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How To Improve Your Slides, Articles etc.

This document aims to help to circumvent some pitfalls in data presentation and creating scientific articles and slides. The intended audience are undergraduate, graduate, PhD students and possibly also working scientists in Computer Science and Bioinformatics. It is partly based on [Sanders, 1999] and partly based on what we conceive as major mistakes often made in slides and articles.

All of the points presented here will revolve around the following fundamental principle:

The main aim of using is to transport information in a concise way.

General Rules

Differentiating Your Work From Others'

For qualifying work such as Bachelor's, Master's and PhD theses, it is very important to differentiate your work from other people's. Use quotations to give credit.

In theses, it is okay to use numeric style, e.g. [1]. For slides, citing the authors is better.

One often sees the result of the following "algorithm". Give the author names and the year of publication. For one author, give its name. For two authors, give both names, separated by a comma. For more authors, give the first name followed by an "et al." Put the result in (square) brackets and maybe use a different color.

Examples are [Smith, 2001], [Doe, Smith, 2001], [Smith et al. 2001].

At the end of the presentation, there should be a "Literature" slide. You might to show the slide to the audience very briefly only or not at all but it is good to have as a reference or when you make your slides available for download.

On Presentation Slides

Giving comprehensive advice on how to prepare a good presentation with good slides is out of the scope of this paper. Still, we want to give some landmark rules that apply to most cases. Remember, rules are there to make you think before you break them.

And again: Your aim is to transfer your central points as clear and terse as possible.

Focus On "Take Aways"

A good tip that can be used when preparing the slides is to write down very few (e.g. 1-3) central "take away" points. Examples are "I wrote a fast Quicksort implementation. For most inputs, it is faster than the GNU std::sort." or "I wrote a high quality protein aligner. It is faster than all existing tools and shows comparable quality."

If something in your presentation is not necessary for supporting these core points then ask yourself if they are necessary. Note that this is a very soft rule. You might leave informative or "funny" facts in, for example.

Note that it is also a useful trick when attending talks: Remember and focus on the takeaways. Structure your memory and understanding around these takeaways.

Do Not Overload Your Slides

Chances are good that you are not giving a lecture where the slides are the script. Latter is the only case where it might make sense to fill slides with text. In the most common case, you are not doing this.

Generally, a slide should not have more than a handfull of bullet points and rarely have more than 50 words. In most cases, do not put more than 2 graphics on one slide, and then only if you are making a comparison.

Use A Plain Theme

First, too much gimmicks and playfulness can distract your audience. Second, things like gradients can also interfere with clear regonition. You might also give a thought to what happens if you print out your slides. Unnecessary gradients or images in the background waste ink and if the slides are scaled down might make them unreadable.

It is generally okay, to be playful on the first and last slide which give the title of the talk and say "thank you for your interest" respectively.

Be Consistent

Use the same fonts and colors for the same things everywhere.

MS Sans Serif looks very informal and has not been designed to be easy to read. Your computer ships with fonts that are easy to read and that are probably the default for your slide template anyway.

Graphics become easier to read once one has understood the style. Use the same fonts, axis style in all your graphics if possible. For example, if one graphic has a box around it (matching with the axes) then all graphics should have one.

Italic, Bold and Color Text

Italic and bold text are good means to highlight text for printed work. Black and white is still dominant over color. On slides, however, color can be very useful if used properly.

Highlighting text can be very useful. Consider the example of giving two definitions on one slide that only differ in one or two words. When the difference is highlighted, the slide becomes easy to read. When the difference is not highlighted, the slide becomes very hard to read: Your audience has to read the two texts word by word at the same time to find all differences.

Usage Of Formulas

For formulas, consider the ratio between complexity and importantness.

The arithmetic mean is very simple but probably useless. The geometric mean is very simple and some people might like to be remembered. The normal distribution is pretty complex and probably useless if you are not actually giving a talk on it.

If you give a complex formula on a slide then make sure you understand it and can explain it. If the formula is not so important, consider giving it a symbolic name and putting it in the appendix. When giving the talk, describe roughly what the formula describes and show it when requested only.

If you decide on using formulas, make sure that they are not too painful to read:

  • Use letter consistently, e.g. i and j for indices, n and m for lengths.
  • Make sure mathematical symbols have the correct size, e.g. make sure that $\sqrt{\frac{a}{b}}$ is not typeset as $\frac{\sqrt{a}}{b}$, that overlines go over all symbols they cover etc.
  • Make sure you do not overuse and underuse greek symbols.
  • Sometimes text is easier to understand than a very dense formula.

Data Presentation

In [Tufte, 1983], Tufte gives the principle minimize non-data ink to achieve the previously stated aim of a concise presentation.

Grids

Grids are non-data ink. As a corolary of Tufte's rule, they probably are not a good idea. If you use them, use lighter colors, e.g. gray.

Axe Labels / Tics

Each axe should be labeled with an explanation such as "time", "input size" or more complex descriptions where it makes sense ([Sanders, 1999] gives some examples).

Give the unit for each axe if it makes sense to do so: "time [ns]", "time per operation [ns]", "input size", "input size / 1000" etc.

Use appropriate font sizes: Axe labels and tick marks are useless if they cannot be read! For slides, remember that while beamers are catching up resolution-wise, you are still limited to a lower resolution than on your monitor.

On the x axis, one often uses a logarithmic scale, e.g. when using input sizes of $10^k$ for $k = 1..9$. Since $10^9$ is 1'000'000'000, most plotting tools will abbreviate this to 1E09. It is more readable to actually write $%10^9%$ at the x axis than 1E09. You can do this with gnuplot, for example, using the "enhanced" option in the output driver.

On the y axis, one sometimes has very small or very large values in a relatively small range, e.g. 0.00034 to 0.00045. Many tools would plot this as 34E-04 to 45E-04 which is hard to "parse" for the human mind. Additionally, a small change in the exponent, say between 34E-04 and 34E-03 has a large impact on the represented value. However, it is hard to "parse" this out of the "noise". In this cases, one could simply multiply the values by one thousand, yielding tick marks from 34 to 45. If another graph has a value of 0.00045, this becomes 4.5 and it is easier to see the difference. An axe label "running time [ms]" documents the "zoom".

The range of an axis should cover all realized values and if possible a margin for aesthetics. If you have more than one graph, and the y values are to be compared then make sure that the scale is the same. Otherwise, the advantage of graphics is strongly reduced.

Do not use pie charts. It is much easier for humans to compare lengths than angles or areas.

Tables

The wording of [Sanders, 1998] on tables is concise and comprehensive:

Tables are easier to produce than graphs and perhaps this advantage caues that they are often overused. Tables are more difficult to interpret and too large for large data sets. [...] Nevertheless, tables have their place. Tufte [Tufte, 1983] gives the rule of thumb that "tables usually outperform a graph for small data sets of 20 numbers or less". Tables give very acurate values which make it easiert to check whether some experiments can be reproduced. Furthermore, one sometimes wants to present some quantities, e.g solution quality as a function of problem instances which cannot be meaningfully arranged on the axis of a graph. In that case, a graph or bar chart may look nicer but does not add utility compared to a more accurate way. Furthermore, there may be an apendix or a link to a wb page containing larger tables for detailed documentation of the results.

Checklist

If you can answer any question with "yes" then try to improve your work using this article or [Sanders, 1998].

  • General
    • Was color used inconsistently on one or more slides?
    • Was there a point where color/highlighting could have been used to improve understandability?
    • Was there a formula that was unnecessarily complex?
    • Was there a formula that was too simple?
  • Graphs
    • Was there a graph with too small text?
    • Was there a graph where the axe label did not give the unit where it would have been apropriate?
    • Was there a graph where the caption, x labels and key did not give sufficient explanation?
    • Did the author use a pie chart?
    • Were the graphs with different axis scales where using the same scale could have made them easier to use for comparison?
    • Were points connected by a line without a reason?
    • Was there a graph with excessive complexity?
    • Was a different style used for corresponding curves in different graphs?
  • Slide Layout
    • Did the author use a font that is hard to read?
    • Is there a slide with inconsistent layout (exceptions to fit two graphics, as large as possible are acceptable)?
    • Is there distracting "noise" in the background, such as gradients?
  • Slide Structure
    • Was there a slide with bad structure?
    • Was there a slide with structure that could be improved.
    • Was there a slide with too small text?

Literature

[Sanders, 1999]
Presenting Data from Experiments in Algorithmics. Peter Sanders. Experimental Algorithmics -- From Algorithm Design to Robust and Efficient Software, volume 2547 of LNCS, pages 181-196.
[Tufte, 1983]
The Visual Display of Quantitative Information. Edward E. Tufte. Graphics Press, Cheshire, Connecticut, USA, 1983.

Topic revision: r4 - 14 Mar 2012, AnjaKasseckert
 
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