Ten Simple Rules for Making Good Oral Presentations

Continuing our “Ten Simple Rules” series [1–5], we consider here what it takes to make a good oral presentation. While the rules apply broadly across disciplines, they are certainly important from the perspective of this readership. Clear and logical delivery of your ideas and scientific results is an important component of a successful scientific career. Presentations encourage broader dissemination of your work and highlight work that may not receive attention in written form.


C
hoosing to go to graduate school is a major life decision.Whether you have already made that decision or are about to, now it is time to consider how best to be a successful graduate student.Here are some thoughts from someone who holds these memories fresh in her mind (JG) and from someone who has had a whole career to reflect back on the decisions made in graduate school, both good and bad (PEB).These thoughts taken together, from former student and mentor, represent experiences spanning some 25 or more years.For ease, these experiences are presented as ten simple rules, in approximate order of priority as defined by a number of graduate students we have consulted here in the US; but we hope the rules are more globally applicable, even though length, method of evaluation, and institutional structure of graduate education varies widely.These rules are intended as a companion to earlier editorials covering other areas of professional development [1][2][3][4][5][6][7].

Rule 1: Let Passion Be the Driving Force of Your Success
As with so many other things in life, your heart and then your head should dictate what thesis project makes sense to embark on.Doing your best work requires that you are passionate about what you are doing.Graduate school is an investment of up to a seven-year commitment, a significant chunk of your life.Use the time wisely.The educational system provides a variety of failsafe mechanisms depending on the part of the world where you study.Laboratory rotations and other forms of apprenticeship should not be overlooked, for they are opportunities to test the waters and measure your passion in a given subject area.It is also a chance to test your aptitude for research.Take advantage of it!Research is very different from simply taking courses.If you do not feel excited about doing research and the project selected, do not do it; reevaluate your career decisions.
Rule 2: Select the Right Mentor, Project, and Laboratory Finding the right mentor can be hard since it is not always possible to know the kind of mentoring that is going to work best for you until you actually start doing research.Some of us like to work independently, others like significant feedback and supervision.Talk to other students in the laboratory and get their impressions of how the principle investigator's mentoring works for them.In a large laboratory, chances are you will get less direct mentoring from the principle investigator.In that case, other senior scientists in the laboratory become important.What mentoring are they likely to offer?Judge, as best you can, if the overall environment will work for you.A key element is the standing of your mentor in his or her scientific field.When you graduate, the laboratory you graduate from is going to play a role in determining what opportunities exist for your postdoctoral work, either in academia, industry, or other sectors.Your proposed mentor should be very enthusiastic about the project you discuss.If he or she is not, you have the wrong mentor and/or project.At the same time, beware that such enthusiasm, however senior the mentor, may be misplaced as far as your interests are concerned.Gauge the novelty of the research project and potential for high-quality publications by doing your own background check through reading previously published research and talking to other scientists in related areas.Also consider if the project can be reasonably completed in the allocated time for graduation.To propel your career, you want to come out of a higher degree as a recognized individual having made a significant scientific contribution.Thus, it is absolutely critical that you do take the time to find the project and mentor that is going to fulfill this goal.
Regardless of your initial work habits and how much you depend on your mentor (Rule 2), eventually you will have to be more independent than when you started graduate school.The earlier you start on that path to independence the better.Independence will play a critical part in your career as an innovative scientist.As much as possible define your own research project with a view to make a significant and unique scientific contribution.

Rule 2: Less is More
A common mistake of inexperienced presenters is to try to say too much.They feel the need to prove themselves by proving to the audience that they know a lot.As a result, the main message is often lost, and valuable question time is usually curtailed.Your knowledge of the subject is best expressed through a clear and concise presentation that is provocative and leads to a dialog during the question-and-answer session when the audience becomes active participants.At that point, your knowledge of the material will likely become clear.If you do not get any questions, then you have not been following the other rules.Most likely, your presentation was either incomprehensible or trite.A side effect of too much material is that you talk too quickly, another ingredient of a lost message.

Rule 4: Make the Take-Home Message Persistent
A good rule of thumb would seem to be that if you ask a member of the audience a week later about your presentation, they should be able to remember three points.If these are the key points you were trying to get across, you have done a good job.If they can remember any three points, but not the key points, then your emphasis was wrong.It is obvious what it means if they cannot recall three points!Rule 5: Be Logical Think of the presentation as a story.There is a logical flow-a clear beginning, middle, and an end.You set the stage (beginning), you tell the story (middle), and you have a big finish (the end) where the take-home message is clearly understood.

Rule 6: Treat the Floor as a Stage
Presentations should be entertaining, but do not overdo it and do know your limits.If you are not humorous by nature, do not try and be humorous.If you are not good at telling anecdotes, do not try and tell anecdotes, and so on.A good entertainer will captivate the audience and increase the likelihood of obeying Rule 4.

Rule 7: Practice and Time Your Presentation
This is particularly important for inexperienced presenters.Even more important, when you give the presentation, stick to what you practice.It is common to deviate, and even worse to start presenting material that you know less about than the audience does.The more you practice, the less likely you will be to go off on tangents.Visual cues help here.The more presentations you give, the better you are going to get.In a scientific environment, take every opportunity to do journal club and become a teaching assistant if it allows you to present.An important talk should not be given for the first time to an audience of peers.You should have delivered it to your research collaborators who will be kinder and gentler but still point out obvious discrepancies.Laboratory group meetings are a fine forum for this.

Rule 8: Use Visuals Sparingly but Effectively
Presenters have different styles of presenting.Some can captivate the audience with no visuals (rare); others require visual cues and in addition, depending on the material, may not be able to present a particular topic well without the appropriate visuals such as graphs and charts.Preparing good visual materials will be the subject of a further Ten Simple Rules.Rule 7 will help you to define the right number of visuals for a particular presentation.A useful rule of thumb for us is if you have more than one visual for each minute you are talking, you have too many and you will run over time.Obviously some visuals are quick, others take time to get the message across; again Rule 7 will help.Avoid reading the visual unless you wish to emphasize the point explicitly, the audience can read, too!The visual should support what you are saying either for emphasis or with data to prove the verbal point.Finally, do not overload the visual.Make the points few and clear.

Rule 9: Review Audio and/or Video of Your Presentations
There is nothing more effective than listening to, or listening to and viewing, a presentation you have made.Violations of the other rules will become obvious.Seeing what is wrong is easy, correcting it the next time around is not.You will likely need to break bad habits that lead to the violation of the other rules.Work hard on breaking bad habits; it is important.

Rule 10: Provide Appropriate Acknowledgments
People love to be acknowledged for their contributions.Having many gratuitous acknowledgements degrades the people who actually contributed.If you defy Rule 7, then you will not be able to acknowledge people and organizations appropriately, as you will run out of time.It is often appropriate to acknowledge people at the beginning or at the point of their contribution so that their contributions are very clear.
As a final word of caution, we have found that even in following the Ten Simple Rules (or perhaps thinking we are following them), the outcome of a presentation is not always guaranteed.Audience-presenter dynamics are hard to predict even though the metric of depth and intensity of questions and off-line followup provide excellent indicators.Sometimes you are sure a Rule 1: Read many papers, and learn from both the good and the bad work of others.
It is never too early to become a critic.Journal clubs, where you critique a paper as a group, are excellent for having this kind of dialogue.Reading at least two papers a day in detail (not just in your area of research) and thinking about their quality will also help.Being well read has another potential major benefit-it facilitates a more objective view of one's own work.It is too easy after many late nights spent in front of a computer screen and/or laboratory bench to convince yourself that your work is the best invention since sliced bread.More than likely it is not, and your mentor is prone to falling into the same trap, hence rule 2.
Rule 2: The more objective you can be about your work, the better that work will ultimately become.
Alas, some scientists will never be objective about their own work, and will never make the best scientistslearn objectivity early, the editors and reviewers have.
Rule 3: Good editors and reviewers will be objective about your work.
The quality of the editorial board is an early indicator of the review process.Look at the masthead of the journal in which you plan to publish.Outstanding editors demand and get outstanding reviews.Put your energy into improving the quality of the manuscript before submission.Ideally, the reviews will improve your paper.But they will not get to imparting that advice if there are fundamental flaws.
Rule 4: If you do not write well in the English language, take lessons early; it will be invaluable later.This is not just about grammar, but more importantly comprehension.The best papers are those in which complex ideas are expressed in a way that those who are less than immersed in the field can understand.Have you noticed that the most renowned scientists often give the most logical and simply stated yet stimulating lectures?This extends to their written work as well.Note that writing clearly is valuable, even if your ultimate career does not hinge on producing good scientific papers in English language journals.Submitted papers that are not clearly written in good English, unless the science is truly outstanding, are often rejected or at best slow to publish since they require extensive copyediting.

Rule 5: Learn to live with rejection.
A failure to be objective can make rejection harder to take, and you will be rejected.Scientific careers are full of rejection, even for the best scientists.The correct response to a paper being rejected or requiring major revision is to listen to the reviewers and respond in an objective, not subjective, manner.Reviews reflect how your paper is being judged-learn to live with it.If reviewers are unanimous about the poor quality of the paper, move on-in virtually all cases, they are right.If they request a major revision, do it and address every point they raise both in your cover letter and through obvious revisions to the text.Multiple rounds of revision are painful for all those concerned and slow the publishing process.
Rule 6: The ingredients of good science are obvious-novelty of research topic, comprehensive coverage of the relevant literature, good data, good analysis including strong statistical support, and a thought-provoking discussion.The ingredients of good science reporting are obvious-good organization, the appropriate use of tables and figures, the right length, writing to the intended audiencedo not ignore the obvious.
Be objective about these ingredients when you review the first draft, and do not rely on your mentor.Get a candid opinion by having the paper read by colleagues without a vested interest in the work, including those not directly involved in the topic area.Some would argue that this places too much emphasis on publishing, but it could also be argued that it helps define scope and facilitates hypothesisdriven science.The temptation of novice authors is to try to include everything they know in a paper.Your thesis is/was your kitchen sink.Your papers should be concise, and impart as much information as possible in the least number of words.Be familiar with the guide to authors and follow it, the editors and reviewers do.Maintain a good bibliographic database as you go, and read the papers in it.Reviewing other papers will help you write better papers.To start, work with your mentors; have them give you papers they are reviewing and do the first cut at the review (most mentors will be happy to do this).Then, go through the final review that gets sent in by your mentor, and where allowed, as is true of this journal, look at the reviews others have written.This will provide an important perspective on the quality of your reviews and, hopefully, allow you to see your own work in a more objective way.You will also come to understand the review process and the quality of reviews, which is an important ingredient in deciding where to send your paper.
Rule 9: Decide early on where to try to publish your paper.
This will define the form and level of detail and assumed novelty of the work you are doing.Many journals have a presubmission enquiry system available-use it.Even before the paper is written, get a sense of the novelty of the work, and whether a specific journal will be interested.
Rule 10: Quality is everything.
It is better to publish one paper in a quality journal than multiple papers in lesser journals.Increasingly, it is harder to hide the impact of your papers; tools like Google Scholar and the ISI Web of Science are being used by tenure committees and employers to define metrics for the quality of your work.It used to be that just the journal name was used as a metric.In the digital world, everyone knows if a paper has little impact.Try to publish in journals that have high impact factors; chances are your paper will have high impact, too, if accepted.
When you are long gone, your scientific legacy is, in large part, the literature you left behind and the impact it represents.I hope these ten simple rules can help you leave behind something future generations of scientists will admire.&

Editorial Ten Simple Rules for Better Figures
Nicolas P. Scientific visualization is classically defined as the process of graphically displaying scientific data.However, this process is far from direct or automatic.There are so many different ways to represent the same data: scatter plots, linear plots, bar plots, and pie charts, to name just a few.Furthermore, the same data, using the same type of plot, may be perceived very differently depending on who is looking at the figure.A more accurate definition for scientific visualization would be a graphical interface between people and data.In this short article, we do not pretend to explain everything about this interface; rather, see [1,2] for introductory work.Instead we aim to provide a basic set of rules to improve figure design and to explain some of the common pitfalls.

Rule 1: Know Your Audience
Given the definition above, problems arise when how a visual is perceived differs significantly from the intent of the conveyer.Consequently, it is important to identify, as early as possible in the design process, the audience and the message the visual is to convey.The graphical design of the visual should be informed by this intent.If you are making a figure for yourself and your direct collaborators, you can possibly skip a number of steps in the design process, because each of you knows what the figure is about.However, if you intend to publish a figure in a scientific journal, you should make sure your figure is correct and conveys all the relevant information to a broader audience.Student audiences require special care since the goal for that situation is to explain a concept.In that case, you may have to add extra information to make sure the concept is fully understood.Finally, the general public may be the most difficult audience of all since you need to design a simple, possibly approximated, figure that reveals only the most salient part of your research (Figure 1).This has proven to be a difficult exercise [3].

Rule 2: Identify Your Message
A figure is meant to express an idea or introduce some facts or a result that would be too long (or nearly impossible) to explain only with words, be it for an article or during a time-limited oral presentation.In this context, it is important to clearly identify the role of the figure, i.e., what is the underlying message and how can a figure best express this message?Once clearly identified, this message will be a strong guide for the design of the figure, as shown in Figure 2.Only after identifying the message will it be worth the time to develop your figure, just as you would take the time to craft your words and sentences when writing an article only after deciding on the main points of the text.If your figure is able to convey a striking message at first glance, chances are increased that your article will draw more attention from the community.

Rule 3: Adapt the Figure to the Support Medium
A figure can be displayed on a variety of media, such as a poster, a computer monitor, a projection screen (as in an oral presentation), or a simple sheet of paper (as in a printed article).Each of these media represents different physical sizes for the figure, but more importantly, each of them also implies different ways of viewing and interacting with the figure.For example, during an oral presentation, a figure will be displayed for a limited time.Thus, the viewer must quickly understand what is displayed and what it represents while still listening to your explanation.In such a situation, the figure must be kept simple and the message must be visually salient in order to grab attention, as shown in Figure 3.It is also important to keep in mind that during oral presentations, figures will be video-projected and will be seen from a distance, and figure elements must consequently be made thicker (lines) or bigger (points, text), colors should have strong contrast, and vertical text should be avoided, etc.For a journal article, the situation is totally different, because the reader is able to view the figure as long as necessary.This means a lot of details can be added, along with complementary explanations in the caption.If we take into account the fact that more and more people now read articles on computer screens, they also have the possibility to zoom and drag the figure.Ideally, each type of support medium requires a different figure, and you should abandon the practice of extracting a figure from your article to be put, as is, in your oral presentation.

Rule 4: Captions Are Not Optional
Whether describing an experimental setup, introducing a new model, or presenting new results, you cannot explain everything within the figure itself-a figure should be accompanied by a caption.The caption explains how to read the figure and provides additional precision for what cannot be graphically represented.This can be thought of as the explanation you would give during an oral presentation, or in front of a poster, but with the difference that you must think in advance about the questions people would ask.For example, if you have a bar plot, do not expect the reader to guess the value of the different bars by just looking and measuring relative heights on the figure.If the numeric values are important, they must be provided elsewhere in your article or be written very clearly on the figure.Similarly, if there is a point of interest in the figure (critical domain, specific point, etc.), make sure it is visually distinct but do not hesitate to point it out again in the caption.

Rule 5: Do Not Trust the Defaults
Any plotting library or software comes with a set of default settings.When the end-user does not specify anything, these default settings are used to specify size, font, colors, styles, ticks, markers, etc.
(Figure 4).Virtually any setting can be specified, and you can usually recognize the specific style of each software package (Matlab, Excel, Keynote, etc.) or library (LaTeX, matplotlib, gnuplot, etc.) thanks to the choice of these default settings.Since these settings are to be used for virtually any type of plot, they are not fine-tuned for a specific type of plot.In other words, they are good enough for any plot but they are best for none.All plots require at least some manual tuning of the different settings to better express the message, be it for making a precise plot more salient to a broad audience, or to choose the best colormap for the nature of the data.For example, see [4] for how to go from the default settings to a nicer visual in the case of the matplotlib library.

Rule 6: Use Color Effectively
Color is an important dimension in human vision and is consequently equally important in the design of a scientific figure.However, as explained by Edward Tufte [1], color can be either your greatest ally or your worst enemy if not used properly.If you decide to use color, you should consider which colors to use and where to use them.For example, to highlight some element of a figure, you can use color for this element while keeping other elements gray or black.This provides an enhancing effect.However, if you have no such need, you need to ask yourself, ''Is there any reason this plot is blue and not black?''If you don't know the answer, just keep it black.The same holds true for colormaps.Do not use the default colormap (e.g., jet or rainbow) Figure 1.Know your audience.This is a remake of a figure that was originally published in the New York Times (NYT) in 2007.This new figure was made with matplotlib using approximated data.The data is made of four series (men deaths/cases, women deaths/cases) that could have been displayed using classical double column (deaths/cases) bar plots.However, the layout used here is better for the intended audience.It exploits the fact that the number of new cases is always greater than the corresponding number of deaths to mix the two values.It also takes advantage of the reading direction (English [left-to-right] for NYT) in order to ease comparison between men and women while the central labels give an immediate access to the main message of the figure (cancer).This is a self-contained figure that delivers a clear message on cancer deaths.However, it is not precise.The chosen layout makes it actually difficult to estimate the number of kidney cancer deaths because of its bottom position and the location of the labelled ticks at the top.While this is acceptable for a general-audience publication, it would not be acceptable in a scientific publication if actual numerical values were not given elsewhere in the article.doi:10.1371/journal.pcbi.1003833.g001unless there is an explicit reason to do so (see Figure 5 and [5]).Colormaps are traditionally classified into three main categories: N Sequential: one variation of a unique color, used for quantitative data varying from low to high.
N Diverging: variation from one color to another, used to highlight deviation from a median value.
N Qualitative: rapid variation of colors, used mainly for discrete or categorical data.
Use the colormap that is the most relevant to your data.Lastly, avoid using too many similar colors since color blindness may make it difficult to discern some color differences (see [6] for detailed explanation).

Rule 7: Do Not Mislead the Reader
What distinguishes a scientific figure from other graphical artwork is the presence of data that needs to be shown as objectively as possible.A scientific figure is, by definition, tied to the data (be it an experimental setup, a model, or some results) and if you loosen this tie, you may unintentionally project a different message than intended.However, representing results objectively is not always straightforward.For example, a number of implicit choices made by the library or software you're using that are meant to be accurate in most situations may also mislead the viewer under certain circumstances.If your software automatically rescales values, you might obtain an objective representation of the data (because title, labels, and ticks indicate clearly what is actually displayed) that is nonetheless visually misleading (see bar plot in Figure 6); you have inadvertently misled your readers into visually believing something that does not exist in your data.You can also make explicit choices that are wrong by design, such as using pie charts or 3-D charts to compare quantities.These two kinds of plots are known to induce an incorrect perception of quantities and it requires some expertise to use them properly.As a rule of thumb, make sure to always use the simplest type of plots that can convey your message and make sure to use labels, ticks, title, and the full range of values when relevant.Lastly, do not hesitate to ask colleagues about their interpretation of your figures.Chartjunk refers to all the unnecessary or confusing visual elements found in a figure that do not improve the message (in the best case) or add confusion (in the worst case).For example, chartjunk may include the use of too many colors, too many labels, gratuitously colored backgrounds, useless grid lines, etc. (see left part of Figure 7).The term was first coined by Edward Tutfe in [1], in which he argues that any decorations that do not tell the viewer something new must be banned: ''Regardless of the cause, it is all non-data-ink or redundant data-ink, and it is often chartjunk.''Thus, in order to avoid chartjunk, try to save ink, or electrons in the computing era.Stephen Few reminds us in [7] that graphs should ideally ''represent all the data that is needed to see and understand what's meaningful.''However, an element that could be considered chartjunk in one  1{y), y §0) where each particle interacts with the other.Depending on the initial conditions, the system may end up in three different states.The left figure has been prepared for a journal article where the reader is free to look at every detail.The red color has been used consistently to indicate both initial conditions (red dots in the zoomed panel) and trajectories (red lines).Line transparency has been increased in order to highlight regions where trajectories overlap (high color density).The right figure has been prepared for an oral presentation.Many details have been removed (reduced number of trajectories, no overlapping trajectories, reduced number of ticks, bigger axis and tick labels, no title, thicker lines) because the time-limited display of this figure would not allow for the audience to scrutinize every detail.Furthermore, since the figure will be described during the oral presentation, some parts have been modified to make them easier to reference (e.g., the yellow box, the red dashed line).doi:10.1371/journal.pcbi.1003833.g003On the upper left part, we used the disc area to represent the value, while in the bottom part we used the disc radius.Results are visually very different.In the latter case (red circles), the last value (10) appears very small compared to the first one (30), while the ratio between the two values is only 3:1.This situation is actually very frequent in the literature because the command (or interface) used to produce circles or scatter plots (with varying point sizes) offers to use the radius as default to specify the disc size.It thus appears logical to use the value for the radius, but this is misleading.On the right part of the figure, we display a series of ten values using the full range for values on the top part (y axis goes from 0 to 100) or a partial range in the bottom part (y axis goes from 80 to 100), and we explicitly did not label the y-axis to enhance the confusion.The visual perception of the two series is totally different.In the top part (black series), we tend to interpret the values as very similar, while in the bottom part, we tend to believe there are significant differences.Even if we had used labels to indicate the actual range, the effect would persist because the bars are the most salient information on these figures.doi:10.1371/journal.pcbi.1003833.g006We have seven series of samples that are equally important, and we would like to show them all in order to visually compare them (exact signal values are supposed to be given elsewhere).The left figure demonstrates what is certainly one of the worst possible designs.All the curves cover each other and the different colors (that have been badly and automatically chosen by the software) do not help to distinguish them.The legend box overlaps part of the graphic, making it impossible to check if there is any interesting information in this area.There are far too many ticks: x labels overlap each other, making them unreadable, and the three-digit precision does not seem to carry any significant information.Finally, the grid does not help because (among other criticisms) it is not aligned with the signal, which can be considered discrete given the small number of sample points.The right figure adopts a radically different layout while using the same area on the sheet of paper.Series have been split into seven plots, each of them showing one series, while other series are drawn very lightly behind the main one.Series labels have been put on the left of each plot, avoiding the use of colors and a legend box.The number of x ticks has been reduced to three, and a thin line indicates these three values for all plots.Finally, y ticks have been completely removed and the height of the gray background boxes indicate the [21,+1] range (this should also be indicated in the figure caption if it were to be used in an article).doi:10.1371/journal.pcbi.1003833.g007Figure 8. Message trumps beauty.This figure is an extreme case where the message is particularly clear even if the aesthetic of the figure is questionable.The uncanny valley is a well-known hypothesis in the field of robotics that correlates our comfort level with the human-likeness of a robot.To express this hypothetical nature, hypothetical data were used (y~x 2 {5e {5(x{2) 2 ) and the figure was given a sketched look (xkcd filter on matplotlib) associated with a cartoonish font that enhances the overall effect.Tick labels were also removed since only the overall shape of the curve matters.Using a sketch style conveys to the viewer that the data is approximate, and that it is the higher-level concepts rather than low-level details that are important [10].doi:10.1371/journal.pcbi.1003833.g008graphics, design, and art are becoming thinner and thinner [9].There exists a myriad of online graphics in which aesthetic is the first criterion and content comes in second place.Even if a lot of those graphics might be considered beautiful, most of them do not fit the scientific framework.Remember, in science, message and readability of the figure is the most important aspect while beauty is only an option, as dramatically shown in Figure 8.

Rule 10: Get the Right Tool
There exist many tools that can make your life easier when creating figures, and knowing a few of them can save you a lot of time.Depending on the type of visual you're trying to create, there is generally a dedicated tool that will do what you're trying to achieve.It is important to understand at this point that the software or library you're using to make a visualization can be different from the software or library you're using to conduct your research and/or analyze your data.You can always export data in order to use it in another tool.Whether drawing a graph, designing a schema of your experiment, or plotting some data, there are open-source tools for you.They're just waiting to be found and used.Below is a small list of open-source tools.
Matplotlib is a python plotting library, primarily for 2-D plotting, but with some

Citation:
Bourne PE (2007) Ten simple rules for making good oral presentations.PLoS Comput Biol 3(4): e77.doi:10.1371/journal.pcbi.0030077Copyright: Ó 2007 Philip E. Bourne.This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Dr. Philip E. Bourne is a Professor in the Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America.E-mail: bourne@sdsc.edu

Rule 7 :
Start writing the paper the day you have the idea of what questions to pursue.
Citation: Bourne PE (2005) Ten simple rules for getting published.PLoS Comput Biol 1(5): e57.Copyright: Ó 2005 Philip E. Bourne.This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.DOI: 10.1371/journal.pcbi.0010057Philip E. Bourne is Editor-in-Chief of PLoS Computational Biology.E-mail: bourne@sdsc.eduRule 8: Become a reviewer early in your career.

Citation:
Rougier NP, Droettboom M, Bourne PE (2014) Ten Simple Rules for Better Figures.PLoS Comput Biol 10(9): e1003833.doi:10.1371/journal.pcbi.1003833Published September 11, 2014This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.The work is made available under the Creative Commons CC0 public domain dedication.Editor: Scott Markel, Accelrys, United States of America Competing Interests: The authors have declared that no competing interests exist.* Email: Nicolas.Rougier@inria.frFunding: The authors received no specific funding for this article.

Figure 2 .
Figure 2. Identify your message.The superior colliculus (SC) is a brainstem structure at the crossroads of multiple functional pathways.Several neurophysiological studies suggest that the population of active neurons in the SC encodes the location of a visual target that induces saccadic eye movement.The projection from the retina surface (on the left) to the collicular surface (on the right) is based on a standard and quantitative model in which a logarithmic mapping function ensures the projection from retinal coordinates to collicular coordinates.This logarithmic mapping plays a major role in saccade decision.To better illustrate this role, an artificial checkerboard pattern has been used, even though such a pattern is not used during experiments.This checkerboard pattern clearly demonstrates the extreme magnification of the foveal region, which is the main message of the figure.doi:10.1371/journal.pcbi.1003833.g002

Figure 3 .
Figure 3. Adapt the figure to the support medium.These two figures represent the same simulation of the trajectories of a dual-particle system ( dx dt ~(1 = 4 z(x{y))(1{x), x §0, dy dt ~(1 = 4 z(y{x))(1{y), y §0) where each particle interacts with the other.Depending on the initial conditions, the system may end up in three different states.The left figure has been prepared for a journal article where the reader is free to look at every detail.The red color has been used consistently to indicate both initial conditions (red dots in the zoomed panel) and trajectories (red lines).Line transparency has been increased in order to highlight regions where trajectories overlap (high color density).The right figure has been prepared for an oral presentation.Many details have been removed (reduced number of trajectories, no overlapping trajectories, reduced number of ticks, bigger axis and tick labels, no title, thicker lines) because the time-limited display of this figure would not allow for the audience to scrutinize every detail.Furthermore, since the figure will be described during the oral presentation, some parts have been modified to make them easier to reference (e.g., the yellow box, the red dashed line).doi:10.1371/journal.pcbi.1003833.g003

Figure 4 .
Figure 4. Do not trust the defaults.The left panel shows the sine and cosine functions as rendered by matplotlib using default settings.While this figure is clear enough, it can be visually improved by tweaking the various available settings, as shown on the right panel.doi:10.1371/journal.pcbi.1003833.g004

Rule 9 :
Figures have been used in scientific literature since antiquity.Over the years, a lot of progress has been made, and each scientific domain has developed its own set of best practices.It is important to know these standards, because they facilitate a more direct comparison between models, studies, or experiments.More importantly, they can help you to spot obvious errors in your results.However, most of the time,

Figure 5 .
Figure 5. Use color effectively.This figure represents the same signal, whose frequency increases to the right and intensity increases towards the bottom, using three different colormaps.The rainbow colormap (qualitative) and the seismic colormap (diverging) are equally bad for such a signal because they tend to hide details in the high frequency domain (bottom-right part).Using a sequential colormap such as the purple one, it is easier to see details in the high frequency domain.Adapted from [5].doi:10.1371/journal.pcbi.1003833.g005

Figure 6 .
Figure 6.Do not mislead the reader.On the left part of the figure, we represented a series of four values: 30, 20, 15, 10.On the upper left part, we used the disc area to represent the value, while in the bottom part we used the disc radius.Results are visually very different.In the latter case (red circles), the last value(10) appears very small compared to the first one (30), while the ratio between the two values is only 3:1.This situation is actually very frequent in the literature because the command (or interface) used to produce circles or scatter plots (with varying point sizes) offers to use the radius as default to specify the disc size.It thus appears logical to use the value for the radius, but this is misleading.On the right part of the figure, we display a series of ten values using the full range for values on the top part (y axis goes from 0 to 100) or a partial range in the bottom part (y axis goes from 80 to 100), and we explicitly did not label the y-axis to enhance the confusion.The visual perception of the two series is totally different.In the top part (black series), we tend to interpret the values as very similar, while in the bottom part, we tend to believe there are significant differences.Even if we had used labels to indicate the actual range, the effect would persist because the bars are the most salient information on these figures.doi:10.1371/journal.pcbi.1003833.g006

Figure 7 .
Figure 7. Avoid chartjunk.We have seven series of samples that are equally important, and we would like to show them all in order to visually compare them (exact signal values are supposed to be given elsewhere).The left figure demonstrates what is certainly one of the worst possible designs.All the curves cover each other and the different colors (that have been badly and automatically chosen by the software) do not help to distinguish them.The legend box overlaps part of the graphic, making it impossible to check if there is any interesting information in this area.There are far too many ticks: x labels overlap each other, making them unreadable, and the three-digit precision does not seem to carry any significant information.Finally, the grid does not help because (among other criticisms) it is not aligned with the signal, which can be considered discrete given the small number of sample points.The right figure adopts a radically different layout while using the same area on the sheet of paper.Series have been split into seven plots, each of them showing one series, while other series are drawn very lightly behind the main one.Series labels have been put on the left of each plot, avoiding the use of colors and a legend box.The number of x ticks has been reduced to three, and a thin line indicates these three values for all plots.Finally, y ticks have been completely removed and the height of the gray background boxes indicate the [21,+1] range (this should also be indicated in the figure caption if it were to be used in an article).doi:10.1371/journal.pcbi.1003833.g007 Rule 4: Remember, Life Is All about Balance The other Ten Simple Rules editorials are a start here[1][2][3][4][5][6][7], but you need to work on developing these skills at the same time as you work on your thesis.The second part involves using these emergent skills to figure out what to do with the higher postgraduate degree.Do not wait until you graduate to take the next step.Have a position and a fellowship, if possible, lined up ahead of time.If graduate school wasn't quite what you thought it would be, be it scientifically or otherwise, find out what your options are to address the problem.Discuss these problems with your mentors.A good mentor is there not just to guide you scientifically, but also in your personal development.Remember, they have been there themselves and have likely seen similar issues with earlier students.Take time off to reflect on your future if this is needed.A good mentor will understand that you come first.
presentation will go well, and afterward you feel it did not go well.Other times you dread what the audience will think, and you come away pleased as punch.Such is life.As always, we welcome your comments on these Ten Simple Rules by Reader Response.&