Welcome to week five of Writing in the Sciences.
So this week we're actually going to change modes and jump right into talking
about a format of an original manuscript, But first, I want to give a little recap
of what we talked about last week. So we talked about streamlining your
writing process. And I told you that like to break the
writing process actually into three discrete steps.
So there's what I call the prewriting step.
This is when you're gathering and organizing your information coming up with
the themes of your manuscript. I like to keep that separate from the next
step which is writing the first draft. This is when you're actually sitting down
at the computer composing prose. And then the third step is actually
revision, going back and editing that prose.
And, and the reason why I like to think about that as three distinct steps is that
I think that often times there's a temptation for scientists to just want to
jump right into that writing the draft phase.
You really just want to get into it and bite the bullet and say, I'm going to sit
down today and whip off that manuscript. And the problem with that is that it,
actually, surprisingly turns out to be a really inefficient way to write.
If you spend the time upfront, As I told you last week, getting
organized, gathering the information so that it is at your finger tips,
Then when you sit down to write, it would be much, much more efficient.
You will be able to write much more quickly and it will be a lot more fun to
write. I have also encouraged you to spend more
time in the revision step. So, when you are writing that first draft,
don't feel that you need to edit yourself as you go along.
Save that step for afterwards, you can go back and make it sound better afterwards.
So I think this, the mistake that scientists make is they tend to spend too
little time in prewriting and revision. And that actually it might seem like
you're going to end up taking less time overall, but in fact, I think you're, you
end up taking more time overall writing the whole manuscript.
You can actually, Compress all of the time for writing the
manuscript by each, by doing each of those steps well.
So that's one of the points that I made last week.
The other thing I wanted to add, the other tip that I wanted add that I forgot to
mention last week, Was if you find, writing to be daunting.
If, if you're, you know, intimidated when you've got to go in and write your
manuscript, Which was, which is true for a lot of
people. One of the things that I do to kinda help
ease that, anxiety about writing, is to set yourself very reasonable goals for
writing for each day. Kind of break your manuscript or your
paper up into sections, And what you need to do is just work on,
you know, a reasonable, set a modest goal for yourself for each of those sections.
So for example, if I've got a feature story I'm working on and, I've got to go
and write today, maybe I just say, well, today I want to finish these two sections.
I want to write 800 words and I set myself something that's a very reasonable goal
and I'm very confident that I can accomplish and that doesn't seem so
daunting. Imagine if you're driving into work and
you're saying, well today, I've got to write the whole manuscript.
Well, that's just, you know, it's, what, it's probably unrealistic, you're probably
not going to be able to whip out a whole manuscript in one day.
And, and two, it's just very daunting, Whereas, if you say, today I'm going to
write the introduction section or I'm going to write 300 words.
Set yourself some very reasonable modest goals,
Break it up into sections and that could help really make the whole process seem
easier and more fun. Alright.
So, we'll continue to talk about the writing process as we go through the
course, but now I want actually today focus on a format of an original
manuscript. And I'm going to talk about,
The original manuscript in the following order.
This may not be the order that you're used to hearing about, hearing about original
manuscripts, but this is the order in which I write an original manuscript and
I, this is what I'm going recommend, Or the order this is the order that I am
going to recommend for you. So I think it's best when you are going to
write an original manuscript to actually start with the tables and figures first.
And I don't just mean kind of putting together some rough data in a table.
I mean nailing your tables and figures. Your tables and figures are actually the
story of our paper, So you really need to get those down kind
of into final form, Complete them before you actually write
your paper. Each table and figure should tell a story
and that together they should progress to tell the entire story of the manuscript.
So I recommend getting those down getting those really well done, having them look
professional, kind of nailing down the story and tables and figures first.
If you do that first, then the next thing that kind of follows is then you write
your results section, right. Because the tables and the figures kind of
leads right into the results section. The results section is a summary of what's
in your tables and figures, so it makes sense to write the results section next.
Then that usually, the next thing would be the methods.
Now of course, you could write the methods at any time, because you've already done
the experiments, you know what you did, But I put that as the third thing.
I find the methods a little bit boring to write.
It's not hard to ,, but it's kind of boring,
You're just kind of rehashing what you did.
But you know, I put it as the third step after that.
And then the next thing I would do is write the introduction section.
And, again, you could technically write the introduction section before you've
written these other sections. But what inevitably happens is,
You know, you're, again, you're telling a kind of story with your, with your data
and your tables and figures. And the introduction, even though, it's
somewhat independent of what you've found, kind of knowing what that story is kind of
helps you to set up the introduction. So I'll write that fourth,
Then I'll write the discussion section, That's of course, the hardest to write
because it's involves the most writing, it's the most complex.
I'll do that next, cuz that's of course, comes after all of these other sections.
And then I leave the abstract to write for the very end.
Don't bother to try to write your abstract before you've written any of these other
sections, Because the abstract of course, you're
just kind of pulling a little bit out of each of these other sections.
So it's much easier to write after you've already done these other sections.
So this is the order I'm going to present things to you today.
I wanted to give you a couple of good references, if you want to do some further
reading about the format of an original manuscript and I've used both of these
references to help me, to have contributed to some of my slides, as you'll see later.
So there's a great series of articles published by Clinical Chemistry. And even
if you're not a chemist Pretty, the, they're pretty generally written and would
apply to most scientific disciplines. And he goes through and writes about, you
know, how to write an abstract, how to write an introduction and all of those
things and does a really nice job. So I'd recommend if you've got some time to go
and read that, They're pretty sure so it's not to hard to
go through those and you can pick out particular articles that would be most
helpful to you. It's a whole series.
If you're in the biomedical sciences, there's a really nice textbook by Mimi
Zeiger on the Essentials of Writing Biomedical Research Papers.
She goes through in great detail each of the sections of an original manuscript,
in, in much more detail than I'm going to go through in these lectures.
So if you're coming from biomedicine that's a really good reference, if you've
got the time to pick that one up. Again, I'll be, using some material from
both of those today, in this week's lectures.
So, in this first unit here, we are going to talk about tables and figures.
And again, tables and figures are really, in a way the most important part of your
manuscripts. And, the big mistake that scientists make
a lot of times is, they kind of do tables and figures very haphazardly.
We're just going to, I am just going to throw out my this data in table, throw
that data and the figure, and they don't think it's written very carefully.
So, you really want to think of those tables and figures very carefully, because
they actually sound to form the foundation of your story.
And editors, reviewers, and sometimes readers, may look first at the tables and
figures, maybe skim the abstract and then jump to the tables and figures.
That's what I do as a reviewer, so I got a paper to review, I'll skim the abstract
just to get a sense of what the paper's about and then I jump right to the tables
and figures. I want to see that data for myself before
I hear what the authors want to tell me about their data.
So I look at those tables and figures without having read the text.
So that means that the tables and figures need to be self-contained.
So all the information you need to understand them has to be right there in
the table. Acronyms have to be defined,
Experimental details need to be defined. So that you should be able to look at that
table and immediately or very easily understand what it is that the authors
were looking at and what it is that they found.
So those figure, tables and figures have to each tell a little bit of a, of a story
and it should be clear to the reader what story the table or figure is trying to
tell and it should also progress in the story from one to the next.
So I jump to those tables and figures and I wanna be able to figure that story out
without having to read the text so make sure you know the point of each table and
figure as you're creating it. It's kind of like writing the topic sentence of the
paragraph, you need to know what is it I'm trying to convey to the reader,
And stick to that point as much as possible.
Some people would say that the data, which is in the tables and figures, is actually
is the story of your, manuscript is your scholarship.
And some people call that manuscript that you write around the tables and figures
just the advertising for the scholarship for the data.
I, I picked out a quote here that's specifically about computational science
but actually applies across. Its been the computational part didn't go
put in the data part. So he says an article about computational
science in a scientific publication isn't the scholarship itself.
It's merely advertising of the scholarship.
The actual scholarship is the complete software development environment and the
complete set of instructions, which generated the figures, and again you can
insert in there, all, you know, the, the scholarship is actually a data that you've
collected, so in a way, you're writing is just an advertising for that data, so
they've gotta have that whole story in there.
So, a couple of tips on tables and figures.
So, first of all, you want to use the fewest figures and tables needed to tell
the story. And often, scientific journals may even
have limits on the number of tables and figures that you're allowed to submit.
You don't want to repeat the same data in both a table and a figure.
Choose one or the other. Choose to present the data as a table or
as a figure. You don't need to have it in both places.
And a lot of times one of the decisions you've got to make is what data do I show
in a table and what data do I show in a figure.
So, the distinction is that, you know, figures are really used because they have
some kind of visual impact. So if you've got data that really lends
itself to a nice visually appealing figure, think about putting it in a
figure. Figures are also used to show trends and
patterns, and you have to be able to, be able to visually, quickly pick up those
trends and patterns from the figure. If it's too complicated, then you can
better put it in a table. Figures tell a, a quick story.
So if there's a real main point you want to get across to your readers, that's
another thing to put in a figure. I really like figures also because they
tend to tell the whole story about the data.
So if you're putting like means and standard deviations in a table, that's
telling you something about the data, but it's not displaying like the range of the
data, the variation, all the data points. So, sometimes when you display all your
data, you're kind of, airing your dirty laundry.
You're really showing the reader exactly what's going on in your data.
So that's nice, cuz it's telling the reader the whole story.
And often times you go, will use figures to highlight the most important result of
their paper cuz again figures aren't visually appealing,
So that will tempt the reader to look at them. So if it is the result that you
really want to highlight to your reader you might put it in a figure.
Tables are used to give precise values, So in a figure, you will often not be able
to get the precise, you know, value down to the decimal place.
So if you think those precise values are important, you might be putting those in a
table. And of course, if you have many values and
many variables you need to display a table is better for, for putting a lot of
information. So we're going to talk a little bit about
how to format a table. So, a table's going to have a title and
those are usually pretty easy. You need to make sure that you identify
the specific topic or point of the table. Again, what's the story in the table?
A couple other things to keep in mind, you want to make sure that you repeat the same
key terms in the table title, the column headings of the table and in the text the
main text of the paper. And so we talked about it couple of weeks
ago, it's okay to repeat those key terms because you want it to be consistent so
that the reader's not confused. So make sure that those match exactly.
You want to also keep the table title as brief as possible but make sure you have
all the key information in there. So an example would be something like
descriptive characteristics of the two treatments groups, you might wanna specify
what the, the treatment group names are, I've just made up a generic title here
means plus or minus standard deviation or n percents, so you're telling them exactly
what data are presented, or what statistics are presented in that table.
Tables will also have footnotes, so, it can get kinda confusing, because what
symbols do you use to denote the footnotes?
Well, some journals will have one, two, three, four.
Other, other journals will have A, B, C, D.
Some journals will have a series like star, double star.
So you don't want to figure that out on your own.
Just when you're starting to build your tables and figures.
Try to, at that point, identify what journal it is that you're going to be
submitting to, Then go pick up that journal,
Pick up a published article from that journal and just look and see what, what
superscript symbols, what footnote symbols are used in that journal.
Each journal has its own set of guidelines and the best way to figure out those
guidelines is either to read the author's instructions or even easier, pull a paper
from that journal, and just copy what they've done because, every journal will
have its own preference. And you might as well format the table for
your particular journal that you're targeting.
You're going to use footnotes to do things like explain statistical significance.
If you put little stars to indicate statistically significant differences,
You've got to explain what groups those refer to,
What test was used, What, if the actual P values, if you want
to be more specific than less than [INAUDIBLE]. use the footnotes also to
explain experimental details or acronyms, abbreviations.
Again, the table has to stand on its own. So you can't just use acronyms in the
table that you've defined in the text. You have to redefine those within the
table. As we talked about before, try not to
flood your paper with acronyms though. But if you're using them in the main text,
be consistent. Use the same acronyms in the table, but
make sure you define them in the footnote. Also, things like, if there's particular
ways you define variables that aren't standard or that the reader might not be
sure of. Because the table has to stand on its own, you're going to define those
again in the footnotes. So again, in terms of formatting the table
all of these little decision points you got to make like, do I capitalize the, the
variable names in the table? Do I flush down last year's center?
Do I use Italics? Where do I put the footnotes?
What are the footnote symbols? All those little decision points.
Don't try to reinvent the wheel and figure them out from scratch.
Just go pull up published paper that's right from the journal that you're
targeting and just copy what's in there. Every journal has its own set of style
guidelines. You might as well make your tables and
like other published papers in that journal.
That is helpful in terms of getting your paper in the journal because if an editor
looks at that and says oh yeah, looks professional, looks like things in our
journal. So that's actually very helpful.
Another thing to point out about table formats is keep in mind that most journals
the standard really is to use three horizontal lines in your table.
So you put one above the column headings, One below the column headings, and one
below the data. And a lot of people, the first time they
create a table, for some reason, aren't, really haven't paid attention to that and
aren't aware of that. And so, they come up with very funny
things in terms of where they're putting lines, and I'll show you in a minute.
That looks very unprofessional. So, so here's an example table.
I made up some data. My daughter, right now, is really into the
Wizard of Oz. So there's a line in the Wizard of OZ
where the good witch is asking Dorothy, are you a bad witch or a good witch?
So, they have bad witches and good witches.
She's very into that, she's two right now. So I thought, well, imagine that we had
some data on bad witches and good witches and we wanted to put them in a table.
So I've gotta, a group of thirteen bad witches and twelve good witches in my
study. I've collected some descriptive
characteristics on them and I've put them in a sort of a typical table one that's
describing my study population. So, this is what a typical table, table
would look like, Notice the three horizontal legs.
So if you haven't ever kind of paid attention to that before, just kinda get
that in the back of your mind that, most journals you'll have, sort of, three
horizontal lines. Again, there may be variation with
different journals, so you'll want to go to that journal and see what their
standard is, but this is pretty much, the usual thing is, to have those three lines.
You'll also see sometimes, You know, some journals will also
highlight with some greying each row. So that helps the reader to kinda
distinguish from one row to the next and so that also looks very professional, and
that's done by a lot of journals. So either of these kinds of formats looks
good, looks professional. Alright.
So, I'm now going to show you some things that you don't want to do in your tables.
Okay. So first of all, I mentioned earlier, the
use of lines, The importance of getting those three
lines correctly. So here's a table in which they have
included all of the grid lines. And you might think, well, what's the big
deal. So what, they left in the grid lines, who
cares. Right?
This is such a minor detail, it has nothing to do with the science.
Okay, fair, that's a fair point. But let me, let me tell you, A little
story that I like to tell my Stanford students.
So That doesn't look, If you look at that table it doesn't look
like a professional table cuz that's not the way their formatted in the literature.
It looks different than a professional And so I always emphasize this, emphasize this
very heavily with, with my students at, at Stanford.
Because I've reviewed so many papers over the years, that at some point I kind of
realised and noticed that., Whenever I would get, you know, again I
turn to the tables and figures right away. That's the first thing I look at.
So I kind of notice that whenever I flip to the tables and figures, and the first
thing that jumps out at me is a table with all of the grid lines, I've kind of
noticed over time that those papers tended to be written, possibly by somebody who's
the first time, they've written a paper or just, somebody who, it, it turned out
that, that tended to correlate with problems elsewhere in the paper.
And so, over time, I think I've formed an association between papers that have
tables with all the grid lines and Other problems in the paper.
Because I think again, it tends to be people who are new to writing a paper,
don't know how to format the tables or just are not paying that close of
attention. And so, I've kind of made this connection
in my head. So, when I flip to the tables and see all
those grid lines, there's a little red flag that goes off in my head that like,
whoops, this is gonna be an unprofessional paper and paper, you know, oftentimes does
have other problems in it. So I'm kind of aware of that association
just cuz I, I thought about it since I, I teach about making tables and figures.
I think though that a lot of editors and reviewers probably have the same
association in their head. They may not be consciously aware of it
though, But they got that association from being
exposed to lots of papers and when you come up with all these gridlines there
just tends to be a bad paper. Not always, but it tends to be more likely
a bad paper. So the problem is if you submit a paper
with all those grid lines, or you got this kinda like professional looking table, it
set's of this little alarm bell in people's heads, even if they're unaware of
it, that this is going to be an unprofessional and poorly done paper.
So, Don't set yourself off on, on a bad foot
to begin with. Always get, take the time to get those
grid lines ready. May seem like a silly thing but as I've
said, I've had a lot of students come back to me after taking my course and say, oh
yeah, I've noticed that I do that as a reviewer too, that I have that association
in my head. So it's important enough that I'd like to
spend some time on it. So figure out in your word processing
program how to get rid of all those extra grid lines and just have the ones that
match what's in a professional paper. Make your table look professional.
Along those same lines, a couple of other things we want to do to make that, that
table look professional. You know, you want to make sure that
you've got all your columns, all the data's lined up correctly.
They don't usually look as bad as this, but I've obviously done this for effect.
But sometimes you'll have things like decimal places that aren't lined up, like
all the decimal places should line up. I don't have any decimal places here, but,
you can see that it just looks kind of unprofessional.
Things aren't lined up nicely. I varied here between capitals and no
capitals, so it just kind of looks unprofessional.
So again, to really make sure that you're spending the time to make that table look
professional, It makes a big impression on your reader,
and your reviewer and editors. Another little problem that I see with a
lot of tables is people love to go out to ridiculous numbers of significant figures,
ridic, ridiculous numbers of decimal places.
And I think that is just because you put something like age in your statistical
analysis program and your computer can average those ages and come out with, you
know, as many decimal places as you want in the average.
So you can get a number out of your computer, like 36.007.
But of course, do we really need to know age,
The min age to three decimal places. We don't.
It's not important to go that far out and it makes the table look cluttered.
So go back and make sure that you're not getting in too many decimal places.
I generally recommend having no or one decimal place.
In critical data now of course if you're measuring things right precisely in other
fields you may need to go to many decimal places, but really go out only to the
decimal places that you can claim as significant figures.
And even then, you might want to go out to fewer if it, if it looks better in the
table and you don't need to be that precise.
Make sure that in tables you always give units.
So a lot of st, students, when I get, paper from them, one of the things they
tend to leave off are the units. Again, you're kind of cranking things out
in your statistical analysis program, and you forget about that all these variables
come with a unit. So, for example, age here.
Well, obviously, if you look at 45 and 36, you would guess that this is years, but
hey, could be months. Right?
We don't know for sure. Maybe we're looking at bad witch and good
witch tunnels. So, you, you need to specify again, at VMI
we could probably guess the units. Blood pressure, we could probably guess
the units. But, you guys want to exercise and that
got 30 and 60. Well, is that 30 minutes a day?
Is that 30 minutes a week?. Is that 60 minutes, year?
Is that 60 hours a year? There is no way to tell without having the
units. So, make sure you are also very specific,
it's very easy tell to what you are referring to what the units are.
And then the final tip I want not to do on tables is there's also a tendency to want
to put too many columns in your tables. So I had a nice kind of crisp table to
begin with. Bad witches and good witches, we want to
compare them, see what's similar and what's different between them.
And that's probably was the story of that table.
What's the difference, well, bad witches if you know, they exercise less.
They seem to be less healthy and then in my made up data they're a little bit more
unemployed, they tend to smoke more. So that's the main point in that table.
Now a lot of people will then say, well, oh okay,
Well let me just keep adding data. So let me add an overall of my computer
calculated that, my, my statistical analysis calculated program, calculated
that. So let me throw in a column that shows the
overall. But you can see that we don't really need
the whole overall column here. Because I can look at, you know, bad
witches on average. 45 years old, good witches were a little
younger at 36 years old. You can kind of average that in your head.
You don't really need to be overall, to be given separately.
And also that's not the most important thing here.
The comparison is the important thing. Additionally you notice in the original
table that I showed you, I had indicated p values with subscripts and footnotes.
And what, I just showed you, what's significantly different and what's not
significantly different. Here I've added an additional column with
all the p-values. Now, there may be instances where it's
important to show the exact p-values for all the different variables,
But I don't think that is the case here. Here, we really want to know which ones
were significantly different between the groups and which ones weren't and that all
the reader really wants to gather. And now, the reader's got to go through
the work of looking at each of those p-values and figuring out which ones are
different, whereas the star really drew the reader to which ones were different.
Plus we don't really need the exact penal information here.
So it's just another column that makes that table more cluttered and hard to read
and kind of takes away from the main focus of that table.
So try to get rid of columns that really aren't necessary.
Don't try to put too much in your table. Stay focused on that main point.
So that's my main messages with tables. So then, figures.
There's three types of figures that we're going to talk about.
So there's what we call primary evidence. That would be things like gels,
photographs, x-rays, micrographs, pathology slides.
They are in there to show the quality of the data, and also there's some sort of a,
seeing as believing element, right so if you see the gel, if you see the x-ray, you
see the original raw data, you really, you know, kind of feel more confident in that,
evident, kind of somebody just tells you what was in the job.
So hm in a lot of discipline is very important to have that primary evidence.
Those are usually pretty straightforward to create'cause you're just taking a
picture. Now we get into graphs.
So, these are graphical ways of displaying your data.
And I'm gonna talk a little bit more a-, about some of these.
So, there's things like line graphs, bar graphs, scatter plots.
Again, it's a different way to show your data than to just summarize it in a table.
It's a little bit more complete, and also gives a kind of a, a nicer story.
It's easier for the reader to get information usually from the graphs than
from the tables. And then finally there are drawings and
diagrams and these are probably underused in scientific literature.
These have some really nice purposes, so you can use them to do things like
illustrate the experimental set up or the workflow, indicate the flow of
participants. So sometimes rather than writing a whole
paragraph in your method section, you might be able to put that into a nice
little drawing, or diagram that shows that and that's much easier on the reader.
Or sometimes it's nice for illustrating you've got an idea about the cause and
effect cycle, about cause and effect relationships, you can remodel, you can
put that in a little diagram that can be really helpful and kind of tying together
the concepts for your reader. And sometimes people like to represent
microorganisms and micro particles as little cartoons and that can be nice for
your reader to give them a visual of something they would otherwise not see.
So I'll go through each of these in turn. Of course, all of your figures will have a
legend. And again, you need that legend so that
the figure can stand alone. So it's got to contain a brief title,
Essential experimental details, so that the, the reader doesn't have to go back
and read that main text. This is where you're going to define
symbols and liner bar patterns to say which group is which.
Hopefully, you make those easy on the reader or you might explain what's in each
different panel. If you've got a series of panels, as is
often the case. And it's where you put the statistical
information. What test did you use?
And what are the p values if you are doing statistical comparisons.
So here's an example of a figure legend. I'm not going to read the whole thing, but
the title is "Root Transverse Sections in Electron Micrographs of Tomato and
Arabidopsis show GFP equal I in the apple glass and inside root cells" This is a
paper showing that. Plants actually eat microbes like e-coli.
So that's kind of a cool paper. It has some cool images.
You notice that there's a lot of different panels in this figure.
I'm gonna show you this figure in a minute.
So, they had to define, very carefully, what all those different panels are.
They also have some letters in the figures, and arrows, and they're telling
you what those letters and arrows are in the figures.
So you can read the figure, And have all the information you need
right there. So here's the figure that goes with that
legend. So it's kind of a cool picture, right?
They're showing all those little green parts are the E-coli that are being taken
up by the plant roots. So the, the plants are actually eating the
E-coli, which is kind of cool. You know, you saw all of those white
arrows, and there's some letters like e, m and r.
All of that's defined in a single legend. So this is primary evidence.
It's showing you, hey, you know, here it is,
Here's the green, here's the E-coli in the plant root.
So you're going to go, yeah, if seeing is believing, you're going believe that
that's actually the case or this typical, not a typical primary evidence thing would
be like a gel. You actually want to see those bands on
the gel. So here's the bands on my gel to prove
what I am trying to prove. Alright graphs, we could have a whole
course on graphs so I'm going to give the eight minute version here of graphs.
There's a number of different ways that you can represent data graphically.
And, again, you really need you know, a whole course on data visualization to do
justice to this topic. But I'm just going to name a couple of
different types of graphs, just so you know some of the possibilities that are
out there. So, you'll see things like line graphs,
scatter plots, bar graphs, individual bar graphs, histograms, box plots, survival
curve. There are others that you could come up
with, But probably these are the most common.
Yeah, I'm going to talk about a few of these that are, that come up very
commonly. Again, I'd recommend if you're going to be
writing on a scientific manuscripts those figures are so important that you really
do want to train yourself with, well on data visualizations.
Becoming more and more important as we get,
You know, more and more things online these days. Now you can have colors in
your manuscripts which you didn't really didn't really used to be able to do.
So, think of, think carefully about data visualizations and, and it, it's a topic
for another course, but I'll give you the very, very quick my quick version.
So, so first of all, line graphs. So line graphs I really like, because
they're used to show trends over time. And sometimes time, it might be trends
over time or something that's increasing like age or dose, but kind of similar to
time. It's used to show trends.
And you can display, usually people will display the means and standard errors of,
of different groups. Sometimes if you have a low number of,
participants in your study, you could actually have individual lines.
And that's kinda nice, too, If you have, if you can display them all
in one graph. So here's an example of our line graph.
They're comparing, two groups here, a control and a treated group, and it's,
this is really a dose response cuz they're trying over different, levels of dpi.
They're looking at the number of positive cells and you can see that it's kind of
flat in the control group and that there's something happening over time in the
trajectory, or over dose I should say. And that's what the line graph is meant to
show. And again, see the nice colors that they
have there. This one, and, you know, you have to think
a little bit about what the stars mean above, but that is something to do with
statistical, significance. But anyway, that's a an example of a line
graph, and they have good uses when you're doing something over dose, age or time.
Bar graphs, everybody loves bar graphs because they're really easy to understand.
They're generally used to compare groups at a single time point or a single dose.
They tell a quick result story, everybody can get what's on a bar graph.
So that's nice, because it's really easy on your reader, your reader doesn't
usually have to do much work to understand what's in that bar graph.
So here's an example, this was from that same study on looking at tomato plants
eating E-coli. And so they have two controls here and a
treatments and you can see that you know, A B C. Now of course,
There's a few things and notes I'll make on this bar graph so it's not immediately
obvious to me what the ABC's are. So that's one thing to keep in mind is
there a better way to indicate whatever they're trying to indicate there without
making the bar graph to go to the figure legend and figure that out.
Hm, the other thing about this one that's maybe not quite as visually appealing as
you can get on a bar graph is that they've left the bars, with white space and that's
leaving a lot of white space in the graph, So visually it's not quite as appealing as
it could be. So I'll give you a more visually appealing
bar graph. So, notice, this one they've actually
filled in that bar with a little gray. So that's just more visually appealing.
This one is also in higher resolution. So it looks nicer.
The other thing to point out about this bar graph is, I really like the fact that
they've put the n's in each of those groups on the bar graph, cuz that's one of
the problems with bar groups normally, is you don't know what the ends of the group
are. And it could be that those groups are very
different sizes, and things like that. So they've indicated the ends on that,
So it's nice to do that on a graph when you can.
So that's a nice little bar graph, showing, something's increasing over
different concentrations. Okay, one last thing I'm going to point
out on graphs. Again, this is the very quick version of,
of graphs, But just to point out some of the most
common ones and what they're used for. So, scatter plots.
Scatter plots are used to show relationships between two variables.
Usually you're looking at two continuous variables and you want to indicate
something like a linear, or quadratic correlation.
I, personally, love scatter plots, Because it shows all the data.
So unlike a bar graph which maybe just shows the average or a table which you're
going to just report the average, the scatter plot shows everything about the
data. So it's going to show the good and the bad
about the data. It really, really gives the reader a sense
of what's actually going on in the data instead of just kind of one version of
what the data say. So I love scatter plots for that reason.
It allows the readers to see those individual data points so there's more
information there. So here's an example.
This was a study that was looking at high grade and low grade, brain tumors.
The high grade's in red. The low grade's in blue.
And so you can see that you're getting a lot more information from a plot like
this. They're relating something about a gene
expression value to this outcome, CD44. And, you can see that there's a number of
things that you pick up immediately. So one is, that there does seem to be an
increasing relationship here, But it's not quite that simple, right.
It's not necessarily a line. It might have a little bit of curvature,
even. Also, the, the blue group is lower on the
CD44 levels and the expression values than the red group.
Those tend to have higher values. Mm, you can also pick up that the blue
group has a lot less variation than the red group.
So if they're in a, if they were outliers that people or, if this is a, a human
study or if, you know, animals, human study cells that don't, don't kind of fit
the mold, you'd see those, So there's just a lot more you can see on
a scatter plot. So I really love scatter plots for that
reason. It you're, it is, again airing your, your
dirty laundry a little bit, But it's really allowing the reader to
make a really good assessment of what, what's going on in your data.
And, It also shows you how many people there are.
How many subjects are in, in your data set.
So one thing to keep in mind, Just one little point I'm gonna make on
scatter plots. So here's a nice scatter plot again.
Sometimes, they'll be used to show, like, a linear relationship.
So this one actually came from a theoretical model.
So that's why that relationship is quite clear,
That as the log CE goes up, the log, say, CF goes up and that's a really nice clear
linear relationship. They've superimposed a best fit, a linear
regression line here, which is appropriate to do, and showing you kind of, the
patterns. Just be a little careful, though.
When you're superimposing lines on scatter plots.
Now, I'm going to show you something here. So, here's a scatter plot that I made up.
I had some made up data, where I'm relating some made up data on vitamin D
to, some kind of cognitive test called the DSST score.
The details of that aren't important, But, we've got two variables that we're
trying to relay here and I've plotted them.
And then I superimposed a line here, Sort of probably, you know, trying to
indicate something about, the, the correlation here.
And so, if you look in that line, you know, you're,
That line kind of draws your eye. It, it,
If you look at that picture, you go, oh, yeah, there's oh,
Yeah, Those vitamin D levels go up.
It's a little, It's not a strong relationship, but
there's, some increase in DSST squares. It looks definitely like it's an
increasing, linearly increasing, to some extent.
With the line there, That's what you see,
But it's actually completely a visual illusion.
So, it's not actually increasing at all. So here, if I take the line off.
You can see that these are made up data, So I know that there's no relationship
between those two things. That it's actually a completely flat
slope. So there is no relationship here between
vitamin D levels and DSST score. But when you put the line on, oops,
It suddenly looks like there's an increasing relationship.
So be careful when you're superimposing lines,
Because it often makes the relationship look stronger than it really is and that
can be a little bit misleading. Couple of tips on graphs.
Again, the graphs are supposed to tell a quick visual story, so you want to keep it
simple. You want to make it easy on your reader.
So, there are lot's of graphs that will have triangles for one group, circles for
another, squares for another, and are often really, really hard.
You have to have really good eyesight to differentiate the triangle from the
square. Not only that, but you've got to keep
track of which group is my triangle, which group is my square.
So sometimes, it's really nice to do something like put inside, you know, put,
for your plotting symbol, put a circle, and then put, like, put a C for control
inside that circle, or T for treatment so that it's easy for the reader to, to know,
or to guess, well, C must be control or T must be treatment.
Do something that makes it easier for the reader.
Use colors. And, keep in mind that if your figure is
coming out or graph is coming out to be too complex, maybe that data actually
belongs in a table. So I'll give you a couple of examples,
where I think maybe they tried to put too much in, in a graph.
So, so here's an example I really like the, the line graph on the left here of,
of the screen. you can see that there's four groups and the proportion of
cooperation is going down over time in all the groups,
But one of the groups seems to be different.
That group in red seems to be different than the other three in terms of the
trajectory. So that's selling kind of a nice story.
Kind of move over, They also added in this figure a bar
graph. In the summarizing this same data that's
in, a line graph. What they did is, they averaged sessions
five to ten, So, they wanted to show you like statistically, the red group is
higher than those other three and, that's what they are doing with all those stars.
But, you can see, it kind of gets very confusing, there's a lot of stars here
going on, so it's trying to tell which groups are significantly different than
the other, But, it's really hard to get that
information from, just too many stars on there they're also, you know, the
information, I think, is better presented as a line graph,
Rather than averaging over five sessions, which loses a lot of the information.
So I don't really think you gain much by putting that on the side there.
It is probably just more confusing to the reader than anything else.
So try to keep it simple. Another tip I could give on this graph,
Was on the line graph, it would be nice, again, instead of having those circles and
squares, and, you know, It's kinda hard to differentiate.
Just put right after the line ends to the right of each of those lines put the group
name so that it's easy for the reader. So kind of try to make it as easy for the
reader and as simple as possible. Couple of other graphics I pulled out of a
paper, where I thought there was a little bit too much going on.
It was not telling a clear and simple story.
There's just a lot of data being presented here and there was no kind of clear
pattern to me. There's, green is no virus, and red is
rhinovirus, And, you know, without knowing any
details, I can't glean anything from this figure.
It's just too much, I think and then, also, another figure from this paper.
Just a lot going on, and there's no clear patterns, right,
Where I can quickly visually pick up something.
So I'm not sure this really needs to be in a figure.
It might be better in a table. Alright, so that's my little quick story
on graphs. And, again, I'll refer you to, to take a,
of course, course in statistical analysis or data visualization to learn more about
that. But the final thing I want to point out is
you also have at your disposal, in terms of figures, you can do diagrams and
drawings and these can be really great. So, for example rather than trying to
explain in prose how the flow of participants in your study or the
experimental set up or work-flow of your study, try to quit that in some kind of
table or figure that you are drawing where you show, how, you know, the participants
went through. So, it's much easier to get that from a
picture then from prose. Also, if you are tying, you know, have some hypothetical
cause and effect or model that you are trying to, to tell, hey here's all the,
all these different elements relate. Again, hard to explain in prose,
Easy to explain with a pictures. And it's often nice too if you're talking
about something people can't see like, like viruses or proteins, if you put a
little cartoon in there. So I'm just going to use a couple of
examples of drawing through diagrams. So here's a figure that was, this was a
paper that was looking at, drug company advertisements in journals and so they're
showing you the general layout of a, of an advertisement.
So, you know, you got the picture of the happy physician and the happy guy getting
surgery at the top and then you've got your Kaplan-Meier curve down at the
bottom. So actually, that's really nicely
presented in a little picture cuz I can glean the information from that really,
really quickly. Where, if you try to describe that in
prose, it probably wouldn't be that interesting to read, and it would be much
harder to convey that information. So it's nice to put something like that in
a cute little, diagram. I love this diagram.
This was a, story, paper looking at, dog bites and cat bites and where they happen
and things like that. And so, rather than just sticking this
data into a boring table, They thought, well, we're going to, you
know, visually show you. Well, here's, you know, a couple, there's
not many bites that happen on the seat, But there's a lot that happen on the hand.
That's a much nicer way to present that data than just kind of putting it in a
boring table. And, you know, if you've got things like
this was a, a study on evolutionary games and there are, you know, different ways to
graph approach and bigraph approach and different things going on here.
So you can sometimes illustrate complex ideas like that in a nice little figure,
Where you've got, just you know, you kind of draw it out.
And, if you've got something like software, that you are trying to,
Then this was a study that was looking at Software, so they are kind of showing you
how the software works and with the screenshots look like and things like
that. So that's sometimes again is much more
better presented and sometimes the diagram that we are drawing, rather than trying to
explain that in prose. You can imagine, explaining that process
is going to be lot more complicated. And now, the final thing I just want to
mention, is that nowadays you're not just limited to tables and figures.
We also, actually, in the literature have movies.
So you know, if you've got something that's in a movie form, you could put this
in a supplemental material note. Movies often make great little things for
people to watch and also can be very convincing.
So I will leave you the link here if you want to play that one.
This is from that study that was looking at plants eating E-coli.
And, and another movie, and again, I'll leave you to a, click on the link and play
that one if you want to. The preceding program is copyrighted by
the board of trustees of The Leland Stanford Junior University.
Please visit us at med.stanford.edu.