Scientific Data Presentation

PhD-level course — 3 hp — September-October 2013 Centre for Image Analysis, Polacksbacken, Uppsala

Schedule

All sessions will be held in the CBA lecture room, Polacksbacken 2115.

Thursday, 19 September, 9:00-16:00
Exercises to do before this session:	Find two or three difficult to understand graphs in the literature. What is the main goal of each graph? (Individual exercise)
Activities:	Lecture: Introduction: purpose of graphing, when to use tables instead, simplicity, story. Lecture: “Proper” formatting of tables. Lecture: How good are we at percieving quantities when represented in different ways? Lecture: Basic graph types: line plots, scatter plots, box/jitter/violin plots, dot plots, bar charts, pie charts, bubbles, etc. Discussion: we'll look at the difficult graphs you found, discuss them, and think of ways to make them simpler.
Thursday, 3 October, 9:00-16:00
Exercises to do before this session:	In one issue of a journal, find the three best graphs and three worst graphs. Discuss why they are good/bad. How would you improve them? (Individual exercise) Take one of the difficult graphs you found, and produce what your group thinks is the best presentation for that data. (Group exercise)
Activities:	Lecture: Data transformations: logarithmic axis, smoothing, aspect ratio, scale breaks, projections. Discussion: present the graphs you created, we discuss good and bad aspects, think about how to improve them. Lecture: Labels, legends, decorations. Demo: Using Inkscape to prepare your graphs for publication (by Gustaf Kylberg). (slides) Discussion: we discuss the good and bad graphs you found in the literature, and propose various ways of making them better. Demo: Overview of most common graphing software.
Thursday, 17 October, 9:00-16:00
Exercises to do before this session:	Read Edward Tufte, The Visual Display of Quantitative Information, 2nd Ed. (Individual exercise) Take some of your own data and create a journal-ready graph from it. Use Inkscape to make it prettier. (Individual exercise)
Activities:	Discussion: present the graphs you created, we discuss good and bad aspects, think about how to improve them. Lecture: Colour perception (by Gunilla Borgefors). Lecture: Plotting multiple data sets and multidimensional data: small multiples, scatter plot matrix, images, surfaces, parallel coordinates, etc.

Signing up for the course

The course has filled up really fast! Read below, I'll add you to the wait list. Given the response, I'm sure the faculty will provide funds to run this course again next year.

Email me at cris at cb dot uu dot se to sign up for the course. Please include the name of your department and division.

The course is limited to 20 participants. I am looking to fill this with participants from many different backgrounds, and therefore will try to limit the number of participants from the same division. In principle people will be accepted on a “first come, first served” basis, but you might be put on the wait list if too many people from your division are already accepted.

On September 5^th, two weeks before the course start, I'll fill the class with people on the wait list. You will be notified then if you can participate.

If you are registered for the course, but cannot participate for whatever reason, please let me know as soon as possible so that someone else can take your place!

Information

The course has filled up really fast! Read here, I'll add you to the wait list. Given the response, I'm sure the faculty will provide funds to run this course again next year.

See here for the course description and goals.

Sign up for the course by emailing Cris Luengo. The deadline is two weeks before the course start. The course is limited to 20 participants, and taught in English. All sessions will be held at the Centre for Image Analysis (Polacksbacken, house 2, ground floor, room 2115).

Teachers:

Prerequisites:

No specific knowledge is required, besides that expected of any PhD student.

Literature:

Mandatory reading:

Edward Tufte, The Visual Display of Quantitative Information, 2nd Ed. (2001). [Amazon.co.uk]

Suggested reading:

Edward Tufte, Beautiful Evidence (2006). [Amazon.co.uk]
William S. Cleveland, The Elements of Graphing Data (1994). [Amazon.co.uk]
W.E. Russey, H.F. Ebel and C. Bliefert, How to Write a Successful Science Thesis (2006). [Amazon.co.uk] (Chapters 20 and 21 deal with tables and figures, respectively [PDF].)
Gunilla Borgefors, Ögonblick i färg – om seendets natur och naturens seende (2007), in Swedish.
T. Moeller, J. Huang and others, Introduction to Flow Visualization (slides).

Software:

Besides the graphing software you are used to, you will learn to use Inkscape, an open-source drawing program, to make your graphs publication-ready. You will have to install this on your computer to do some of the homework. If you have access to a program such as Adobe Illustrator or CorelDRAW, feel free to use that instead.

Other resources:

Points of View, a series of columns about graphing in Nature Methods.
Junk Charts, a blog about terrible graphs.
Top ten worst graphs.
Color Brewer 2.0, an applet that helps you select a colour scheme for your graphs.
Vischeck, a program that simulates various forms of colour blindness.