppeteer.js) - slides shown in class

October 15th - NO CLASS, Legislative Day

October 22nd – Week 7. Guest Speaker Sarah Groff Hennigh-Palermo & Project presentations

Data & Publics

Readings:

• Rethinking Maps: Thinking about Maps, Rob Kitchin, Chris Perkins & Martin Dodge
• When Maps Lie, Andrew Wiseman
• Here Be Dragons: Finding the Blank Spaces in a Well-Mapped World, Lois Parshley
• Mapping’s Intelligent Agents Shannon Mattern
• IMG MGMT: The Nine Eyes of Google Street View, Jon Rafman

October 29th – Week 8. Topic survey & technical workshop (Web Maps / Public Data) - slides shown in class

Assignment

• Spatial Data Practice: Explore the relationship between landscape, data and publics by intervening in public space with data OR representing spatial data through mapping or visualization.

• Due Week 10, November 12th. Documentation should be posted and a link emailed before class begins

• Four students prepare to present the readings for next week:

  • Rethinking Maps: Thinking about Maps, Rob Kitchin, Chris Perkins & Martin Dodge
  • When Maps Lie, Andrew Wiseman
  • IMG MGMT: The Nine Eyes of Google Street View, Jon Rafman
  • Here Be Dragons: Finding the Blank Spaces in a Well-Mapped World, Lois Parshley
  • Mapping’s Intelligent Agents Shannon Mattern

November 5th – Week 9. Discussion of readings & overview of other resources (Leaflet.js / Mapbox GL / Mappa + p5.js / Turf.js) - slides shown in class

November 12th – Week 10. Guest Speaker Alvin Chang & Project presentations

Ethics, Humans & Responsibility

Readings:

• Critical Questions for Big Data, danah boyd & Kate Crawford
• Big data problems we face today can be traced to the social ordering practices of the 19th century, Joanne Travaglia & Hamish Robertson
• A chronology of tactics: Art tackles Big Data and the environment, Brooke Singer
• Dataveillance and Countervailance, Rita Raley

November 19th – Week 11. Topic survey & overview of the landscape - slides shown in class and Guest Speaker Shirley Wu

Assignment

• Data Critique: Critique an aspect of data culture

• Due Week 14, December 10th. Documentation should be posted and a link emailed before class begins

• Two students prepare to present the readings for next week:

  • Critical Questions for Big Data, danah boyd & Kate Crawford
  • Big data problems we face today can be traced to the social ordering practices of the 19th century, Joanne Travaglia & Hamish Robertson

November 26th – Week 12. Discussion of readings & D3 - slides shown in class

Assignment

• Two students prepare to present the readings for next week:
  • Dataveillance and Countervailance, Rita Raley
  • A chronology of tactics: Art tackles Big Data and the environment, Brooke Singer

December 3rd – Week 13. Technical workshop (Color + 3D Data Vis) - slides shown in class

December 10th - Week 14. Project presentations