jmeggitt / reflections-research

Reflections Research Edition

Note that, in this second half of the term, each reflection should now be a research paper! Ideally, upload a link to the paper or a PDF as part of your reflection submission.

Reflections will be due before the first class of the week.

Because this is not your typical CS course assignment, let me begin by explaining the rationale.

Students may learn much in courses, but little beyond the scope of the course. This is a failure.

To combat this failure, the goal of Reflections is to allow you to engage with the outer visualization community, guided by your own interests.

Every course you take is a sampling of a diverse community of research, practitioners, and professionals. Engaging with this community, even as an observer, will significantly enhance your learning in this course by allowing you to forge connections to the things we learn with the outside world.

Here's how it works:

1. Every week, make an effort to explore the data visualization community (see Finding Connections below).
2. Before class, add a reflection on a writing of your choosing to the appropriate week in your fork of the repo (see Writing Reflections).
3. To submit, make a pull request.
4. Come to class prepared to talk about what you've found this week with others.

Finding Connections

Everyone has their preferred way of finding out more about a topic. You probably already come across visualizations in your daily life, either through news or social media. If you already feel "plugged in" to the data-driven world, by all means use those as reflection material.

I imagine many of you will want to "plug in" more to the visualization ecosystem, however. Here are several ways you might do this:

• Following folks on Twitter (for some reason there are tons of datavis folks on Twitter)
• Reading visualization news blogs like FiveThirtyEight and The Upshot
• Diving into a domain of interest (biology, security, etc) and it's relation to visualization (see BioVis, VizSec for example)
• Wikipedia
• Reddit (/r/dataisbeautiful)
• Stack Overflow (check the d3 and ggplot2 tags)
• Academic / practitioner visualization blogs: eagereyes.org (he links to many others)

...and hundreds of other ways.

Writing Reflections

There are few requirements for the reflection. I ask that you aim for 10 sentences at minimum as a service to yourself and to others that take time to read what you've written. Any less and the effectiveness of the reflection for learning may be lost.

The form and overall length of the writing is up to you. This task is meant to give you the creative space for exploration in the the data viz world. Some suggestions about the kinds of refelctions we expect are agreements or disagreements with a paper or talk you find, a small introduction to a new visualization technique/technology that came out, improvements or future work for a visualization you find on a news website etc. I recommend including links so you can find the resources later on. You can make multiple entries if it suites you-- I could imagine calling on reflections as you generate ideas for your final project.