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Graphs, Gadgets, and Clarity: Mastering the Art of Scientific Storytelling in Research Presentation with Maarten Boers
927 Plays9 months ago
In this week’s episode, we delve into the pivotal role of visual clarity in scientific research. Join me and Professor of Clinical Epidemiology Maarten Boers as we discuss his new book, Data Visualization for Biomedical Scientists. If you are at all interested in being a better science communicator—and especially if you are interested in publishing your work in academic journals—this episode is for you! We talk about how Maarten’s book extends beyond the world of biomedical science into good table design, small multiples, and how academic publishing needs to get its act in order.
Topics Discussed
- The Necessity of Clear Experimental Procedures: We highlight the significance of understanding every step within an experiment. Our discussion unpacks the ways in which clear, precise procedures facilitate reproducibility and validation of scientific work.
- Deciphering Scientific Terminology: Maarten’s book emphasizes the importance of demystifying complex scientific jargon. We examine strategies for breaking down terminology barriers for both specialist and general audiences.
- Graphical Excellence in Research Communication: We focus on the power of well-titled, labeled, and annotated graphs in conveying research and analysis.
- Impactful Captions and Visual Storytelling: Captions are more than mere descriptions—they’re a gateway to engaging the reader. We explore how to craft active captions that not only inform but also captivate and retain the reader’s attention.
- Challenges in Academic Publishing: We confront the practical challenges researchers often encounter with journals, their design (or lack thereof), and other publishing pitfalls. We talk about how to effectively intervene when production staff mishandle figures and how to work within the constraints of journal page limits.
- Ensuring Accuracy in the Publication Process: Our conversation also touches on the responsibilities of researchers to ensure their findings are presented accurately and effectively, even in the final stages of publication.
➡️ Check out more links, notes, transcript, and more at the PolicyViz website.
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Transcript
Introduction to Martin Boers and His Work
00:00:12
Speaker
Welcome back to the Policy of This Podcast. I'm your host, John Schwabish. Thanks for tuning in to the show on this week's episode of the podcast. I welcome Martin Boers, scientist who's written a great new book on visualizing data, something I really am interested in, obviously, and I'm sure you are too, having listened to this podcast. Now, why should you read Martin's book and listen to this podcast in addition to all the other great books that you're reading? I think there are two big things to consider with this book. First,
00:00:42
Speaker
He spends a lot of time talking about table design and we're going to talk about it in the interview. It's something that doesn't really get a lot of attention in the data visualization library.
Effective Data Visualization Techniques
00:00:52
Speaker
So from my perspective, it's really Martin's new book. My book and Steven Fu's book are really the few books.
00:00:59
Speaker
that talk about table design. So that's one thing. If you're interested in better tables, this is the episode for you. The other thing that we talk about a lot is small multiples or what Martin calls matrix graphs. So we talk about good styles and designs and ideas and concepts about creating better small multiple charts, which if you don't know are exactly what they sound like a combination of smaller multiple charts.
00:01:20
Speaker
And the last thing that we talked about that is really interesting for me, from my perspective, as someone who's worked in publishing in the academic field, in academic journals, is how the academic publishing doesn't focus on data visualization and how that's been a detriment to the ability to communicate research and scientific findings more effectively.
Why Focus on Biomedical Science?
00:01:42
Speaker
So there's a lot of information and guidance packed into this interview and into Martin's book.
00:01:47
Speaker
So I hope you'll enjoy this week's episode of the podcast. My interview with Martin Boers starts right now. Hi, Martin. Good afternoon, your time. Morning, my time. How are you? Happy New Year. Good to see you. Hi. Good to see you too. Thank you for inviting me to do this interview. I'm really excited. Yeah, this is terrific. I'm excited to chat with you. I've got your book, Data Visualization for Biomedical Scientists. We'll talk about how it's not just for biomedical scientists.
00:02:17
Speaker
And there are some unique pieces in your book that you don't find in many other data visualization books. So I'm excited to chat with you. So maybe we can start with your background, and then we just jump right in to talk about the book. The first question I have about the book is, what are the unique challenges with visualizing data that biomedical scientists face? And then I think we can, because the book is definitely broader than just that, but I think we can start there.
00:02:45
Speaker
So kind of two questions for you, just kick the whole thing off.
00:02:48
Speaker
Yeah, well, I mean, to be really honest, I don't think there are unique challenges to biomedical science. The reason I decided to focus on what is my own field is that there's lots of general data visualization books around, and some of them also address scientific papers and presentations.
00:03:18
Speaker
In the process of writing my book, I had some feedback from some very notable people in the field and one of them said, you know, why write another book on data visualization?
Challenges in Scientific Paper Writing
00:03:31
Speaker
I mean, look at my book.
00:03:34
Speaker
That's all people need. And I was writing this book because even though that book was great and there are many great books out there and I reference them all in my book. It's not like I suddenly invented this field. But I was struggling with what I could not find in those books that were day-to-day issues in the papers. I was trying to write or mostly I was trying to
00:04:02
Speaker
supervise in my fellows and finding out that there was no guidance at all and everybody did it wrong, including senior scientists.
Improving Journal Templates for Better Visualization
00:04:14
Speaker
Nothing was being corrected from the journals. If you look at the journal templates, 99% are simply horrible.
00:04:22
Speaker
and could be easily improved. So I started writing about that and writing to journal editors. And when I was on editorial board, spending time to improve papers and writing guidelines, and that sort of expanded into something. Then I decided COVID came along.
00:04:40
Speaker
Yeah, let's write a book. So that's basically how it happened. And biomedical science is my field, so I know what's wrong on my field. And if I were to say, okay, and by the way, economics or another field, they have poor graphs as well.
00:04:58
Speaker
Then I really have to go and study those journals to make sure that I was making a fool of myself and people would fall all over me. Oh, you haven't seen me done this and that. I know my field. So I thought, you know, if I stick to that, nobody can.
00:05:13
Speaker
blame me for going outside of my comfort zone. So but let's talk about that a little bit because like I will tell you in the economics field like the graphs are also fairly terrible in most journals and so you spend there's like this this section right at the end that's kind of tucked away but it's kind of like my favorite part because it it talks about this peer review process that nobody talks about um and and I guess I'm not sure what my question is other than why
00:05:42
Speaker
Do you think peer-reviewed journals haven't tapped into what we know is data visualization as a popular means to communicate research and data?
Lack of Expertise in Academia
00:05:56
Speaker
Because nobody feels enough of an expert to comment on it.
00:06:03
Speaker
And so it's from the junior to all the way to the senior level that nobody knows how to do it.
00:06:17
Speaker
I was interested in data visualization right from the very beginning of my scientific career before, so I always spent a lot of time doing stuff. But nobody of my supervisors would ever comment on that part. And if you made a table, well,
00:06:36
Speaker
you just made a table and nobody said, gee, why is the order of your categories like this? Or why have you ordered your numbers in this way? And why is this table so terrible to read? It was sort of a natural phenomenon that tables and graphs would magically appear.
00:06:54
Speaker
And then right from the first version, they would go all the way into the published paper. And unfortunately, that is either still the case or journals have caught up and put in some guidelines that make tables and graphs worse. Right. They built these templates that are not built by.
00:07:14
Speaker
I mean, I'm sure well-intentioned, but not people who may have expertise in how to do these well. Or they, I mean, the most recent version of this is that the bigger journals have put little software machines in place where once you are past the acceptance stage, you go into the proof, you get your proof, but the proof is no longer a PDF, but it's actually a web page where you can correct your paper
00:07:40
Speaker
And the web page has a little table machine in there that does not allow you to format the table properly. Right. Or it makes it so difficult that everybody gives up. Right.
00:07:52
Speaker
There's a revenue stream problem in academic publishing, right? Like my first, the first paper I wrote, I remember the first paper that I, well, co-authored back in the day, the journal helped make all the, the graphics, you know, they, they had all the editors. I mean, there was a team and now you're on your own. It's like, you know, yeah.
00:08:13
Speaker
Um, exactly.
AI and Data Visualization Practices
00:08:15
Speaker
And do you, do you think, I mean, this is looking ahead, but do you think that maybe some of this artificial intelligence tools and as the, you know, database tools, maybe you start developing it better, that will be helpful? Or do you think we're just going to be spinning our wheels and.
00:08:30
Speaker
Well, if you say AI, AI needs to be trained. And if AI is going to be trained with the current body of literature, they will engrain the horrible practice that we have now. I hadn't even thought of that, but you give me a new nightmare to contemplate.
00:08:50
Speaker
That's not going to really help. I mean, the software has improved immensely. So there's much better tools that you can use today than there were when I started out. I mean, when I started out, we didn't have computers. I mean, I'm that old. Punch cards.
00:09:12
Speaker
Actually, I started before we had punch cards. Before punch cards. So my first scientific article was drawn by a scientific artist. Oh, wow. Who had, you know, like architects have. Yeah, the big draft. And then they would have paper and then they would have sets of dotted and dashed lines on paper and they would
00:09:37
Speaker
put it on the line, and then they would scratch the ink onto the... Well, that was pure art. I remember dreaming of a computer program when the first Mac started, when the first Mac came on the market, I dreamt of a program that would do things. And when that program appeared, it isn't around anymore, so I can safely name it without sponsoring anybody. Cricket Graph.
00:10:07
Speaker
Oh, wow. When that program came along, I bought my first Mac because I had dreamed of that program. You click on an axis and it opens and you can change the scale. That sort of, well, that's routine now, but it wasn't around.
Importance of Table Design in Visualization
00:10:22
Speaker
Right. I've seen it all come and now there's such good programs.
00:10:28
Speaker
And some of the programs are invented by people who know about graphs. So the default is already half or three quarters good, which means the errors, if you just keep on the default, you already have a pretty good starting point. And many people don't get past the default, as you know. When Microsoft changed their standard form, fund,
00:10:53
Speaker
Everybody uses that font because nobody ever changes a font. Right. And it's with graphs and with tables. It's, it's the same way you have one template. Oh, that's the way to do it. Okay, fine. Don't think about it. So, so you've mentioned a couple of times tables and the first chapter after the introduction, the first chapter in your book is on table design and.
00:11:14
Speaker
By my count, there are only three data visualization books that spend any significant time talking about tables. Your book, my book, and Steven Few's book. And those are the only three that I'm aware of that spend any significant time. Exactly. And I'm curious, I guess my curiosity really stemmed why you decided to do tables at the very beginning of the book. Well, I was going to do a book on graphs. Right. And then I thought, yeah.
00:11:43
Speaker
I need to do tables first because tables in a way are simpler. Although making a good table is pretty difficult. But tables are simpler because you don't have all these directions you can go in. And they are sort of the basic staple of science. That's not graphs, it's tables.
00:12:09
Speaker
And I said, nobody writes about them. So I was highly inspired when I didn't have your book then. I just had Steven Fu's book. And I thought, here's a kind of writes about table. And it's really, I mean, his table chapter is really very good. So like with all things, do I need to add to that? Yes, because Steven does not address the issues I have in my field with tables and extensively described in the chapter where you have
00:12:39
Speaker
And I'm moving away from that, but that's what you see very commonly. You have multiple numbers within one cell that are, you know, unclearly separated. So you have these number blurbs that almost become words inside a cell. And then on the next line, you have the same blur, but slightly differently. And, you know, within four lines, your table is a total mess. So there were quite a number of things that
00:13:07
Speaker
Steven Few didn't have to focus on because his tables were basically about business and sales and housing and whatever. So it's sort of one category, which is either dollars or bricks or whatever. We have all these multi-item tables with different things. So there's highly specific things that I was struggling with before I wrote the chapter and I had sort of found some solutions for.
00:13:36
Speaker
which were universally rejected by journals if I submit it. They say, yeah, but this is not standard. I mean, it's so interesting, like they have their own publishing platform, whatever tools they use. Yeah. And it's like, that's that's it. And if it doesn't fit in that little box, then. Well, it's worse than that. I think most of the journals
00:14:00
Speaker
have one template which looks like a format, but what they do is they just pour your numbers in the cells and then forget about it. There's some good journals that make very nice tables, but
00:14:19
Speaker
Usually it's just, Oh, you're submitting a table. Okay. Here's our matrix, put it in. There you go. This is our format. This is how we always do it. So there's no, there's no thinking behind it. It's just production. What would your top recommendations be for, for people when it comes to making good tables? I can name only one. Okay. Only, only one.
00:14:43
Speaker
love it. Because I have a lot of one. Yeah, one would be good. Yeah. I think the first recommendation would be proper alignment. Yeah, it's it's about categories and numbers. And if your numbers are not properly aligned, they're difficult to read. Yeah.
00:15:01
Speaker
It is fascinating to me when I see this, I'm sure, I mean, you know, science is science. So they have, like you said, you have multiple things in a cell. You've got the, you know, a coefficient and a standard error with stars and parentheses around it. And it is shocking to me how the numbers are not aligned and it is just objectively difficult to read. I don't think we need any sort of like real fundamental study to prove that point. It is just harder to read.
00:15:29
Speaker
Exactly. And it's so basic and it is fairly easy to correct. I mean, there's some issues, you know, shall we do it this way, that way? But it's not like, oh my God, how am I going to solve this problem? It's just saying this is how you do it and implementing it. I think the other thing about tables is
00:15:51
Speaker
to communicate to people that tables are actually sort of like very simple graphs. So you have to think about the ordering of the information in your table.
00:16:05
Speaker
which I term after one of the other giants telling a story. So it's not like you make a table and you order your categories by alphabet or by the order in your database or by random.
00:16:30
Speaker
Whatever your code says the variable name is. Usually things you find important are going to be at the top of the table. So don't let me scroll through 18 rows of noise before we hit on the thing you wanted to show me in the first place.
00:16:47
Speaker
So those, it's more than one, that would be the two things and then there's all the other stuff because as you rightly noted in the prep for this interview, I think that the tables chapter is actually the longest of the whole book. It is interesting because Steven's chapter on tables, I don't remember, but exactly where, but it's somewhere in the middle of the book. The chapter I wrote on tables is at the end of my book. And I just, I did, I found it interesting
00:17:15
Speaker
that it was at the beginning of this book, but when I think about, at least in economics, and I'm guessing it's similar in your field, if I think about,
The Role of Matrix Graphs
00:17:23
Speaker
picking up any journal and just going page by page and counting graphs versus tables, I'm sure there are more tables than graphs in any random journal that I select. And in every paper there's a table one and it always comes before any graph. It gives you the baseline data. So it's sort of really, really basic.
00:17:47
Speaker
Yeah. The other interesting, uh, piece of the book is you have an entire chapter dedicated to, you know, what you call matrix graphs. Other people call it small multiples trellises panel chart. I mean, whatever. Right. But so again, I'm curious about the decision to include, to, to write an entire chapter about it. I think it's, uh, I don't, I'm not saying I disagree with it. I actually agree with it that it deserves it's, it's one of the, one of my pillars of good data visualization is.
00:18:17
Speaker
think small multiples and see where you go. So again, what was your thinking behind a whole chapter dedicated to small multiples? And then we could talk about what your, you know, top recommendation would be when people are creating them.
00:18:29
Speaker
Okay, so that chapter actually has two parts. The basic part, the basic principles of small multiples, which covers topics like trying to harmonize your axis so that you can do away with a lot of labeling and trying to, again, get the order right for the message you have in your data.
00:18:54
Speaker
We like to compare horizontally rather than vertically. So if your main comparison is horizontal, they should be like this, all that sort of stuff, which goes for all of those, what I call matrix graphs. But the real story is the second part. And the second part is my effort to make a good matrix graph for basic scientists.
00:19:23
Speaker
E. So in my field, basic scientists are the people in the labs. They call themselves usually translational because they want people to feel that they're connected to the bedside. But they do stuff with cells and with DNA and with experimental animals and what have you. Right. And within my field, that is a really close chop.
00:19:49
Speaker
of people who have their own methods of doing research. And if you're not in the ink crowd, you usually don't understand anything of what's going on. And that's because they have their own codes, they have their own abbreviations, they have their own way of doing things, they have their own way of doing statistical analysis. And I'm sort of a statistician as well.
00:20:14
Speaker
doing a lot of things that in the rest of the world is found to be quite objectionable and not really very right, like doing experiments with very, very small sample sizes and then doing paramedic statistics to see differences, significant differences, and then doing multiple tests. And I can go on and on. So there's a lot of things in methodology
00:20:41
Speaker
in my type of methodology, which is not the lab mechanistic methodology, but really statistics, how to set up your experiment, how to do controls and all that sort of thing, which is
00:21:03
Speaker
Well, difficult to understand and usually not the way I would recommend. But the other side of it is they do a lot of, so they have this theory of, you know,
00:21:15
Speaker
whatever. Agent A blocks the production of some sort of stuff that you need or not need from the raw compound. And we're going to prove that that is the fact and also that it has a biologic effect that is relevant. And so they have this system where they say, okay, let's first look
00:21:38
Speaker
in the genes whether this is happening and then let's look at the gene expression and then let's look at whether the cells who have that gene expression are actually in the place where they should be doing the work or not and then if we have that then let's look at you know in whole lab animals and let's see whether the agent that is needed for that process is actually being blocked or not so they have
00:22:04
Speaker
a lot of ways to buttress their theory, which is really very cool. In human experiments, we have this drug, and we put it in the human and see what happens to the human, and we have to infer all those steps in between. Their, if you want statistical methodology, I think is very poor, but their biological methodology
00:22:26
Speaker
is very very advanced very precise very elegant they always have you know three or four ways of proving their point so yeah we start here and there and there and there and that's why this is true so
00:22:40
Speaker
Really good stuff, beautiful science. And I always thought when I saw these horrible mattresses that they produce, because they have like in an eight minute presentation, they will have 20 slides. And all those slides are small multiples of at least 16 graphs with unreadable letters with no explanation. And they'll say, so here you see that A was blocked by B. And by the way, next slide, B was blocked by C, another 20 graphs.
00:23:10
Speaker
Oh my God, oh my God, I can't. So I thought, you know, it may just be that I'm too stupid to understand this stuff. And all the rest of these basic science people, they understand and they're happy with these slides, just me being stupid. Okay.
00:23:26
Speaker
I don't understand this graph. And I had a colleague, you know, I work in a university department with a lot of basic scientists. And there was a very nice presentation from one of his PhD students. And I saw from the slides, okay, here's someone who's, you know, sort of interested in trying to convey what's going on to me who is not in lab. So that was the starting point. I said, look,
00:23:49
Speaker
I saw this presentation. It's nice. I understood about 25% of it. I want to understand 100% and I want to see your graphs and I want to see whether I can improve it after understanding so that it's better. Is it okay? So it was a bit of, you know, to and fro before I had the data and I had the graphs and I had, so I started building this. I think I wasn't working full time. I think it cost me three months.
00:24:17
Speaker
Oh wow. To understand the science, you know, really on the detail level. And again, you know, the science is not really very difficult. It's just understanding what's going on in the nucleus with that cytokine and that stuff and what is being activated and repressed. But you have to know what kind of abbreviations they're using. Are they useful? And then you find out they always do the same experiment.
00:24:47
Speaker
It's one generic experiment. And they do that in different settings over and over and over again. It's like this. Okay. I have a negative control. Yeah. It's normal saline or unstimulated cells or wild type baby calves or whatever. It's, you know, let's see what happens. That is my zero condition. Yeah.
00:25:14
Speaker
And then I have my positive control, which is, um, let's inject this baby calf with something which will turn all the white blood cells on. We know that it does. So, okay. In our system, let's see what happens. And we have, you know,
00:25:30
Speaker
all the local side cons going up or all the interleukins being activated, whatever. So that's my positive control. Okay. Now let's put in the agent we think is going to do something in this system. Right.
00:25:45
Speaker
Let's see what happens in this leukocyte kind of whatever the system is I'm using to measure. Okay. We have that. Okay. Now let's see what we, what happens if I put in a blocking agent without my stimulus, nothing happens because nothing's being seen. Now let's put them in together. Is it really blocking? Yeah, because the original is going down by 50%. Right. Okay. So this is blocking that. Okay. Good.
00:26:12
Speaker
Next experiment, let's try the same thing in another setup with different readouts. Is it happening there too? Is it happening there too? Is it happening there too? And then they cycle through this because once they've shown this to be a blocking agent, they're going to see, okay, if I block it and now I stimulate something else, what will be the effect downstream and it goes on and on. But the basic experiment is always positive control, negative control, action blocking. So those are the generic labels.
00:26:42
Speaker
So why would I need to read 16 graphs with HRZBQ25 and have to go down to the footnote, which is half the page long, and halfway it says stimulates ABCLL50X, which is the readout system that I'm using in this little panel.
00:27:07
Speaker
Anyway, I'm using a lot of words, but that chapter cost me three months to make. And in the end, well, you've seen it in the book. I hope you said, okay, from the original, this is quite an improvement because I sort of can see what they're doing with little text saying, in this experiment, we showed blah, blah, blah, but not that. And then here, this was stimulated and the labels, the text and the labels are as generic as possible with as little abbreviations as necessary. And then sort of
00:27:37
Speaker
walks you down that path of little multiples so that at the end, you have the story. Yeah, it's kind of like a little comic book. Yeah, it's a comic book. Exactly. That's the word. But it's interesting. Your story of that. What's interesting about it is that your focus is not
00:27:56
Speaker
so much on, you know, should this axis be this width and should the grid lines be this color and should the line be such and such. It's interesting to hear you talk about it because the focus of that story was on making the labels and the language accessible to people.
00:28:17
Speaker
Exactly. And it is interesting to me, because this is one of the things that I talk about people that that it's it's about the titling and the labels and the annotation because
00:28:28
Speaker
you know, a graph, whether a graph is beautiful or not is a lot in the eye of the beholder, right? You like blue, I like green, whatever, but it's being able to tell people what's going on in the graph with the words. And I guess I'll say one last thing and then I'll let you just respond that I think you've heard this too, where people say, oh, you should be able to look at a graph and get it right away. And I just, I don't think that's true because you need words.
00:28:58
Speaker
Well, you know, there are rare instances. I mean, they say, you know, one picture is worth a thousand words, okay. But usually the graphs that are immediately obvious are those situations where I say, did you really need a graph for this?
00:29:18
Speaker
So, you know, if I show you a graph with a mean length of men versus women, and you see a bar with men being higher than women, you say, okay, so men are bigger, larger than women.
00:29:35
Speaker
Then I say, okay, you could have said that in one sentence, right? This is what pharma used to do. They've moved on from that, I have to say, but they used to show you these graphs, you know, before, after bars. Oh, right. Oh yeah. So, oh, now I see it in a graph. Oh man. No, I think, and there's also this other thing of, um,
00:30:02
Speaker
I think more traditional scientific views that you should not impose on the reader your interpretation of the data. I'm just showing you the data, and then you can make your own interpretation. And I'm not going to bias you by telling you, well, that's all nonsense. In current times, people spend
00:30:28
Speaker
maybe two or three minutes on an abstract before moving on to the next and if you want to hit them you hit them between the eyes so the the graph and the caption and and everything is telling you that message again and again again so your caption should not read here's the results of my experiments i mean that's not gonna help it's gonna say this clearly shows that a is blocking b
00:30:52
Speaker
Yeah, I find this fascinating and it happens in scientific publishing all the time where people say, Oh, I can't tell you what the result is in the graph. But if you go to the text that they write, the text will tell you exactly what the argument is.
00:31:09
Speaker
but like when you look at the graph, it's like, it's just purely descriptive. And I never understood that disconnect between what I write in the text versus what I write in and around the graph. Like why don't they don't? Well, yeah, I mean the same discussion around what one calls declarative titles of papers. So many journals will not allow you to say,
00:31:35
Speaker
A randomized trial showing drug A is good for patients with B. Right. No, no, no, no, no. You have to say a randomized trial of the effect of A in patients with B. Yeah. But in the text.
00:31:50
Speaker
In a text, it'll tell you that first piece. It's just so frustrating to me. I just don't understand it. But I want to come back to what you said earlier because it is really two
Influence of Pioneers in Data Visualization
00:32:03
Speaker
things. I think it's sort of those two things labeled, I think, John Cleveland.
00:32:11
Speaker
You know of John Cleveland? It's an old book, but he was also one of the pioneers. William Cleveland, Bill Cleveland. Yeah, together with Tuft. Those were the two pioneers in the field, the giants that we still worship every day. But I think Bill Cleveland said clear vision and clear understanding.
00:32:36
Speaker
you need them both and they're not completely independent. But first you think, okay, what's the main message here?
00:32:46
Speaker
And how I'm going to tell this story, that is the basic. And that would be maybe your storyboard of your multiple panels saying, I want to show first this experiment and that experiment and that. And these three together lead to a conclusion which I can use in my next set of experiments, et cetera, et cetera. If that's not there, if the order there is wrong, then you're immediately lost.
00:33:08
Speaker
Then the clear vision comes in with not only clear labels, but also consistent labels and color coding and cues in the symbols that you use.
00:33:25
Speaker
and the thinness of the axis versus the thickness of the series lines, and always making your experimental condition slightly more prominent than your control, either by color or by you or what have you. And that is the technique of clear vision. It's driven by this story, but you want, I mean, it's as simple as, this happens all the time.
00:33:55
Speaker
you have this order of, you know, A, then B, then C, and then you have a graph where the order is C, B, A. Like, you know, immediately your brain is, hey, what was first here? And that's usually the programs that do that, right? They have a tendency to give you the wrong order. So all these things is designed, and like in tables, ordering is designed.
00:34:24
Speaker
It sort of fits together and you need to go back and say, okay, now I fiddled around with my labels, but is the story still clear? Oh no, because here I used another label because it fit better or because it was more appropriate in this graph. But given the story, perhaps I should change it to the other label because then the consistency is maintained. Or I can't, but then I thought about it.
00:34:47
Speaker
Right. I wanted to ask you one last question on the small multiples. So some of the examples in the book have, say, an odd number of graphs. But the way at least some of the examples are is, you know, you might have, let's just say, two squares next to each other, then a rectangle below it that kind of spans across. What would you recommend to people if they have small multiples, but they have an odd number? So they have one, two, three, and they have this blank
00:35:15
Speaker
area that's kind of left over. What do you recommend people do with that blank area? That's a difficult question also because usually you're not the one making the page layout.
00:35:31
Speaker
Yes, that's true. So any form you produce will be handled or mishandled by someone in the production process, turning it into something different. I remember with the tables article that I wrote that they got the layout so wrong that I ended up printing the proof.
00:36:01
Speaker
cutting it into pieces putting the layout together and then making photographs that say I want it in this order so if you have these if you have these three panels they could either make a big white area or they could just fill it up with text in which case
00:36:20
Speaker
it maybe it doesn't matter or they put the caption there which if it's big enough you know fits coolly inside then you have to you know place your i would say then then you know you have a row on top if i do it in mirror image and i would put the the third one
00:36:36
Speaker
uh on the right side and then I put the caption on the left below the first well you know that sort of thing if they allow you to fiddle around that's that's fine I don't think you need to fill up a square space with a you know a stretch graph just because then
00:36:55
Speaker
you'll have the block fields. But it does remind me to say that it's really important that you think about the limitations of the journal page, whether it be electronic or paper. It isn't really helpful if you make graphs that fill one and a half column where you know it has two columns because either they'll blow it up
00:37:21
Speaker
which would be nice, or they'll make it smaller. And then everything becomes unreadable. So you have to think in advance, okay, what do I want for this figure? Can it be in one column? Can it be in two or should it be a full page? And if so, should it be rotated, which is usually not good for figures because people read upright, et cetera, et cetera. So that kind of thing. Yeah, yeah. No, but that's a good point. And it is true that a lot of journals don't tell you that until
00:37:51
Speaker
And they never tell you that, and they just say, this is how it's going to be, and then you're going to start. And most authors are so thrilled that their paper is finally accepted. Right. They don't want to be difficult, where the reality is the science editor is usually on your side, and is just as exasperated with the production process as you are, but is out of the loop.
00:38:17
Speaker
The moment it goes into production, you're dealing with technicians who may or may not be helpful. It's perfectly fine just to say, you know, sorry, this proof is not okay because this and that. And then I've had proofs come back four times. But then of course I'm a cranky old man. So if you're a junior fellow.
Engaging with Martin Boers' Work
00:38:40
Speaker
Well, on that note, so the book is Data Visualization for Biomedical Scientists, Creating Tables and Graphs at Work. Martin, where can folks find you if they have questions or they want to bring you in to talk? What's the best way to find you? Well, I think the best way, if you just were to project the slides I sent you with my personal details, also the
00:39:07
Speaker
the link to the book, if they want to buy it, it's QR codes. And I have to say, you know, I'm actually next week, I'm going to be in Canada, in Toronto to give a database course, which is, you know, a half day affair with some prep work. And that's really a lot of work to do. Yeah. You know, so I do travel.
00:39:32
Speaker
I don't organize these courses myself, mostly. I just tell people, you know, you want me to give that course, okay? Then I'll come over and this is what you need to do. Sounds great. Yeah, that's great. Well, I'll put links to the book and to your classes site and folks who are getting touched. So Martin, thanks so much for coming on the show. I really appreciate it. It was a great conversation. Thanks.
00:39:55
Speaker
Thanks for tuning in to this week's episode of the show. I hope you enjoyed that. I hope you will check out Martin's book. I put links to his website and all of the things that we talked about on the show notes at policyvis.com. And you can check out other resources, other tutorials, and other things around the world of data and data visualization at policyvis.com. Thanks so much for listening. Until next time, this has been the Policy Vis podcast.
00:40:21
Speaker
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00:40:42
Speaker
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