Editorial

Ten Simple Rules for Making Good OralPresentationsPhilip E. Bourne

Continuing our ‘‘Ten SimpleRules’’ series [1–5], we considerhere what it takes to make a

good oral presentation. While the rulesapply broadly across disciplines, theyare certainly important from theperspective of this readership. Clearand logical delivery of your ideas andscientific results is an importantcomponent of a successful scientificcareer. Presentations encouragebroader dissemination of your workand highlight work that may notreceive attention in written form.

Rule 1: Talk to the AudienceWe do not mean face the audience,

although gaining eye contact with asmany people as possible when youpresent is important since it adds alevel of intimacy and comfort to thepresentation. We mean preparepresentations that address the targetaudience. Be sure you know who youraudience is—what are theirbackgrounds and knowledge level ofthe material you are presenting andwhat they are hoping to get out of thepresentation? Off-topic presentationsare usually boring and will not endearyou to the audience. Deliver what theaudience wants to hear.

Rule 2: Less is MoreA common mistake of

inexperienced presenters is to try tosay too much. They feel the need toprove themselves by proving to theaudience that they know a lot. As aresult, the main message is often lost,and valuable question time is usuallycurtailed. Your knowledge of thesubject is best expressed through aclear and concise presentation that isprovocative and leads to a dialogduring the question-and-answersession when the audience becomesactive participants. At that point, yourknowledge of the material will likelybecome clear. If you do not get anyquestions, then you have not beenfollowing the other rules. Most likely,

your presentation was eitherincomprehensible or trite. A sideeffect of too much material is that youtalk too quickly, another ingredient ofa lost message.

Rule 3: Only Talk When You HaveSomething to Say

Do not be overzealous about whatyou think you will have available topresent when the time comes. Researchnever goes as fast as you would like.Remember the audience’s time isprecious and should not be abused bypresentation of uninterestingpreliminary material.

Rule 4: Make the Take-HomeMessage Persistent

A good rule of thumb would seem tobe that if you ask a member of theaudience a week later about yourpresentation, they should be able toremember three points. If these are thekey points you were trying to getacross, you have done a good job. Ifthey can remember any three points,but not the key points, then youremphasis was wrong. It is obvious whatit means if they cannot recall threepoints!

Rule 5: Be LogicalThink of the presentation as a story.

There is a logical flow—a clearbeginning, middle, and an end. You setthe stage (beginning), you tell the story(middle), and you have a big finish (theend) where the take-home message isclearly understood.

Rule 6: Treat the Floor as a StagePresentations should be

entertaining, but do not overdo it anddo know your limits. If you are nothumorous by nature, do not try and behumorous. If you are not good attelling anecdotes, do not try and tellanecdotes, and so on. A goodentertainer will captivate the audienceand increase the likelihood of obeyingRule 4.

Rule 7: Practice and Time YourPresentation

This is particularly important forinexperienced presenters. Even moreimportant, when you give thepresentation, stick to what youpractice. It is common to deviate, andeven worse to start presenting materialthat you know less about than theaudience does. The more you practice,the less likely you will be to go off ontangents. Visual cues help here. Themore presentations you give, the betteryou are going to get. In a scientificenvironment, take every opportunity todo journal club and become a teachingassistant if it allows you to present. Animportant talk should not be given forthe first time to an audience of peers.You should have delivered it to yourresearch collaborators who will bekinder and gentler but still point outobvious discrepancies. Laboratorygroup meetings are a fine forum forthis.

Rule 8: Use Visuals Sparingly butEffectively

Presenters have different styles ofpresenting. Some can captivate theaudience with no visuals (rare); othersrequire visual cues and in addition,depending on the material, may not beable to present a particular topic wellwithout the appropriate visuals such asgraphs and charts. Preparing goodvisual materials will be the subject of afurther Ten Simple Rules. Rule 7 will

Citation: Bourne PE (2007) Ten simple rules formaking good oral presentations. PLoS Comput Biol3(4): e77. doi:10.1371/journal.pcbi.0030077

Copyright: � 2007 Philip E. Bourne. This is an open-access article distributed under the terms of theCreative Commons Attribution License, whichpermits unrestricted use, distribution, andreproduction in any medium, provided the originalauthor and source are credited.

Dr. Philip E. Bourne is a Professor in the Departmentof Pharmacology, University of California San Diego,La Jolla, California, United States of America. E-mail:bourne@sdsc.edu

PLoS Computational Biology | www.ploscompbiol.org April 2007 | Volume 3 | Issue 4 | e770593

help you to define the right number ofvisuals for a particular presentation. Auseful rule of thumb for us is if youhave more than one visual for eachminute you are talking, you have toomany and you will run over time.Obviously some visuals are quick,others take time to get the messageacross; again Rule 7 will help. Avoidreading the visual unless you wish toemphasize the point explicitly, theaudience can read, too! The visualshould support what you are sayingeither for emphasis or with data toprove the verbal point. Finally, do notoverload the visual. Make the pointsfew and clear.

Rule 9: Review Audio and/or Video ofYour Presentations

There is nothing more effective thanlistening to, or listening to andviewing, a presentation you havemade. Violations of the other rules willbecome obvious. Seeing what is wrongis easy, correcting it the next timearound is not. You will likely need tobreak bad habits that lead to the

violation of the other rules. Work hardon breaking bad habits; it isimportant.

Rule 10: Provide AppropriateAcknowledgments

People love to be acknowledged fortheir contributions. Having manygratuitous acknowledgements degradesthe people who actually contributed. Ifyou defy Rule 7, then you will not beable to acknowledge people andorganizations appropriately, as you willrun out of time. It is often appropriateto acknowledge people at thebeginning or at the point of theircontribution so that theircontributions are very clear.

As a final word of caution, we havefound that even in following the TenSimple Rules (or perhaps thinking weare following them), the outcome of apresentation is not always guaranteed.Audience–presenter dynamics are hardto predict even though the metric ofdepth and intensity of questions andoff-line followup provide excellentindicators. Sometimes you are sure a

presentation will go well, andafterward you feel it did not go well.Other times you dread what theaudience will think, and you comeaway pleased as punch. Such is life. Asalways, we welcome your comments onthese Ten Simple Rules by ReaderResponse. &

Acknowledgments

The idea for this particular Ten SimpleRules was inspired by a conversation withFiona Addison.

Funding. The author received no specificfunding for this article.

Competing interests. The author has declaredthat no competing interests exist.

References1. Bourne PE (2005) Ten simple rules for getting

published. PLoS Comp Biol 1: e57.2. Bourne PE, Chalupa LM (2006) Ten simple

rules for getting grants. PLoS Comp Biol 2:e12.

3. Bourne PE, Korngreen A (2006) Ten simplerules for reviewers. PLoS Comp Biol 2: e110.

4. Bourne PE, Friedberg I (2006) Ten simple rulesfor selecting a postdoctoral fellowship. PLoSComp Biol 2: e121.

5. Vicens Q, Bourne PE (2007) Ten simple rulesfor a successful collaboration. PLoS Comp Biol3: e44.

PLoS Computational Biology | www.ploscompbiol.org April 2007 | Volume 3 | Issue 4 | e770594

Editorial

Ten Simple Rules for a Good PosterPresentationThomas C. Erren*, Philip E. Bourne

P osters are a key component ofcommunicating your scienceand an important element in a

successful scientific career. Posters,while delivering the same high-qualityscience, offer a different medium fromeither oral presentations [1] orpublished papers [2], and should betreated accordingly. Posters should beconsidered a snapshot of your workintended to engage colleagues in adialog about the work, or, if you are notpresent, to be a summary that willencourage the reader to want to learnmore. Many a lifelong collaboration [3]has begun in front of a poster board.Here are ten simple rules formaximizing the return on the time-consuming process of preparing andpresenting an effective poster.

Rule 1: Define the PurposeThe purpose will vary depending on

the status and nature of the work beingpresented, as well as the intent. Someposters are designed to be used againand again; for example, those makingconference attendees aware of a sharedresource. Others will likely be usedonce at a conference and then berelegated to the wall in the laboratory.Before you start preparing the poster,ask yourself the following questions:What do you want the person passingby your poster to do? Engage in adiscussion about the content? Learnenough to go off and want to trysomething for themselves? Want tocollaborate? All the above, or none ofthe above but something else? Styleyour poster accordingly.

Rule 2: Sell Your Work in Ten SecondsSome conferences will present

hundreds of posters; you will need tofight for attention. The firstimpressions of your poster, and to alesser extent what you might say whenstanding in front of it, are crucial. It isanalogous to being in an elevator andhaving a few seconds to peak someone’sinterest before they get off. The sad

truth is that you have to sell your work.One approach is to pose your work asaddressing a decisive question, whichyou then address as best you can. Onceyou have posed the question, whichmay well also be the motivation for thestudy, the focus of your poster shouldbe on addressing that question in aclear and concise way.

Rule 3: The Title Is Important

The title is a good way to sell yourwork. It may be the only thing theconference attendee sees before theyreach your poster. The title shouldmake them want to come and visit.The title might pose a decisivequestion, define the scope of the study,or hint at a new finding. Above all, thetitle should be short andcomprehensible to a broad audience.The title is your equivalent of anewspaper headline—short, sharp, andcompelling.

Rule 4: Poster AcceptanceMeans Nothing

Do not take the acceptance of aposter as an endorsement of your work.Conferences need attendees to befinancially viable. Many attendees whoare there on grants cannot justifyattending a conference unless theypresent. There are a small number ofspeaking slots compared withattendees. How to solve the dilemma?Enter posters; this way everyone canpresent. In other words, your posterhas not been endorsed, just accepted.To get endorsement from your peers,do good science and present it well onthe poster.

Rule 5: Many of the Rules for Writinga Good Paper Apply to Posters, Too

Identify your audience and providethe appropriate scope and depth ofcontent. If the conference includesnonspecialists, cater to them. Just as theabstract of a paper needs to be asuccinct summary of the motivation,

hypothesis to be tested, major results,and conclusions, so does your poster.

Rule 6: Good Posters Have UniqueFeatures Not Pertinent to Papers

The amount of material presented ina paper far outweighs what is presentedon a poster. A poster requires you todistill the work, yet not lose themessage or the logical flow. Postersneed to be viewed from a distance, butcan take advantage of your presence.Posters can be used as a distributionmedium for copies of associatedpapers, supplementary information,and other handouts. Posters allow youto be more speculative. Often only thetitles or at most the abstracts of posterscan be considered published; that is,widely distributed. Mostly, they maynever be seen again. There is theopportunity to say more than youwould in the traditional literature,which for all intents and purposes willbe part of the immutable record. Takeadvantage of these unique features.

Rule 7: Layout and FormatAre Critical

Pop musician Keith Richards put thematter well in an interview with DerSpiegel [4]: ‘‘If you are a painter, thenthe most important thing is the barecanvas. A good painter will never coverall the space but will always leave some

Citation: Erren TC, Bourne PE (2007) Ten simple rulesfor a good poster presentation. PLoS Comput Biol3(5): e102. doi:10.1371/journal.pcbi.0030102

Copyright: � 2007 Erren and Bourne. This is anopen-access article distributed under the terms ofthe Creative Commons Attribution License, whichpermits unrestricted use, distribution, andreproduction in any medium, provided the originalauthor and source are credited.

Thomas C. Erren is with the Institute and Policlinic forOccupational and Social Medicine, School ofMedicine and Dentistry, University of Cologne,Lindenthal, Germany. Philip E. Bourne is a Professorin the Department of Pharmacology, University ofCalifornia San Diego, La Jolla, California, UnitedStates of America.

* To whom correspondence should be addressed.E-mail: tim.erren@uni-koeln.de

PLoS Computational Biology | www.ploscompbiol.org May 2007 | Volume 3 | Issue 5 | e1020777

blank. My canvas is silence.’’ Yourcanvas as poster presenter is also whitespace. Guide the passerby’s eyes fromone succinct frame to another in alogical fashion from beginning to end.Unlike the literature, which is linear byvirtue of one page following another,the reader of a poster is free to wanderover the pages as if they are tacked tothe poster board in a random order.Guide the reader with arrows,numbering, or whatever else makessense in getting them to move from onelogical step to another. Try to do thisguiding in an unusual and eye-catchingway. Look for appropriate layouts inthe posters of others and adopt some oftheir approaches. Finally, never use lessthan a size 24 point font, and make surethe main points can be read at eye level.

Rule 8: Content Is Important, butKeep It Concise

Everything on the poster should helpconvey the message. The text mustconform to the norms of soundscientific reporting: clarity, precisionof expression, and economy of words.The latter is particularly important forposters because of their inherent spacelimitations. Use of first-rate pictorialmaterial to illustrate a poster cansometimes transform what wouldotherwise be a bewildering mass ofcomplex data into a coherent andconvincing story. One carefullyproduced chart or graph often saysmore than hundreds of words. Usegraphics for ‘‘clear portrayal ofcomplexity’’ [5], not to impress (andpossibly bewilder) viewers withcomplex artistry. Allow a figure to beviewed in both a superficial and adetailed way. For example, a large tablemight have bold swaths of colorindicating relative contributions fromdifferent categories, and the smallertext in the table would provide grittydetails for those who want them.Likewise, a graph could provide a boldtrend line (with its interpretationclearly and concisely stated), and alsohave many detailed points with errorbars. Have a clear and obvious set ofconclusions—after the abstract, this is

where the passerby’s eyes will wander.Only then will they go to the results,followed by the methods.

Rule 9: Posters Should HaveYour Personality

A poster is a different medium from apaper, which is conventionally dry andimpersonal. Think of your poster as anextension of your personality. Use it todraw the passerby to take a closer lookor to want to talk to you. Scientificcollaboration often starts for reasonsother than the shared scientific interest,such as a personal interest. A photo ofyou on the poster not only helpssomeone find you at the conferencewhen you are not at the poster, it canalso be used to illustrate a hobby or aninterest that can open a conversation.

Rule 10: The Impact of a PosterHappens Both During and After thePoster Session

When the considerable effort ofmaking a poster is done, do not blowit on presentation day by failing tohave the poster achieve maximumimpact. This requires the rightpresenter–audience interaction. Workto get a crowd by being engaging; oneengaged viewer will attract others.Don’t badger people, let them read. Beready with Rule 2. Work all theaudience at once, do not leave visitorswaiting for your attention. Make eyecontact with every visitor.

Make it easy for a conferenceattendee to contact you afterward.Have copies of relevant papers on handas well as copies of the poster onstandard-sized paper. For work that ismore mature, have the poster onlineand make the URL available as ahandout. Have your e-mail and otherdemographics clearly displayed. Followup with people who come to the posterby having a signup sheet.

The visitor is more likely toremember you than the content of yourposter. Make yourself easy toremember. As the host of the workpresented on the poster, be attentive,open, and curious, and self-confidentbut never arrogant and aggressive.

Leave the visitors space and time—theycan ‘‘travel’’ through your poster attheir own discretion and pace. If avisitor asks a question, talk simply andopenly about the work. This is likelyyour opportunity to get feedback onthe work before it goes to publication.Better to be tripped up in front of yourposter than by a reviewer of themanuscript.

Good posters and their presentationscan improve your reputation, bothwithin and outside your working groupand institution, and may alsocontribute to a certain scientificfreedom. Poster prizes count whenpeers look at your resume.

These ten rules will hopefully helpyou in preparing better posters. For amore humorous view on what not to doin preparing a poster, see [6], and forfurther information, including theopportunity to practice your German,see [7]. &

Acknowledgments

Thomas Erren’s contributions to this pieceare based on [7] and were stimulated byexchanges with Michael Jacobsen. Thanksalso to Steven E. Brenner for useful input.

Funding. The authors received no specificfunding for this article.

Competing interests. The authors havedeclared that no competing interests exist.

References1. Bourne PE (2007) Ten simple rules for making

good oral presentations. PLoS Comput Biol 3:e77. doi:10.1371/journal.pcbi.0030077

2. Bourne PE (2005) Ten simple rules for gettingpublished. PLoS Comput Biol 1: e57. doi:10.1371/journal.pcbi.0010057

3. Vicens Q, Bourne PE (2007) Ten simple rulesfor a successful collaboration. PLoS ComputBiol 3: e44. doi:10.1371/journal.pcbi.0030044

4. (1998) Interview with Keith Richards. MeineLeinwand ist die Stille. Der Spiegel 45: 167–170.

5. Tufte ER (2001) The visual display ofquantitative information. Cheshire(Connecticut): Graphics Press. p. 191.

6. Wolcott TG (1997) Mortal sins in posterpresentations or how to give the poster no oneremembers. Newsletter Soc Integr ComparBiol Fall: 10–11. Available: http://www.sicb.org/newsletters/fa97nl/sicb/poster.html. Accessed23 April 2007.

7. Erren TC (2006). Schau mich an! Ein Leitfadenzur Erstellung und Prasentation von Postern inder Medizin und den Naturwissenschaften.Munchen/Wien/New York: W. ZuckschwerdtVerlag.

PLoS Computational Biology | www.ploscompbiol.org May 2007 | Volume 3 | Issue 5 | e1020778

Editorial

Ten Simple Rules for Writing a Literature ReviewMarco Pautasso1,2*

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France, 2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand

in most scientific fields. Their need stems

from the ever-increasing output of scien-

tific publications [1]. For example, com-

pared to 1991, in 2008 three, eight, and

forty times more papers were indexed in

Web of Science on malaria, obesity, and

biodiversity, respectively [2]. Given such

mountains of papers, scientists cannot be

expected to examine in detail every single

new paper relevant to their interests [3].

Thus, it is both advantageous and neces-

sary to rely on regular summaries of the

recent literature. Although recognition for

scientists mainly comes from primary

research, timely literature reviews can lead

to new synthetic insights and are often

widely read [4]. For such summaries to be

useful, however, they need to be compiled

in a professional way [5].

When starting from scratch, reviewing

the literature can require a titanic amount

of work. That is why researchers who have

spent their career working on a certain

research issue are in a perfect position to

review that literature. Some graduate

schools are now offering courses in

reviewing the literature, given that most

research students start their project by

producing an overview of what has

already been done on their research issue

[6]. However, it is likely that most

scientists have not thought in detail about

how to approach and carry out a literature

review.

Reviewing the literature requires the

ability to juggle multiple tasks, from

finding and evaluating relevant material

to synthesising information from various

sources, from critical thinking to para-

phrasing, evaluating, and citation skills [7].

In this contribution, I share ten simple

rules I learned working on about 25

literature reviews as a PhD and postdoc-

toral student. Ideas and insights also come

from discussions with coauthors and

colleagues, as well as feedback from

reviewers and editors.

Rule 1: Define a Topic andAudience

How to choose which topic to review?

There are so many issues in contemporary

science that you could spend a lifetime of

attending conferences and reading the

literature just pondering what to review.

On the one hand, if you take several years

to choose, several other people may have

had the same idea in the meantime. On

the other hand, only a well-considered

topic is likely to lead to a brilliant literature

review [8]. The topic must at least be:

(i) interesting to you (ideally, you

should have come across a series of

recent papers related to your line of

work that call for a critical summa-

ry),

(ii) an important aspect of the field (so

that many readers will be interested

in the review and there will be

enough material to write it), and

(iii) a well-defined issue (otherwise you

could potentially include thousands

of publications, which would make

the review unhelpful).

Ideas for potential reviews may come

from papers providing lists of key research

questions to be answered [9], but also

from serendipitous moments during des-

ultory reading and discussions. In addition

to choosing your topic, you should also

select a target audience. In many cases, the

topic (e.g., web services in computational

biology) will automatically define an

audience (e.g., computational biologists),

but that same topic may also be of interest

to neighbouring fields (e.g., computer

science, biology, etc.).

Rule 2: Search and Re-searchthe Literature

After having chosen your topic and

audience, start by checking the literature

and downloading relevant papers. Five

pieces of advice here:

(i) keep track of the search items you

use (so that your search can be

replicated [10]),

(ii) keep a list of papers whose pdfs you

cannot access immediately (so as to

retrieve them later with alternative

strategies),

(iii) use a paper management system

(e.g., Mendeley, Papers, Qiqqa,

Sente),

(iv) define early in the process some

criteria for exclusion of irrelevant

papers (these criteria can then be

described in the review to help

define its scope), and

(v) do not just look for research papers

in the area you wish to review, but

also seek previous reviews.

The chances are high that someone will

already have published a literature review

(Figure 1), if not exactly on the issue you

are planning to tackle, at least on a related

topic. If there are already a few or several

reviews of the literature on your issue, my

advice is not to give up, but to carry on

with your own literature review,

(i) discussing in your review the ap-

proaches, limitations, and conclu-

sions of past reviews,

(ii) trying to find a new angle that has

not been covered adequately in the

previous reviews, and

(iii) incorporating new material that has

inevitably accumulated since their

appearance.

Citation: Pautasso M (2013) Ten Simple Rules for Writing a Literature Review. PLoS Comput Biol 9(7):e1003149. doi:10.1371/journal.pcbi.1003149

Editor: Philip E. Bourne, University of California San Diego, United States of America

Published July 18, 2013

Copyright: � 2013 Marco Pautasso. This is an open-access article distributed under the terms of the CreativeCommons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,provided the original author and source are credited.

Funding: This work was funded by the French Foundation for Research on Biodiversity (FRB) through itsCentre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. Thefunders had no role in the preparation of the manuscript.

Competing Interests: The author has declared that no competing interests exist.

* E-mail: marpauta@gmail.com

PLOS Computational Biology | www.ploscompbiol.org 1 July 2013 | Volume 9 | Issue 7 | e1003149

When searching the literature for per-

tinent papers and reviews, the usual rules

apply:

(i) be thorough,

(ii) use different keywords and database

sources (e.g., DBLP, Google Schol-

ar, ISI Proceedings, JSTOR Search,

Medline, Scopus, Web of Science),

and

(iii) look at who has cited past relevant

papers and book chapters.

Rule 3: Take Notes WhileReading

If you read the papers first, and only

afterwards start writing the review, you

will need a very good memory to remem-

ber who wrote what, and what your

impressions and associations were while

reading each single paper. My advice is,

while reading, to start writing down

interesting pieces of information, insights

about how to organize the review, and

thoughts on what to write. This way, by

the time you have read the literature you

selected, you will already have a rough

draft of the review.

Of course, this draft will still need much

rewriting, restructuring, and rethinking to

obtain a text with a coherent argument

[11], but you will have avoided the danger

posed by staring at a blank document. Be

careful when taking notes to use quotation

marks if you are provisionally copying

verbatim from the literature. It is advisable

then to reformulate such quotes with your

own words in the final draft. It is

important to be careful in noting the

references already at this stage, so as to

avoid misattributions. Using referencing

software from the very beginning of your

endeavour will save you time.

Rule 4: Choose the Type ofReview You Wish to Write

After having taken notes while reading

the literature, you will have a rough idea

of the amount of material available for the

review. This is probably a good time to

decide whether to go for a mini- or a full

review. Some journals are now favouring

the publication of rather short reviews

focusing on the last few years, with a limit

on the number of words and citations. A

mini-review is not necessarily a minor

review: it may well attract more attention

from busy readers, although it will inevi-

tably simplify some issues and leave out

some relevant material due to space

limitations. A full review will have the

advantage of more freedom to cover in

detail the complexities of a particular

scientific development, but may then be

left in the pile of the very important papers

‘‘to be read’’ by readers with little time to

spare for major monographs.

There is probably a continuum between

mini- and full reviews. The same point

applies to the dichotomy of descriptive vs.

integrative reviews. While descriptive re-

views focus on the methodology, findings,

and interpretation of each reviewed study,

integrative reviews attempt to find com-

mon ideas and concepts from the reviewed

material [12]. A similar distinction exists

between narrative and systematic reviews:

while narrative reviews are qualitative,

systematic reviews attempt to test a

hypothesis based on the published

evidence, which is gathered using a

Figure 1. A conceptual diagram of the need for different types of literature reviews depending on the amount of publishedresearch papers and literature reviews. The bottom-right situation (many literature reviews but few research papers) is not just a theoreticalsituation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviewsthan research studies [33].doi:10.1371/journal.pcbi.1003149.g001

PLOS Computational Biology | www.ploscompbiol.org 2 July 2013 | Volume 9 | Issue 7 | e1003149

predefined protocol to reduce bias [13,14].

When systematic reviews analyse quanti-

tative results in a quantitative way, they

become meta-analyses. The choice be-

tween different review types will have to be

made on a case-by-case basis, depending

not just on the nature of the material

found and the preferences of the target

journal(s), but also on the time available to

write the review and the number of

coauthors [15].

Rule 5: Keep the ReviewFocused, but Make It of BroadInterest

Whether your plan is to write a mini- or

a full review, it is good advice to keep it

focused [16,17]. Including material just for

the sake of it can easily lead to reviews that

are trying to do too many things at once.

The need to keep a review focused can be

problematic for interdisciplinary reviews,

where the aim is to bridge the gap between

fields [18]. If you are writing a review on,

for example, how epidemiological ap-

proaches are used in modelling the spread

of ideas, you may be inclined to include

material from both parent fields, epidemi-

ology and the study of cultural diffusion.

This may be necessary to some extent, but

in this case a focused review would only

deal in detail with those studies at the

interface between epidemiology and the

spread of ideas.

While focus is an important feature of a

successful review, this requirement has to

be balanced with the need to make the

review relevant to a broad audience. This

square may be circled by discussing the

wider implications of the reviewed topic

for other disciplines.

Rule 6: Be Critical andConsistent

Reviewing the literature is not stamp

collecting. A good review does not just

summarize the literature, but discusses it

critically, identifies methodological prob-

lems, and points out research gaps [19].

After having read a review of the litera-

ture, a reader should have a rough idea of:

(i) the major achievements in the

reviewed field,

(ii) the main areas of debate, and

(iii) the outstanding research questions.

It is challenging to achieve a successful

review on all these fronts. A solution can

be to involve a set of complementary

coauthors: some people are excellent at

mapping what has been achieved, some

others are very good at identifying dark

clouds on the horizon, and some have

instead a knack at predicting where

solutions are going to come from. If your

journal club has exactly this sort of team,

then you should definitely write a review

of the literature! In addition to critical

thinking, a literature review needs consis-

tency, for example in the choice of passive

vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review

has a number of telling features: it is worth

the reader’s time, timely, systematic, well

written, focused, and critical. It also needs

a good structure. With reviews, the usual

subdivision of research papers into intro-

duction, methods, results, and discussion

does not work or is rarely used. However,

a general introduction of the context and,

toward the end, a recapitulation of the

main points covered and take-home mes-

sages make sense also in the case of

reviews. For systematic reviews, there is a

trend towards including information about

how the literature was searched (database,

keywords, time limits) [20].

How can you organize the flow of the

main body of the review so that the reader

will be drawn into and guided through it?

It is generally helpful to draw a conceptual

scheme of the review, e.g., with mind-

mapping techniques. Such diagrams can

help recognize a logical way to order and

link the various sections of a review [21].

This is the case not just at the writing

stage, but also for readers if the diagram is

included in the review as a figure. A

careful selection of diagrams and figures

relevant to the reviewed topic can be very

helpful to structure the text too [22].

Rule 8: Make Use of Feedback

Reviews of the literature are normally

peer-reviewed in the same way as research

papers, and rightly so [23]. As a rule,

incorporating feedback from reviewers

greatly helps improve a review draft.

Having read the review with a fresh mind,

reviewers may spot inaccuracies, inconsis-

tencies, and ambiguities that had not been

noticed by the writers due to rereading the

typescript too many times. It is however

advisable to reread the draft one more

time before submission, as a last-minute

correction of typos, leaps, and muddled

sentences may enable the reviewers to

focus on providing advice on the content

rather than the form.

Feedback is vital to writing a good

review, and should be sought from a

variety of colleagues, so as to obtain a

diversity of views on the draft. This may

lead in some cases to conflicting views on

the merits of the paper, and on how to

improve it, but such a situation is better

than the absence of feedback. A diversity

of feedback perspectives on a literature

review can help identify where the con-

sensus view stands in the landscape of the

current scientific understanding of an issue

[24].

Rule 9: Include Your OwnRelevant Research, but BeObjective

In many cases, reviewers of the litera-

ture will have published studies relevant to

the review they are writing. This could

create a conflict of interest: how can

reviewers report objectively on their own

work [25]? Some scientists may be overly

enthusiastic about what they have pub-

lished, and thus risk giving too much

importance to their own findings in the

review. However, bias could also occur in

the other direction: some scientists may be

unduly dismissive of their own achieve-

ments, so that they will tend to downplay

their contribution (if any) to a field when

reviewing it.

In general, a review of the literature

should neither be a public relations

brochure nor an exercise in competitive

self-denial. If a reviewer is up to the job of

producing a well-organized and methodi-

cal review, which flows well and provides a

service to the readership, then it should be

possible to be objective in reviewing one’s

own relevant findings. In reviews written

by multiple authors, this may be achieved

by assigning the review of the results of a

coauthor to different coauthors.

Rule 10: Be Up-to-Date, but DoNot Forget Older Studies

Given the progressive acceleration in

the publication of scientific papers, today’s

reviews of the literature need awareness

not just of the overall direction and

achievements of a field of inquiry, but

also of the latest studies, so as not to

become out-of-date before they have been

published. Ideally, a literature review

should not identify as a major research

gap an issue that has just been addressed

in a series of papers in press (the same

applies, of course, to older, overlooked

studies (‘‘sleeping beauties’’ [26])). This

implies that literature reviewers would do

well to keep an eye on electronic lists of

papers in press, given that it can take

months before these appear in scientific

databases. Some reviews declare that they

PLOS Computational Biology | www.ploscompbiol.org 3 July 2013 | Volume 9 | Issue 7 | e1003149

have scanned the literature up to a certain

point in time, but given that peer review

can be a rather lengthy process, a full

search for newly appeared literature at the

revision stage may be worthwhile. Assess-

ing the contribution of papers that have

just appeared is particularly challenging,

because there is little perspective with

which to gauge their significance and

impact on further research and society.

Inevitably, new papers on the reviewed

topic (including independently written

literature reviews) will appear from all

quarters after the review has been pub-

lished, so that there may soon be the need

for an updated review. But this is the

nature of science [27–32]. I wish every-

body good luck with writing a review of

the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-

Schmutz, T. Doring, D. Fontaneto, M. Garbe-

lotto, O. Holdenrieder, M. Jeger, D. Lonsdale,

A. MacLeod, P. Mills, M. Moslonka-Lefebvre,

G. Stancanelli, P. Weisberg, and X. Xu for

insights and discussions, and to P. Bourne, T.

Matoni, and D. Smith for helpful comments on

a previous draft.

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and productivity in scientific synthesis. Bioscience

61: 900–910. doi:10.1525/bio.2011.61.11.9.

5. Ketcham CM, Crawford JM (2007) The impact

of review articles. Lab Invest 87: 1174–1185.

doi:10.1038/labinvest.3700688.

6. Boote DN, Beile P (2005) Scholars before

researchers: on the centrality of the dissertation

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J, Rudd MA (2011) Methods for collaboratively

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9936-1.

PLOS Computational Biology | www.ploscompbiol.org 4 July 2013 | Volume 9 | Issue 7 | e1003149

Editorial

Ten Simple Rules for Writing Research PapersWeixiong Zhang*

Department of Computer Science and Engineering, Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America

The importance of writing well can

never be overstated for a successful

professional career, and the ability to write

solid papers is an essential trait of a

productive researcher. Writing and pub-

lishing a paper has its own life cycle;

properly following a course of action and

avoiding missteps can be vital to the

overall success not only of a paper but of

the underlying research as well. Here, we

offer ten simple rules for writing and

publishing research papers.

As a caveat, this essay is not about the

mechanics of composing a paper, much of

which has been covered elsewhere, e.g.,

[1,2]. Rather, it is about the principles and

attitude that can help guide the process of

writing in particular and research in

general. In this regard, some of the

discussion will complement, extend, and

refine some advice given in early articles of

this Ten Simple Rules series of PLOS

Computational Biology [3–8].

Rule 1: Make It a Driving Force

Never separate writing a paper from the

underlying research. After all, writing and

research are integral parts of the overall

enterprise. Therefore, design a project

with an ultimate paper firmly in mind.

Include an outline of the paper in the

initial project design documents to help

form the research objectives, determine

the logical flow of the experiments, and

organize the materials and data to be used.

Furthermore, use writing as a tool to

reassess the overall project, reevaluate the

logic of the experiments, and examine the

validity of the results during the research.

As a result, the overall research may need

to be adjusted, the project design may be

revised, new methods may be devised, and

new data may be collected. The process of

research and writing may be repeated if

necessary.

Rule 2: Less Is More

It is often the case that more than one

hypothesis or objective may be tackled in

one project. It is also not uncommon that

the data and results gathered for one

objective can serve additional purposes. A

decision on having one or more papers

needs to be made, and the decision will be

affected by various factors. Regardless of

the validity of these factors, the overriding

consideration must be the potential impact

that the paper may have on the research

subject and field. Therefore, the signifi-

cance, completeness, and coherence of the

results presented as a whole should be the

principal guide for selecting the story to tell,

the hypothesis to focus upon, and materials

to include in the paper, as well as the

yardstick for measuring the quality of the

paper. By this metric, less is more, i.e., fewer

but more significant papers serve both the

research community and one’s career

better than more papers of less significance.

Rule 3: Pick the Right Audience

Deciding on an angle of the story to

focus upon is the next hurdle to jump at

the initial stage of the writing. The results

from a computational study of a biological

problem can often be presented to biolo-

gists, computational scientists, or both;

deciding what story to tell and from what

angle to pitch the main idea is important.

This issue translates to choosing a target

audience, as well as an appropriate jour-

nal, to cast the main messages to. This is

critical for determining the organization of

the paper and the level of detail of the

story, so as to write the paper with the

audience in mind. Indeed, writing a paper

for biologists in general is different from

writing for specialists in computational

biology.

Rule 4: Be Logical

The foundation of ‘‘lively’’ writing for

smooth reading is a sound and clear logic

underlying the story of the paper. Although

experiments may be carried out indepen-

dently, the result from one experiment may

form premises and/or provide supporting

data for the next experiment. The exper-

iments and results, therefore, must be

presented in a logical order. In order to

make the writing an easy process to follow,

this logical flow should be determined

before any other writing strategy or tactic

is exercised. This logical order can also help

you avoid discussing the same issue or

presenting the same argument in multiple

places in the paper, which may dilute the

readers’ attention.

An effective tactic to help develop a

sound logical flow is to imaginatively

create a set of figures and tables, which

will ultimately be developed from experi-

mental results, and order them in a logical

way based on the information flow

through the experiments. In other words,

the figures and tables alone can tell the

story without consulting additional mate-

rial. If all or some of these figures and

tables are included in the final manuscript,

make every effort to make them self-

contained (see Rule 5 below), a favorable

feature for the paper to have. In addition,

these figures and tables, as well as the

threading logical flow, may be used to

direct or organize research activities,

reinforcing Rule 1.

Rule 5: Be Thorough and MakeIt Complete

Completeness is a cornerstone for a

research paper, following Rule 2. This

cornerstone needs to be set in both content

and presentation. First, important and

relevant aspects of a hypothesis pursued

in the research should be discussed with

detailed supporting data. If the page limit

is an issue, focus on one or two main

aspects with sufficient details in the main

text and leave the rest to online supporting

Citation: Zhang W (2014) Ten Simple Rules for Writing Research Papers. PLoS Comput Biol 10(1): e1003453.doi:10.1371/journal.pcbi.1003453

Editor: Philip E. Bourne, University of California San Diego, United States of America

Published January 30, 2014

Copyright: � 2014 Weixiong Zhang. This is an open-access article distributed under the terms of theCreative Commons Attribution License., which permits unrestricted use, distribution, and reproduction in anymedium, provided the original author and source are credited.

Funding: The author received no specific funding for this article.

Competing Interests: The author has declared that no competing interests exist.

* E-mail: weixiong.zhang@wustl.edu

PLOS Computational Biology | www.ploscompbiol.org 1 January 2014 | Volume 10 | Issue 1 | e1003453

materials. As a reminder, be sure to keep

the details of all experiments (e.g., param-

eters of the experiments and versions of

software) for revision, post-publication

correspondence, or importantly, reproduc-

ibility of the results. Second, don’t simply

state what results are presented in figures

and tables, which makes the writing

repetitive because they are self-contained

(see below), but rather, interpret them with

insights to the underlying story to be told

(typically in the results section) and discuss

their implication (typically in the discus-

sion section).

Third, make the whole paper self-

contained. Introduce an adequate amount

of background and introductory material

for the right audience (following Rule 3). A

statistical test, e.g., hypergeometric tests

for enrichment of a subset of objects, may

be obvious to statisticians or computation-

al biologists but may be foreign to others,

so providing a sufficient amount of

background is the key for delivery of the

material. When an uncommon term is

used, give a definition besides a reference

to it. Fourth, try to avoid ‘‘making your

readers do the arithmetic’’ [9], i.e., be

clear enough so that the readers don’t

have to make any inference from the

presented data. If such results need to be

discussed, make them explicit even though

they may be readily derived from other

data. Fifth, figures and tables are essential

components of a paper, each of which

must be included for a good reason; make

each of them self-contained with all

required information clearly specified in

the legend to guide interpretation of the

data presented.

Rule 6: Be Concise

This is a caveat to Rule 5 and is singled

out to emphasize its importance. Being

thorough is not a license to writing that is

unnecessarily descriptive, repetitive, or

lengthy. Rather, on the contrary, ‘‘sim-

plicity is the ultimate sophistication’’ [10].

Overly elaborate writing is distracting and

boring and places a burden on the readers.

In contrast, the delivery of a message is

more rigorous if the writing is precise and

concise. One excellent example is Watson

and Crick’s Nobel-Prize-winning paper on

the DNA double helix structure [11] —it

is only two pages long!

Rule 7: Be Artistic

A complete draft of a paper requires a

lot of work, so it pays to go the extra mile

to polish it to facilitate enjoyable reading.

A paper presented as a piece of art will

give referees a positive initial impression of

your passion toward the research and the

quality of the work, which will work in

your favor in the reviewing process.

Therefore, concentrate on spelling, gram-

mar, usage, and a ‘‘lively’’ writing style

that avoids successions of simple, boring,

declarative sentences. Have an authorita-

tive dictionary with a thesaurus and a style

manual, e.g., [1], handy and use them

relentlessly. Also pay attention to small

details in presentation, such as paragraph

indentation, page margins, and fonts. If

you are not a native speaker of the lan-

guage the paper is written in, make sure to

have a native speaker go over the final

draft to ensure correctness and accuracy of

the language used.

Rule 8: Be Your Own Judge

A complete manuscript typically re-

quires many rounds of revision. Taking a

correct attitude during revision is critical

to the resolution of most problems in the

writing. Be objective and honest about

your work and do not exaggerate or

belittle the significance of the results and

the elegance of the methods developed.

After working long and hard, you are an

expert on the problem you studied, and

you are the best referee of your own work, after all.

Therefore, inspect the research and the

paper in the context of the state of the art.

When revising a draft, purge yourself

out of the picture and leave your passion

for your work aside. To be concrete, put

yourself completely in the shoes of a

referee and scrutinize all the pieces—the

significance of the work, the logic of the

story, the correctness of the results and

conclusions, the organization of the paper,

and the presentation of the materials. In

practice, you may put a draft aside for a

day or two—try to forget about it

completely—and then come back to it

fresh, consider it as if it were someone

else’s writing, and read it through while

trying to poke holes in the story and

writing. In this process, extract the mean-

ing literally from the language as written

and do not try to use your own view to

interpret or extrapolate from what was

written. Don’t be afraid to throw away

pieces of your writing and start over from

scratch if they do not pass this ‘‘not-

yourself’’ test. This can be painful, but the

final manuscript will be more logically

sound and better organized.

Rule 9: Test the Water in YourOwn Backyard

It is wise to anticipate the possible

questions and critiques the referees may

raise and preemptively address their con-

cerns before submission. To do so, collect

feedback and critiques from others, e.g.,

colleagues and collaborators. Discuss your

work with them and get their opinions,

suggestions, and comments. A talk at a lab

meeting or a departmental seminar will

also help rectify potential issues that need

to be addressed. If you are a graduate

student, running the paper and results

through the thesis committee may be

effective to iron out possible problems.

Rule 10: Build a Virtual Team ofCollaborators

When a submission is rejected or poorly

reviewed, don’t be offended and don’t take

it personally. Be aware that the referees

spent their time on the paper, which they

might have otherwise devoted to their own

research, so they are doing you a favor and

helping you shape the paper to be more

accessible to the targeted audience. There-

fore, consider the referees as your collab-

orators and treat the reviews with respect.

This attitude can improve the quality of

your paper and research.

Read and examine the reviews objec-

tively—the principles set in Rule 8 apply

here as well. Often a criticism was raised

because one of the aspects of a hypothesis

was not adequately studied, or an impor-

tant result from previous research was not

mentioned or not consistent with yours. If

a critique is about the robustness of a

method used or the validity of a result,

often the research needs to be redone or

more data need to be collected. If you

believe the referee has misunderstood a

particular point, check the writing. It is

often the case that improper wording or

presentation misled the referee. If that’s

the case, revise the writing thoroughly.

Don’t argue without supporting data.

Don’t submit the paper elsewhere without

additional work. This can only temporally

mitigate the issue, you will not be happy

with the paper in the long run, and this

may hurt your reputation.

Finally, keep in mind that writing is

personal, and it takes a lot of practice to

find one’s style. What works and what

does not work vary from person to person.

Undoubtedly, dedicated practice will help

produce stronger papers with long-lasting

impact.

Acknowledgments

Thanks to Sharlee Climer, Richard Korf, and

Kevin Zhang for critical reading of the

manuscript.

PLOS Computational Biology | www.ploscompbiol.org 2 January 2014 | Volume 10 | Issue 1 | e1003453

References

1. Strunk W Jr, White EB (1999) The Elements ofStyle. 4th edition. New York: Longman.

2. Zinsser W (2006) On Writing Well: The ClassicGuide to Writing Nonfiction. 30th anniversary

edition. New York: Harper Perennial.3. Bourne PE (2005) Ten simple rules for getting

published. PLOS Comput Biol 1: e57.

doi:10.1371/journal.pcbi.00100574. Erren TC, Cullen P (2007) Ten simple rules for

doing your best research, according to Hamming.PLOS Comput Biol 3: e213. doi:10.1371/jour-

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5. Bourne PE (2007) Ten simple rules for makinggood oral presentations. PLOS Comput Biol 3:

e77. doi:10.1371/journal.pcbi.0030077

6. Erren TC, Bourne PE (2007) Ten simple rules fora good poster presentation. PLOS Comput Biol 3:

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for reviewers. PLOS Comput Biol 2: e110.doi:10.1371/journal.pcbi.0020110

8. Logan DW, Sandal M, Gardner PP, Manske M,

Bateman A (2010) Ten simple rules for editingWikipedia. PLOS Comput Biol 6: e1000941.

doi:10.1371/journal.pcbi.10009419. Johnson DS (2002) A theoretician’s guide to the

experimental analysis of algorithms. In Gold-

wasser MH, Johnson DS, McGeoch CC, editors.Data Structures, Near Neighbor Searches, and

Methodology: Fifth and Sixth DIMACS Imple-

mentation Challenges. Providence: American

Mathematical Society. pp.215–250.

10. Wikiquote page on Leonardo Da Vinci. Avail-

able: http://en.wikiquote.org/wiki/Leonardo_

da_Vinci#Quotes_about_Leonardo. Accessed

13 December 2013.

11. Watson JD, Crick FHC (1953) Molecular structure

of nucleic acids. Nature 171: 737–738. Available:

http://www.nature.com/nature/dna50/watsoncrick.pdf.

Accessed 31 December 2013.

PLOS Computational Biology | www.ploscompbiol.org 3 January 2014 | Volume 10 | Issue 1 | e1003453

Editorial

Ten Simple Rules for Better FiguresNicolas P. Rougier1,2,3*, Michael Droettboom4, Philip E. Bourne5

1 INRIA Bordeaux Sud-Ouest, Talence, France, 2 LaBRI, UMR 5800 CNRS, Talence, France, 3 Institute of Neurodegenerative Diseases, UMR 5293 CNRS, Bordeaux, France,

4 Space Telescope Science Institute, Baltimore, Maryland, United States of America, 5 Office of the Director, The National Institutes of Health, Bethesda, Maryland, United

States of America

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 visualiza-

tion 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 impor-

tant 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 audi-

ence. 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 ap-

proximated, 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 impor-

tant 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 theSupport 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-pro-

jected 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 NotOptional

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

Citation: Rougier NP, Droettboom M, Bourne PE (2014) Ten Simple Rules for Better Figures. PLoS ComputBiol 10(9): e1003833. doi:10.1371/journal.pcbi.1003833

Published September 11, 2014

This 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 underthe 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.fr

Funding: The authors received no specific funding for this article.

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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 theDefaults

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. How-

ever, 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 wasmade with matplotlib using approximated data. The data is made of four series (men deaths/cases, women deaths/cases) that could have beendisplayed using classical double column (deaths/cases) bar plots. However, the layout used here is better for the intended audience. It exploits thefact 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 thereading direction (English [left-to-right] for NYT) in order to ease comparison between men and women while the central labels give an immediateaccess 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 notprecise. The chosen layout makes it actually difficult to estimate the number of kidney cancer deaths because of its bottom position and the locationof the labelled ticks at the top. While this is acceptable for a general-audience publication, it would not be acceptable in a scientific publication ifactual numerical values were not given elsewhere in the article.doi:10.1371/journal.pcbi.1003833.g001

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unless 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 vary-

ing 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 theReader

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, repre-

senting 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 circum-

stances. If your software automatically re-

scales values, you might obtain an objec-

tive representation of the data (because

title, labels, and ticks indicate clearly

what is actually displayed) that is none-

theless visually misleading (see bar plot in

Figure 6); you have inadvertently misled

your readers into visually believing some-

thing 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 quanti-

ties. 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.

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

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Rule 8: Avoid ‘‘Chartjunk’’

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 back-

grounds, 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

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=4z(x{y))(1{x), x§0,

dy

dt~(1=4z(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. Thered color has been used consistently to indicate both initial conditions (red dots in the zoomed panel) and trajectories (red lines). Line transparency has beenincreased in order to highlight regions where trajectories overlap (high color density). The right figure has been prepared for an oral presentation. Many detailshave 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 duringthe 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. Do not trust the defaults. The left panel shows the sine and cosine functions as rendered by matplotlib using default settings. Whilethis 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

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figure can be justified in another. For

example, the use of a background color in

a regular plot is generally a bad idea

because it does not bring useful informa-

tion. However, in the right part of

Figure 7, we use a gray background box

to indicate the range [21,+1] as described

in the caption. If you’re in doubt, do not

hesitate to consult the excellent blog of

Kaiser Fung [8], which explains quite

clearly the concept of chartjunk through

the study of many examples.

Rule 9: Message Trumps Beauty

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,

you may need to design a brand-new

figure, because there is no standard way of

describing your research. In such a case,

browsing the scientific literature is a good

starting point. If some article displays a

stunning figure to introduce results similar

to yours, you might want to try to adapt

the figure for your own needs (note that we

did not say copy; be careful with image

copyright). If you turn to the web, you

have to be very careful, because the

frontiers between data visualization, info-

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

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, weused 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 (redcircles), 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 isactually very frequent in the literature because the command (or interface) used to produce circles or scatter plots (with varying point sizes) offers touse 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 thefigure, 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 totallydifferent. 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 aresignificant differences. Even if we had used labels to indicate the actual range, the effect would persist because the bars are the most salientinformation on these figures.doi:10.1371/journal.pcbi.1003833.g006

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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 visuallycompare them (exact signal values are supposed to be given elsewhere). The left figure demonstrates what is certainly one of the worst possibledesigns. All the curves cover each other and the different colors (that have been badly and automatically chosen by the software) do not help todistinguish them. The legend box overlaps part of the graphic, making it impossible to check if there is any interesting information in this area. Thereare far too many ticks: x labels overlap each other, making them unreadable, and the three-digit precision does not seem to carry any significantinformation. Finally, the grid does not help because (among other criticisms) it is not aligned with the signal, which can be considered discrete giventhe small number of sample points. The right figure adopts a radically different layout while using the same area on the sheet of paper. Series havebeen split into seven plots, each of them showing one series, while other series are drawn very lightly behind the main one. Series labels have beenput 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 indicatesthese 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

Figure 8. Message trumps beauty. This figure is an extreme case where the message is particularly clear even if the aesthetic of the figure isquestionable. The uncanny valley is a well-known hypothesis in the field of robotics that correlates our comfort level with the human-likeness of arobot. To express this hypothetical nature, hypothetical data were used (y~x2{5e{5(x{2)2

) and the figure was given a sketched look (xkcd filter onmatplotlib) associated with a cartoonish font that enhances the overall effect. Tick labels were also removed since only the overall shape of the curvematters. 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 detailsthat are important [10].doi:10.1371/journal.pcbi.1003833.g008

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graphics, 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 beau-

tiful, most of them do not fit the scientific

framework. Remember, in science, mes-

sage 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 visual-

ization 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

3-D support, which produces publication-

quality figures in a variety of hardcopy

formats and interactive environments

across platforms. It comes with a huge

gallery of examples that cover virtually all

scientific domains (http://matplotlib.org/

gallery.html).

R is a language and environment for

statistical computing and graphics. R

provides a wide variety of statistical (linear

and nonlinear modeling, classical statisti-

cal tests, time-series analysis, classification,

clustering, etc.) and graphical techniques,

and is highly extensible.

Inkscape is a professional vector

graphics editor. It allows you to design

complex figures and can be used, for

example, to improve a script-generated

figure or to read a PDF file in order to

extract figures and transform them any

way you like.

TikZ and PGF are TeX packages for

creating graphics programmatically. TikZ

is built on top of PGF and allows you to

create sophisticated graphics in a rather

intuitive and easy manner, as shown by

the Tikz gallery (http://www.texample.

net/tikz/examples/all/).

GIMP is the GNU Image Manipula-

tion Program. It is an application for such

tasks as photo retouching, image compo-

sition, and image authoring. If you need

to quickly retouch an image or add some

legends or labels, GIMP is the perfect

tool.

ImageMagick is a software suite to

create, edit, compose, or convert bitmap

images from the command line. It can be

used to quickly convert an image into

another format, and the huge script gallery

(http://www.fmwconcepts.com/imagema

gick/index.php) by Fred Weinhaus will

provide virtually any effect you might

want to achieve.

D3.js (or just D3 for Data-Driven

Documents) is a JavaScript library that

offers an easy way to create and control

interactive data-based graphical forms

which run in web browsers, as shown in

the gallery at http://github.com/

mbostock/d3/wiki/Gallery.

Cytoscape is a software platform for

visualizing complex networks and integrat-

ing these with any type of attribute data. If

your data or results are very complex,

cytoscape may help you alleviate this

complexity.

Circos was originally designed for

visualizing genomic data but can create

figures from data in any field. Circos is

useful if you have data that describes

relationships or multilayered annotations

of one or more scales.

Notes

All the figures for this article were

produced using matplotlib, and figure

scripts are available from https://github.

com/rougier/ten-rules.

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