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:[email protected]
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: [email protected]
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: [email protected]
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.
References
1. Rapple C (2011) The role of the critical review
article in alleviating information overload. Annual
Reviews White Paper. Available: http://www.
annualreviews.org/userimages/ContentEditor/
1300384004941/Annual_Reviews_WhitePaper_
Web_2011.pdf. Accessed May 2013.
2. Pautasso M (2010) Worsening file-drawer prob-
lem in the abstracts of natural, medical and social
science databases. Scientometrics 85: 193–202.
doi:10.1007/s11192-010-0233-5.
3. Erren TC, Cullen P, Erren M (2009) How to surf
today’s information tsunami: on the craft of
effective reading. Med Hypotheses 73: 278–279.
doi:10.1016/j.mehy.2009.05.002.
4. Hampton SE, Parker JN (2011) Collaboration
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
literature review in research preparation. Educ
R e s 3 4 : 3 – 1 5 . d o i : 1 0 . 3 1 0 2 /
0013189X034006003.
7. Budgen D, Brereton P (2006) Performing system-
atic literature reviews in software engineering.
Proc 28th Int Conf Software Engineering, ACM
New York, NY, USA, pp. 1051–1052.
doi:10.1145/1134285.1134500.
8. Maier HR (2013) What constitutes a good
literature review and why does its quality matter?
Environ Model Softw 43: 3–4. doi:10.1016/
j.envsoft.2013.02.004.
9. Sutherland WJ, Fleishman E, Mascia MB, Pretty
J, Rudd MA (2011) Methods for collaboratively
identifying research priorities and emerging issues
in science and policy. Methods Ecol Evol 2: 238–
247. doi:10.1111/j.2041-210X.2010.00083.x.
10. Maggio LA, Tannery NH, Kanter SL (2011)
Reproducibility of literature search reporting in
medical education reviews. Acad Med 86: 1049–
1054. doi:10.1097/ACM.0b013e31822221e7.
11. Torraco RJ (2005) Writing integrative literature
reviews: guidelines and examples. Human ResDevelop Rev 4: 356–367. doi:10.1177/
1534484305278283.
12. Khoo CSG, Na JC, Jaidka K (2011) Analysis ofthe macro-level discourse structure of literature
reviews. Online Info Rev 35: 255–271.doi:10.1108/14684521111128032.
13. Rosenfeld RM (1996) How to systematicallyreview the medical literature. Otolaryngol Head
Neck Surg 115: 53–63. doi:10.1016/S0194-
5998(96)70137-7.14. Cook DA, West CP (2012) Conducting systematic
reviews in medical education: a stepwise ap-proach. Med Educ 46: 943–952. doi:10.1111/
j.1365-2923.2012.04328.x.
15. Dijkers M, The Task Force on SystematicReviews and Guidelines (2009) The value of
‘‘traditional’’ reviews in the era of systematicreviewing. Am J Phys Med Rehabil 88: 423–430.
doi:10.1097/PHM.0b013e31819c59c6.
16. Eco U (1977) Come si fa una tesi di laurea. Milan:Bompiani.
17. Hart C (1998) Doing a literature review: releasingthe social science research imagination. London:
SAGE.18. Wagner CS, Roessner JD, Bobb K, Klein JT,
Boyack KW, et al. (2011) Approaches to under-
standing and measuring interdisciplinary scientificresearch (IDR): a review of the literature.
J I n f o r m e t r 5 : 1 4 – 2 6 . d o i : 1 0 . 1 0 1 6 /j.joi.2010.06.004.
19. Carnwell R, Daly W (2001) Strategies for the
construction of a critical review of the literature.Nurse Educ Pract 1: 57–63. doi:10.1054/
nepr.2001.0008.20. Roberts PD, Stewart GB, Pullin AS (2006) Are
review articles a reliable source of evidence tosupport conservation and environmental man-
agement? A comparison with medicine. Biol
Conserv 132: 409–423. doi:10.1016/j.bio-con.2006.04.034.
21. Ridley D (2008) The literature review: a step-by-step guide for students. London: SAGE.
22. Kelleher C, Wagener T (2011) Ten guidelines for
effective data visualization in scientific publica-
tions. Environ Model Softw 26: 822–827.
doi:10.1016/j.envsoft.2010.12.006.
23. Oxman AD, Guyatt GH (1988) Guidelines for
reading literature reviews. CMAJ 138: 697–703.
24. May RM (2011) Science as organized scepticism.
Philos Trans A Math Phys Eng Sci 369: 4685–
4689. doi:10.1098/rsta.2011.0177.
25. Logan DW, Sandal M, Gardner PP, Manske M,
Bateman A (2010) Ten simple rules for editing
Wikipedia. PLoS Comput Biol 6: e1000941.
doi:10.1371/journal.pcbi.1000941.
26. van Raan AFJ (2004) Sleeping beauties in science.
Scientometrics 59: 467–472. doi:10.1023/
B:SCIE.0000018543.82441.f1.
27. Rosenberg D (2003) Early modern information
overload. J Hist Ideas 64: 1–9. doi:10.1353/
jhi.2003.0017.
28. Bastian H, Glasziou P, Chalmers I (2010)
Seventy-five trials and eleven systematic reviews
a day: how will we ever keep up? PLoS Med 7:
e1000326. doi:10.1371/journal.pmed.1000326.
29. Bertamini M, Munafo MR (2012) Bite-size
science and its undesired side effects. Perspect
P s y c h o l S c i 7 : 6 7 – 7 1 . d o i : 1 0 . 1 1 7 7 /
1745691611429353.
30. Pautasso M (2012) Publication growth in biolog-
ical sub-fields: patterns, predictability and sus-
tainability. Sustainability 4: 3234–3247.
doi:10.3390/su4123234.
31. Michels C, Schmoch U (2013) Impact of biblio-
metric studies on the publication behaviour of
authors. Scientometrics. doi:10.1007/s11192-
013-1015-7. In press.
32. Tsafnat G, Dunn A, Glasziou P, Coiera E (2013)
The automation of systematic reviews. BMJ 346:
f139. doi:10.1136/bmj.f139.
33. Pautasso M, Doring TF, Garbelotto M, Pellis L,
Jeger MJ (2012) Impacts of climate change on
plant diseases - opinions and trends. Eur J Plant
Pathol 133: 295–313. doi:10.1007/s10658-012-
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: [email protected]
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-
nal.pcbi.0030213
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:
e102. doi: 10.1371/journal.pcbi.00301027. Bourne PE, Korngreen A (2006) Ten simple rules
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: [email protected]
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.
References
1. Tufte EG (1983) The Visual Display of Quanti-
tative Information. Cheshire, Connecticut:
Graphics Press.
2. Doumont JL (2009) Trees, maps, and theorems.
Brussels: Principiae.
3. Kosara R, Mackinlay J (2013) Storytelling: The
next step for visualization. IEEE Comput 46: 44–
50.
4. Rougier NP (2012) Scientific visualization and
matplotlib tutorial. Euroscipy 2012 & 2013.
Available: http://www.loria.fr/,rougier/
teaching/matplotlib/matplotlib.html. Accessed
12 August 2014.
5. Borland D, Taylor RM (2007) Rainbow color
map (still) considered harmful. IEEE Comput
Graph Appl 27: 14–17.
6. Okabe M, Ito K (2008). Color universal design (cud) -
how to make figures and presentations that are
friendly to colorblind people. Available: http://jfly.
iam.u-tokyo.ac.jp/color/. Accessed 12 August 2014.
7. Few S (2011) The chartjunk debate, a close
examination of recent findings. Visual Business
Intelligence Newsletter. Available: http://www.
perceptualedge.com/articles/visual_business_
intelligence/the_chartjunk_debate.pdf. Accessed
12 August 2014.
8. Fung K (2005). Junk charts: Recycling chartjunkas junk art. Available: http://junkcharts.typepad.
com. Accessed 12 August 2014.
9. Borkin MA, Vo AA, Bylinskii Z, Isola P,Sunkavalli S, et al. (2013) What makes a
visualization memorable? IEEE Trans Vis Com-put Graph 19: 2306–2315.
10. Schumann J, Strothotte T, Raab A, Laser S (1996)
Assessing the effect of non-photorealistic renderedimages in cad. In: Proceedings of the SIGCHI
Conference on Human Factors in ComputingSystems; 13–18 April 1996; New York, New York,
United States. CHI 96. New York: Association for
Computing Machinery. pp.35–41.
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