Paper Writing for PhD Students, pt 8: The Lede – How To Hook The Reader

Previously on this show: how to write the introduction – a four-paragraph template

Keyboard

The first sentence of the first paragraph of any written piece of text is crucially important, as all writers of fiction know (“Call me Ishmael.”). Make it as strong as you can.

First impressions matter. The subset of potential readers who, after getting lured by the abstract of your scientific paper, have decided to have a closer look will first encounter the first sentence of the introduction. For them, this is another decision point: to read on or to stop. The second important sentence is the second one, and the third important sentence is the third one, and so on. The reader can choose to stop reading at any point, after each and every sentence. This means that the first sentence will be the most read sentence of your paper. Your second sentence will be read by fewer readers than the first, and your third sentence will be read by fewer readers still, and so on (if we assume that readers do in fact begin at the beginning instead of jumping in at random points). You will lose readers sentence by sentence whatever you do. This cannot be avoided.

The stronger the sentences, however, the lower the rate of attrition, and the higher the chance that some readers will make it through to the last one. Make the sentences flow and your readers will stick around. Glue them together with transitional words for clarity; place signposts to guide the reader. Create contrast and tension for excitement. Use cliffhanger endings: pose a question. Answer it in the next sentence.

Journalists use the term lede for the first few sentences of a news story—that is indeed how they spell it, instead of “lead”, presumedly for historical reasons that involve mechanical typesetting and lead (the metal, that is). The lede is the lead portion of a news story—it gives the gist of the story, it sets up the story and, most importantly, entices the reader to read the rest. While the lede should give a clear picture of what the story is about, it should not give the whole game away. The lede should raise questions so that the next paragraphs of the story can satisfy the curiosity of the reader by providing answers and details. Journalists even have their standard schemas for ledes. The inverted-pyramid lede attempts to compress the who-what-where-when-why-how of a story into a single sentence or two, and then adds details in decreasing order of importance. The question lede begins with, well, a question, one that you absolutely need to hear the answer to.

Let us have a look at some great openings and powerful first sentences.

As the first example, consider the first sentence of Battiston et al., “The price of complexity in financial networks”, PNAS 113, 10031(2016): “Several years after the beginning of the so-called Great Recession, regulators warn that we still do not have a satisfactory framework to deal with too-big-to-fail institutions and with systemic events of distress in the financial system”. This is a powerful beginning that immediately tells what the general problem addressed by the paper is. It also forces the reader to read on—after all, who wouldn’t want to know where this story is going?

Another example of a great opening, from Centola & Baronchelli, PNAS 112, 1989 (2015): “Social conventions are the foundation for social and economic life. However, it remains a central question in the social, behavioral, and cognitive sciences to understand how these patterns of collective behavior can emerge from seemingly arbitrary initial conditions.” The problem that drives the research is clearly spelled out in the second sentence. Note that in this paper, the exact research question will not appear before the 4th paragraph. The introduction forms a funnel from the broad problem to the more detailed question.

Finally, here is the first paragraph of Altarelli et al., Phys. Rev. Lett. 112, 118701 (2014): “Tracing epidemic outbreaks in order to pin down their origin is a paramount problem in epidemiology. Compared to the pioneering work of John Snow on 1854 London’s cholera hit [1], modern computational epidemiology can rely on accurate clinical data and on powerful computers to run large-scale simulations of stochastic compartment models. However, like most inverse epidemic problems, identifying the origin (or seed) of an epidemic outbreak remains a challenging problem even for simple stochastic epidemic models, such as the susceptible-infected (SI) model and the susceptible-infected-recovered (SIR) model.”

The above paragraph gets from the topic (tracing epidemic outbreaks) to the research question (identifying the origin of an epidemic) with three sentences, and the authors have even managed to include a brief historical detour of the you-know-nothing-John-Snow variety (sorry, I had to). This a great opening. The reader gets a clear idea of what the paper is about, and becomes curious: how did they solve the seed identification problem?

In the next post, we’ll move from the introduction to methods & results.

Paper Writing for PhD Students, pt 7: The Introduction – a 4-Paragraph Template

Introduction

The previous episode of the how-to-write-a-scientific-paper series can be found here; if you are new to the series and want to start from the beginning, click here. In this rather long one, we begin to move from theory to practice and talk about how to write the Introduction with the help of a four-paragraph template.

In terms of structure, the introduction of a scientific paper should follow an hourglass shape (broad-narrow-broad) but emphasize the context—the top of the hourglass—more than the resolution of the story.

A good introduction section begins with a paragraph that sets up the broad scientific context. This paragraph is important: it is the part of your paper that is most likely to be read, in addition to the abstract. In our now-familiar film script analogy, the role of the first paragraph is that of Setup. It introduces the world and the characters; it makes the reader familiar with the concepts and ideas that define the topic of the paper. The first paragraph also paves way for the coming paragraphs: it is the first step on the path to the sentence where the exact research problem of the paper is stated. To get the reader interested, a well-written first paragraph should already point out a broader gap in knowledge that the paper’s results aim at filling.

After the broad context has been introduced in the first paragraph, the scope of the introduction should narrow down. The next one to two paragraphs should move to the Confrontation phase: they should frame and motivate the problem tackled by the paper. They should also cite relevant literature to provide context and connect the paper to the streams of thought that together form your field of science. Then, the exact research question addressed by the paper should be explicitly and clearly stated. This sentence that reveals the question is the climax of the introduction, its highest point of excitement; it is also the narrowest point of the top-heavy hourglass.

What happens next varies slightly, depending on the format.

For short papers and the letter format, the introduction has to be wrapped up rather quickly; it is common to summarise the main finding and tell how it was obtained with one or two paragraphs before moving on to detailed results and methods.

For longer papers, it is common to provide a mini-review of the state of the art, an account of what others have done in the general vicinity of your exact research question. This can be followed by a condensed account of your approach to the problem—your experiments, methods, or theoretical points of view—followed by a discussion of your main findings. Note, however, that there are “old-school” traditions of scientific writing where the results are not discussed in the introduction at all: the approach is, but the outcome is not. Whether such a spoiler-free introduction is mandatory, expected, or grudgingly allowed depends both on your field and your journal. Finally, the introduction of a long paper often finishes with a map of the paper, an outline of what is to come: “In Section X, we will discuss…” and so on.

If you pick some of your favourite scientific papers and analyse their introductions, taking the time to understand the role of each paragraph, you will see that they almost always follow variations of the above arc. It is even possible to try to cluster papers by their type of variation—how many paragraphs are there before the key problem is stated? One, two, three? I’ve seen PNAS papers that move from the broad context to the exact research question in one long paragraph, but this is an exception rather than the norm. I would also rather split such long paragraphs. In any case, there is something of a formula, whose exact details depend on the format and length of the paper and the writer. The paper’s topic and its familiarity to the readers of the journal also play a role: research questions that are obvious to the intended audience do not require lengthy explanations, but new points of view or unexpected questions might.

So because there is a formula, let us use it as a template to write against, or as a starting point. Following a good formula makes writing easier. To this end, I will cover a paragraph-level template of the introduction section of a scientific paper that I often start with.

The aim of the template is to help you to develop a paragraph-level outline for your introduction. Developing an outline is essential before writing entire sentences—as I keep repeating, it is better to plan first and write afterwards. After all, how can you write if you do not know what to write? So, use the template to plan your introduction. For each paragraph, make a note of the topic and the point of the paragraph. You can list a few citations if you can already think of them. Also, do consider how to begin and end the paragraph. While we are not writing entire sentences yet, think about the points that the first and last sentences make—and by all means, if you wish to sketch these sentences, do so. The first and last sentences are the power positions of the paragraph. They have the biggest impact. Use them wisely.

This template is well suited for letters and short papers. It proceeds to the exact research question and the main conclusion of the paper rather quickly—the whole introduction is just four paragraphs long. It shouldn’t, however, be too difficult to expand the template for longer papers: just use more than one paragraph for each topic, and add an outline of the paper to the end if you wish. It is also perfectly possible to squeeze this structure into two paragraphs: just merge the first two and last two. The first two paragraphs provide context and lead to stating the research question; the third paragraph elaborates on the question and how it was approached, and the fourth paragraph states the main conclusion of the paper. The narrowest point of the hourglass is immediately after the second paragraph.

The first paragraph of the introduction is the opening and the so-called lede (it is really spelt that way; more on ledes in the next post); it defines the research question in broad terms and triggers the curiosity of the reader. It provides background—what is already known—and perhaps a glimpse of the knowledge gap, the unknown. In terms of plot structure, the first paragraph deals with the Setup and often the Confrontation as well: it introduces the world, the key characters, and an open problem, and makes the reader want to know what happens to them in the paper. At the same time, the first paragraph identifies your intended audience: readers who are interested in this particular world and its inhabitants.

The first sentence determines the topic of the paragraph and sets expectations on the contents of the whole paper. Avoid the easiest way of entry: it is tempting to use the (too) common opening where you first tell that your research topic has become important in the recent years because of this and that. I’ve done this far too often and I promise to avoid it in the future. There are more exciting ways to begin your story! Say something powerful. Move directly closer to where the gaps in knowledge are—do not begin with a long account of what is well-known already. You can fill in the details later.

After a strong beginning, you can continue the paragraph by giving a short overview of the state of the art, of what has been discovered already. This involves citing a number of earlier works; the aim, however, is not to provide an account of everything in the field—that’s what review papers are for. Rather, you should choose a handful of citations that provide context for the research problem that you address, and that at the same time connect your work to the broader progress of your field. Do cite your own work too, if relevant to the problem. This mini-review of what is already known can fill the rest of the first paragraph. You can describe past research in chronological or in topical order; often, what works best is a funnel structure, where you move closer and closer to your actual problem with every sentence.

The first paragraph is made stronger if there is a contrast, a sentence that says “Despite all this, we do not yet fully understand X…” or “However,  the role of Y remains an open question” or similar. This sentence can conclude the paragraph, or it can lead to one or more concluding sentence(s) that, say, discuss why it would be important to solve the problem or explain the problem in more detail. Note that the issue that provides contrast, the “not-understanding-X”, doesn’t have to be the exact research question that your paper deals with. It can be something bigger—the broader motivation for your question.

The last sentence of the first paragraph should lead to the second paragraph in a natural way. The above way of contrasting knowledge with the lack of knowledge provides an easy bridge. Your second paragraph can then begin by addressing whatever device you used for contrast—the knowledge gap, a lack of studies, or a lack of consensus on some matter. As an example, you can begin the second paragraph with a sentence that tells why X has remained an open problem. This is rather effective.

It is, however, also perfectly OK to structure the first paragraph simply so that each of its sentences just adds detail and depth to the point made by the first sentence. The first paragraph does not have to end with a cliffhanger. If your first paragraph follows this structure—framing the topic and then providing further details—the gap between the first and the second paragraph can be used to switch to a close-up point of view, to zoom in on your problem.

The second paragraph narrows the scope from the broad setup of the first paragraph and moves into the specific topic of the paper. In the second paragraph, the plot advances from Setup to Confrontation. Its aim is to get to the exact research question which is stated at the end of the paragraph. The second paragraph’s job is to point out the gap in knowledge that the paper aims at filling, through argumentation and illustration, and with the help of carefully chosen citations that point out the existence of the gap. These citations should emphasise what is not known over what is known—use the known to highlight the unknown. This gap in knowledge can be familiar to the scientists in your field—an open problem that most experts recognise—or it can be a hole in the knowledge that no-one has noticed yet. Except you.

The first sentence frames the topic of the paragraph, just like with the first paragraph; this is, by the way, true for all paragraphs, and we shall talk more about priming reader expectations later. This first sentence comes with the additional constraint that it has to seamlessly fit to whatever concluded the first paragraph, as discussed above. Here are some common devices that help to achieve this. If the first paragraph focuses on known things and does not pose a question, the second paragraph can begin with a contrasting statement or a question: “However, …” If the first paragraph concludes with a question, the second paragraph can directly continue from there: why is this question important, why hasn’t it been solved, or what approaches might be feasible for answering it. Again, it may be that this question is broader than the specific question that you have studied—in this case, use the second paragraph to move from the broad question to the specific question, motivating why answering it is important. If others have tried to tackle the question before you, the next sentences should tell how they have approached the problem, what they might have missed, and how your point of view relates to this existing body of knowledge.

Then, at the end of this paragraph, the research question addressed by the paper is explicitly and clearly stated.

At the beginning of the third paragraph of the introduction, the point of view moves from what others have done to what you have done. The third paragraph tells how you have approached the research question. In terms of the storyline, the third paragraph is about the action that takes place between Confrontation and Resolution. Now things finally start happening. The narrowest point of the hourglass has just been passed (it is exactly between paragraphs two and three).

The first sentence of the third paragraph tells what you have concretely done to answer the research question. It may begin with a more concise and focused formulation of the question. Examples: “To this end, we have carried out an experiment where…”, or “In this paper, we investigate the relationship between X and Y with the help of…” If you have formulated your research question as a hypothesis, state this hypothesis in the first sentence. A hypothesis is a strong beginning for the paragraph, so even if you haven’t formulated your problem as one, it might be useful to try to do it. While a lot of research is not about hypothesis testing, a clear hypothesis can be a powerful device for the narrative.

After reformulating the question, the rest of the third paragraph tells more about your approach. If you have designed and carried out an experiment to answer the research question that was made explicit in the second paragraph, tell about this experiment. If you have figured out a new theoretical approach to the problem, explain this approach. If you have collected and studied tons of data with new computational approaches, tell about the data and the methods. But stick to the point: we are writing a paragraph for the Introduction, not for Methods. There will be time to fill in the details later.

If needed—and if there is space—the third paragraph can be long; it can even be split into several paragraphs.

Finally, the fourth paragraph of the template moves from your approach to your findings. It (briefly) reveals the outcome of your work. As it is about the Resolution of the story, it is something of a spoiler—but everyone knows your ending already if they have read the abstract, so don’t worry.

I often keep the fourth paragraph short for maximum effect. It summarizes the key findings so that a busy reader can stop here, perhaps to return to the details later; yet it leaves enough unsaid to whet the reader’s appetite. Also, the brevity of the paragraph provides a nice contrast with the lengthy third paragraph; a short paragraph gives the impression of weight and importance.

I hope this introduction template is useful to you. Up next: a post on ledes and strong first sentences.

 

Paper Writing for PhD Students, Part 6: Introduction to the Introduction

writing

Recap: we are now at a stage where you have developed a storyline for your journal article, and this storyline has been condensed into the abstract of the paper. You have some figures and perhaps some schematics, categorized according to their role in the story (see the previous post). You have written draft versions of figure captions. Now it is time to start outlining the different sections of your paper. First, we will talk about how to write the introduction of a scientific paper.

Every story has a beginning and an end, and the Introduction is the beginning of the story that you are about to tell.

A good, well-written Introduction does several things: it introduces the reader to your problem and motivates the problem by reviewing relevant research. It introduces schemas and concepts used in the paper. It points out gaps in the existing knowledge that need to be filled for solving the problem. It defines the exact research problem that the paper addresses, and tells how your research has solved the problem or part of it. It shows how solving it contributes to the big picture. It identifies your reader—who should read the paper?—and makes her so curious that she cannot stop reading. It makes her excited about your work. It makes her want to know more.

In terms of our already-much-abused film script analogy, the Introduction takes care of both Setup and much or all of the Confrontation. This section already provides a glimpse of the Resolution of the story too. It introduces the world and the key characters of the story: the problem area and its important concepts. Remember: the reader will only want to read on if she cares about your world and its inhabitants and the problem that they are facing.

To entice the reader, the Introduction should emphasize the question, not the answer. It should not focus on what you have done, but on why you have done it and what follows from it. There should be an engine for the story, an important question, a need to know. This is what drives the story and whets the appetite of your reader. Curiosity is a strong emotion: trigger it with your introduction, and you have a reader.

Sidetrack: <nerdspeak>The Star Wars prequels failed because there was no big question! Everyone knew that Anakin Skywalker would become Darth Vader; how that would happen was mildly interesting at best. Boring! But at the time of writing this, I do not know what will become of Kylo Ren. And I want to know. </nerdspeak>

How to ask a strong question in the introduction? How to frame the gap in knowledge that needs to be filled? Of course, in a perfect world, your research has had a strong, clear question from the very beginning, and the knowledge gap is obvious. Then, just describe it. Perhaps you have chosen a research problem that everyone knows is important, say, how to solve X. You might even be the first one to have solved X—but this rarely happens because obvious problems are to researchers what a bowl of milk is to an alley full of cats. In any case, if your problem is well-known, you can be brief; if there have been earlier, not-so-successful attempts, or if there have been ideas floating around on how to tackle the problem, you can talk about these in the Introduction.

But, almost always, things are not this straightforward, and you need to think a bit harder about how to frame your question. It might even be that you are not entirely sure of what the question is. Perhaps you have started somewhere, but then along the way, you have noticed that the question you were asking was not the right one or the most important one. Then your research led you elsewhere, and now you are trying to figure out where. Perhaps the importance of your question is not obvious at all, except to you: then you need to tell the rest of the world why the question matters. Perhaps the question that you ask is something that no-one else has ever thought of—perhaps it is your question. If so, then this is a good thing: in my view, science is driven by questions instead of answers, and good questions are rarer than good answers.

When your research aims at answering a nontrivial question that requires a bit more motivating, a good strategy is to start the Introduction with something broad and more familiar, and then gradually move on to your new uncharted territory. All questions are related to bigger questions; begin with a big question and use it to frame the problem that you are solving.

In the next posts, I’ll talk a bit about the structure of the introduction (I’ll provide a template) as well as the importance of the first paragraph and, in particular, the first sentence.

Next: how to write the introduction of a scientific paper – a four-paragraph template

Paper Writing for PhD Students, Part 5: Figures

Figures open in an editor

 

[This post continues the PhD student paper-writing series from here]

At this point, we have covered establishing the focus of your scientific paper: you should already have a clear vision of what your paper is about, and the essence of this vision should be encapsulated in its abstract. You should also have the necessary ingredients at hand: the results to be presented in your paper together with ideas for schematic diagrams, organised into film-script categories according to their function and role in the story (Setup, Confrontation, Resolution, Epilogue).

The next step is to expand the storyline laid out by the abstract and to outline the different sections of your paper. This begins with choosing what sections make up your paper. Depending on your target journal, you may need to follow strict guidelines—the commonly used Introduction–Methods–Results–Discussion structure for instance—or to come up with a structure of your own. Even for short letter-format papers that may or may not have subheadings, it pays off to have a clear idea of what goes where. Usually, this is not a difficult task: all scientific papers begin with an introduction and end with a discussion, even if these span just a few paragraphs, and the results are sandwiched in between. Methods may be explained before the results, or after the discussion as an appendix of sorts (like in Nature and other glossy magazines).

What is more involved is choosing the order of presentation within the section structure. Here, a solid, tried-and-tested approach is to begin with the figures and their order of appearance. If you have followed the approach of this blog, your figures already come with handy labels (Setup, Confrontation, Resolution, Epilogue) and therefore you already have a good overall idea of their order. If your paper has to follow the standard structure, schematic diagrams and result figures will generally be placed in the Results section, so that figures of the Setup category come first and those of the Epilogue come last; schematic diagrams of the Setup category are an exception as they may belong to Methods or even Introduction. In any case, you’ve already done much of the work before you have even begun outlining the main text: the categories of the figures mostly determine their order. What remains to be done is to choose the order within each section in which the results and schematic diagrams are shown: what figure leads to the next?

The order of figures should tell a clear story so that each builds on the previous ones. You can use a multi-panel figure to tell a self-contained part of the story, a miniature story arc. You can combine, for example, a schematic diagram that explains your experiment (Setup), some basic statistics of your data (Setup), and a result plot or two that contains an unexpected finding (Confrontation). This mini-story multi-panel figure is a technique that I often employ in letter-format papers where the story has to move fast and get to the point quickly; it already brings the story close to its Resolution and the key result.

When you have chosen the figures, write a draft version of the caption for each figure. You may need to revise the captions later; at this stage, you may still be unsure of issues like notation and nomenclature, so don’t pay too much attention to details yet. However, try to write the figure captions so that they are self-consistent enough for a hasty reader to understand most of your paper just by glancing through the figures. After all, this is exactly what a great many readers do; this is what the editor of your journal does too, before deciding whether your paper is worth a closer look or deserves to be rejected outright. How long and how self-consistent the captions should be depends, again, on your journal; in some of the letter-format top-tier journals, captions tend to be very long, while in the lesser journals where we mere mortals publish, figures are discussed at length in the main text and therefore the captions can be shorter. In any case, please make sure that your caption tells what the reader should learn from looking at the figure. A caption that only tells that here we see Y plotted as a function of X is not enough; it is redundant if you have remembered to label your axes in the figure already. Always tell the reader what the message of the figure is.

Because much of the story will be told by your figures, let us talk about figure quality for a while. Figures are tremendously important; those who only skim through the paper won’t see much else. Figures make the first impression and first impressions matter. Clear, high-quality figures with a professional look tell that a lot of effort has been put into the paper, and the reader is more likely to trust its contents. Amateurish-looking figures with a colour scheme that looks like PowerPoint in the 1990’s leave the reader wondering if the results are of the same dubious quality.

So do make sure that your figures look good. How to do this? First, learn the ropes of whatever program you use to generate your figures, whether it is a Python or R library, or a stand-alone piece of software (like Gnuplot that has been around since the dawn of man; it will probably outlast even cockroaches once mankind is no more). In particular, learn how to change fonts, how to increase or decrease font sizes, and how to use proper LaTeX-type fonts wherever appropriate. Learn how to choose and manipulate colours and colour schemes and symbols and shadings. Learn how to produce figures of chosen dimensions, so that you can later assemble them into multi-panel plots of your choice and combine them with schematic diagrams. Learn to match figure sizes to your target journal’s column width; not having to scale the figures takes some guesswork out of choosing font sizes (see below).

Second, do learn to use a vector graphics software to post-process your figures (and do learn the difference between bitmap images—they are made of pixels—and freely scalable vector images, made of lines and arcs and Bezier curves). At the time of writing, the industry standard (design industry, that is) would be Adobe Illustrator; there are many free alternatives such as Inkscape. With a vector graphics editor, it is easy to assemble multi-panel figures that contain schematic diagrams (drawn with the same editor) and result figures saved in vector formats (PDF, SVG). You can also add text, arrows, indicators, and so on, as well as retouch your result plots, changing line widths, colours of symbols, or their overall appearance. Often, this is much faster than trying to get everything right when producing the plots.

A few words on the layout: always align things—nothing spells “I am being careless” more clearly than subplots and schematics that are not neatly lined up (it takes just a few seconds to do this). Use white space properly: leave enough white space so that things can breathe, but don’t leave too much white space so that the figures don’t look barren.

Discussing data visualisation at length is beyond the scope of this blog post, but here are a few remarks. Pay attention to your colour schemes. For plot symbols, there are much nicer and much more informative schemes than the pure-RGB red, green, and blue symbols that some programs use as default; on top, your reader might be colour blind and have a hard time distinguishing between red and green. Always use different symbols AND colours for different curves for maximal clarity. If you want a personalised colour scheme, google for colour scheme generators (you have already learned how to set hexadecimal colour values in your program, right?). For heat maps and similar, pay attention to the neutrality of the colour map you use: make sure that it doesn’t artificially highlight some part of your range of values. In all cases, use colours consistently through your figures. If red and blue are categorical indicators of, say, two different data sets in a graph, do not use a heat map where red and blue indicate high and low values: reserve red and blue for the two data sets, and always use them this way. Likewise, if you use a colour map with a gradient from low to high values, reserve its colours for this purpose alone.

Then, labels and fonts. First, always label your axes. This is self-evident, but I still have to explicitly mention it; even though forgetting to label the axes of a plot should feel roughly like forgetting to get dressed when leaving for work in the morning, it still happens. So, I repeat: label your axes, period. At all stages of your work, even if the plot is just a draft for your eyes. And when labelling, please do make sure that the fonts you use are large enough when the figure is scaled to its intended size; if you have chosen the plot’s dimensions so that no scaling is required, use 10 or 12 pt. Not paying attention to font size is a very common beginner’s problem, and there are even many published paper where a magnifying glass is needed to understand what is going on in the figures. I suspect this has to do with the defaults of the commonly used software packages; default font sizes are almost always tiny. I’ve rarely (if ever) seen plots with annoyingly large fonts, so if in doubt, double your font size.

Common errors in results figures

Figure 1: Do avoid these common problems!

Finally, a few words about “having an eye for design”. While coming up with beautiful and impressive figures seems to come more easily for some, every student can learn to produce good-looking visuals. I’ve many times heard someone say “I cannot draw, and therefore my figures look ugly” but—as with any skill—it just takes time and patience; you do not need to go to art school to learn the essentials. Just like learning how to look at things is the key to learning to draw well, the key to producing great-looking figures is knowing how they should look like, instead of stumbling blindly. This is best learned by imitation. So, next time, take one of your plots that you are not entirely satisfied with, and look up a similar figure in some journal article that you like. Look at the two figures side by side, and try to spot the differences in composition, colours, fonts, line widths, and so on. Then modify your figure and keep on modifying it until you are satisfied with the outcome. Next time, you might not even need a reference figure.

PS This series of posts has been rewritten and edited and published as an ebook! Go get it from your favourite digital store!

Paper Writing for PhD Students, Part 4: Theatrical Cut, Or How To Konmari Your Paper

Laptop with a text editor open

This post continues the paper-writing self-help series for PhD students directly from where the previous post ended.

Once you have decided the key point of your paper and have settled on its main conclusion, the next step is to choose what results go in. This choice should be made with care: now that you have a point to make, plan your paper so that everything else supports this point. The rest should go! The best papers are often quite minimalistic: they drive their point home with essential ingredients only. Papers that contain tons of unrelated results are difficult to comprehend, because the reader is left wondering where to focus her attention. Clutter reduces clarity. Always konmari your paper! Keep what makes it happy and discard everything else.

Continuing with the film industry analogy, the process of going through your results and deciding what to keep resembles the process of editing of a Hollywood movie. After the movie has been shot, the director and the editor start working with an abundance of raw materials that are to be sculpted into the final product, the theatrical cut. The goal is to assemble the film from the shots and scenes that best support the storyline, cutting out footage that is not essential and that doesn’t have the emotional impact that the director desires.

Your paper is your theatrical cut. Use only the elements it needs, and leave out the rest.

Cutting out material and deciding not to use some of your results may feel difficult and painful – you spent a WEEK on that plot! But, believe me, it is for the best. If you want your work to have impact, it has to be read and understood, which is greatly hindered if there is too much unimportant or unrelated information to absorb. Clutter draws attention away from the point that you want to make, and leaves the reader exhausted.

Perhaps it is because of the pain caused by discarding perfectly-good-yet-unimportant results that most journals nowadays allow for an extended special five-hour-long director’s cut in the shape of a Supplementary Information document with an unrestricted page count. You can dump all those raw materials that didn’t make it to the theatrical release to the SI so that they can safely be forgotten and ignored by the rest of the world. But now your week spent making that plot means at least something.

But back to your cut – how to choose the results that are to be included and that support the key result?

Let us see how far we can push the film script analogy discussed earlier. A typical film script begins with the Setup phase where the characters and the setting are introduced, then moves on into the Confrontation phase where the characters are put in interesting trouble, and finally there is Resolution (epic fight in space followed by an exploding Death Star or similar). This may be followed by a brief Epilogue (with or without frolicking ewoks). If we divide our results into these four categories, Setup and Confrontation contain results that are needed for getting to the main result, for building up excitement and for leading the storyline to its climax. Resolution is the main conclusion that we discussed in the previous chapter. Epilogue shows what follows from the Resolution.

The Setup category contains plots and results that are required for the reader to make sense of the context, setting, your experiment, and/or your data (like, basic statistics, and so on). Schematic diagrams that visually explain the concepts that your paper works with also fall into this category; always include a schematic diagram or two!

The Confrontation phase brings the story closer to the final revelation that you aim to make; it highlights the open important question that you address. You can do this, for example, by showing empirical results that are surprising and cannot be explained by existing theories, and then providing an explanation as the Resolution of your storyline. You can also build up excitement by presenting a number of competing hypotheses or models, to be then shot down by your results (except for the one model that matches with your data and provides your Resolution). Or, you can begin by displaying some surprising system-level results or statistical observations and then home in on their detailed explanation in the Resolution phase.

The Resolution category should only contain your main result and key point; one to two figures.

The last category of results, the Epilogue, is more important than the last couple of minutes of a blockbuster film. These results are presented after the main result, and serve the purpose of highlighting its significance. One key technique is to think of some application or consequence of the main result and to illustrate this with, say, a figure that plays the role of an example rather than that of an important stand-alone result.

If you look at some research papers published in the glossy magazines (Nature, Science, and so forth), you’ll see that great many authors apply this technique: out of the four or so plots in those letter-format papers, the first is about Setup/Confrontation, the second is the key result (Resolution), and the rest are there for showing why the key result matters, or what it means (Epilogue). For the kinds of journals that us mere mortals publish in, these figure counts may be larger–the important thing is to decide on clear roles for your results and figures and use them accordingly when telling your story.

Next: how to use figures to tell your story.

Paper Writing for PhD Students, Part 3: The Importance of Focus

A keyboard

[This post continues my “self-help” series on how to write a scientific paper for PhD students; the previous episode can be found here. This series is an attempt to share some of the conceptual tools that I use with my students. Their point is to structure your thinking and focus your decision-making on a limited number of problems at a given time; having all options open at all times is an enemy of creativity.]

Scientific papers are stories, not just containers of information. The more focused and exciting the story, the more likely it is that it reaches someone. This is because that someone has to decide to invest their time in reading the paper, and as we all know, the world is full of papers, too many for any of us to read.

Thinking of papers as stories is something that doesn’t come naturally to most Ph.D. students or scientists. If we have been taught at all, we have been taught to write (boring) reports, certainly not to develop storylines, or to work with the kinds of higher-level conceptual elements that, say, journalists use.

Good writing starts with careful planning. I usually plan my storyline in three steps.

The first is defining the key point of the paper, the main conclusion that you want to tell the world. The second step is choosing the essential building blocks for the rest of the storyline, leaving out all results that are not necessary. The third step is taking these building blocks and arranging them into a condensed version of the storyline: the abstract of the paper. That’s right – I recommend writing the abstract before the rest of the paper. This is unconventional but it works.

Defining the focus of the paper – its key point and its main conclusion – is the most important step, as it lays the foundation for the rest.

In the best case, the key point is a single important result, but usually, things are slightly more complicated than that. In any case, you should be able to explain your point and main conclusion with one to three sentences. If you think that this is too little, consider these: the Earth rotates around the Sun, and not vice versa. Space-time is curved by mass. The salt of deoxyribose nucleic acid has a structure with two helical chains, suggesting a possible copying mechanism for genetic material. And so on. Clearly, a sharp focus doesn’t mean that the result is simplistic – to the contrary, there is usually a lot of depth behind results that can be described with a few words.

Choosing a key point that can be condensed into a few sentences doesn’t imply that your paper has to be narrow in scope. If your work is of an exploratory nature your key result might be that you have mapped out a problem area and your paper provides the map, or perhaps your main conclusion is a broad synthesis of several sub-results that make up the bulk of your paper. The most important thing is that you can make it clear to the reader what your paper is about.

If you can compress your message into a package that can be easily communicated, the higher the likelihood that it reaches its intended target, the reader. This is not limited to primary transmission – say, the reader encountering your abstract on the arXiv and deciding to read on – but secondary transmission is important too: getting the reader to share your paper with colleagues, online or face-to-face. Whatever the type of transmission, it works best if the thing being transmitted is compact and focused.

Communication is always difficult and all communication channels are noisy – a tight focus helps your message to make it through in one piece.

Next: Theatrical Cut, Or How To Konmari Your Paper

Thou Shalt Not Smooth!

This is a very short post for those dabbling in the dark arts of network neuroscience. Everyone else, read this or this, they’re probably more fun anyway.

Functional brain networks

[Figure from Eur. J. Neurosci, doi: 10.1111/ejn.13717]

Q: When building ROI-level functional brain networks from fMRI data, should I apply spatial smoothing to the voxel time series?

A: No you should not, what were you thinking? See above; it messes up your degrees and links non-uniformly, and in general has weird effects. In any case, you already average your voxel time series to get your ROIs, which is brutal enough. For more, see our recent (open-access) paper in the European Journal of Neuroscience, with @TuomasAlakorkko and @eglerean and @hpsaarimaki and Onerva Korhonen.

Clone Wars – What Happens When You Get A Splinter In Your Toe?

biohazard sign

For the last two years or so, I’ve been crunching some numbers on the genetics of T cells together with colleagues from the Dept. of Bacteriology and Immunology at the Haartman Institute, Helsinki. It has turned out that with the help of high-throughput sequencing and the resulting massive amounts of data, immunology is an enormous unexplored playground for complex-systems scientists; I’ve had plenty of fun and we’re currently writing up the results of this first stretch. There are all sorts of marvels out there, and yes, there be power laws too.  I’ll write a series of posts on the topic. To whet your appetite, here’s a small story:

Ever wondered what happens in your body when you get a splinter in your toe?

Here’s a summary. The splinter breaches your first line of defence – your skin – and intruders follow. Once they are in, your so-called innate immune system responds. This response has to be swift; bacteria multiply quickly. Many things happen: the chemicals of the so-called complement system start drilling holes into bacterial cell walls. Macrophages, big eater cells, devour any invaders they meet. They become increasingly vicious and release cytokines, chemicals that call other types of cells to arms. Cytokines also increase the permeability of your blood vessels: that’s why your toe will swell. Now neutrophils that circulate in your blood will exit and follow the scent of battle. Once at the front line, they release a cargo of toxic chemicals, killing invaders. When done, they die and become pus.

The battle rages on. New kinds of soldiers become involved. One class – dendrite cells – picks up some battle debris and quietly exits the front lines. Now things will escalate. Dendrite cells travel to the lymph nodes, where an enormous repertoire of T and B cells awaits. Each has a different type of receptor on their surface, waiting to be triggered. Dendrite cells keep on displaying their cargo – bits of dead bacteria – until a matching receptor is found. When this happens, the cell hosting the receptor begins to proliferate, producing a massive army of clones.

Next comes the decisive strike. The clone army enters battle, armed with homing devices targeted at the specific type of invader. B cells begin to sprout and release receptors, producing enormous amounts of antibodies that find the invaders, coat their surfaces, and mark them for destruction by macrophages and other killers. T cells enter the front line, directing the battle, releasing more cytokines, and making sure that all bactericidal cells are fully engaged in battle. All weapons of the immune system are now deployed: the invader is being hit from all directions.

With all likelihood, the invader will now yield. It cannot hold against the combined response of the innate and the adaptive system. The battle winds down. T-cells command the foot soldiers to disengage, macrophages clear the battlefield of wreckage, blood vessels no longer leak fluid. Neutrophils stop pouring out of the blood stream; they move on and look for signs of new trouble elsewhere. Pain, redness, and swelling will cease.

If the invader ever returns, it is dealt with swiftly, without you even noticing. This is because your body remembers the invader: some of the B and T cells that saw battle have become memory cells that can quickly mount an overwhelming defence.

But with many bacteria and viruses, evolution runs fast. Next time you meet them, they might have changed already; your body has won one battle but will be at war forever.

Coming up next: how your immune system does gradient-descent Monte Carlo with zillions of threads in parallel, starting from a massive repertoire of initial conditions.

(In the meantime, if you want a longer, detailed version of the above story, see “How the Immune System Works” by Lauren M. Sompayrac; it’s a textbook that even those of us who don’t have much biomedical background can follow).

 

 

Functional brain networks: the problem of node definition

Summary: Nodes in brain networks from fMRI are usually defined using ROI’s (Regions of Interest) so that each ROI node has a time series that is the average of the BOLD time series of the ROI’s voxels and links represent correlations between nodes. Here, we show that this averaging of voxel time series is problematic.

The human brain is a complex network of neurons. The problem is that there are about 10^12 of them with ~10^5 outgoing connections each; mapping out a network of this scale is not possible. Therefore, one needs to zoom out and look at the coarse-grained picture. This coarse-grained picture can be anatomical – a map of the large-scale wiring diagram between parts of the brain – or functional, indicating which parts of the brain tend to become active together under a given task.

But how should this coarse-graining be done in practice? How to define the nodes of a brain network –– what should brain nodes represent? In functional magnetic resonance imaging (fMRI), the highest level of detail is determined by the imaging technology. In a fMRI experiment, subjects are put inside a scanner that measures the dynamics of blood oxygenation in a 3D representation of the brain, divided into around 10,000 volume elements (voxels). Blood oxygenation is thought to correlate with the level of neural activity in the area. As each voxel contains about 5.5 million neurons, the network of voxels is significantly smaller than the network of neurons. However, it is still too large for many analysis tasks, and further coarse-graining is needed.

A typical way in the fMRI community is to group voxels into larger brain regions that are for historical reasons known as Regions of Interest (ROIs). This can be done in many ways, and there are many pre-defined maps (“brain atlases”) that define ROIs; these maps are based on anatomy, histology, or data-driven methods. It is common to use ROIs as the nodes of a brain functional network. The first step in constructing the brain network is to assign to each ROI a time series that is the average of the time series of its voxels measured in the imaging experiment. Then, to get the links, similarities between the ROI time series are calculated, usually with the Pearson correlation coefficient. The correlation between the two ROIs becomes their link weight. Often, only the strongest correlations are retained, and weak links are pruned from the network.

If the ROI approach is to work, the ROIs should be functionally homogeneous: their underlying voxels should behave approximately similarly. Otherwise, it is not clear what the brain network represents. Because this assumption hasn’t really been tested properly and because it is fundamentally important, we recently set out to explore whether it really holds.

We used resting-state data – data recorded with subjects who are just resting in the scanner, instructed to do nothing – to construct functional ROI-level networks based on some available atlases. We defined a measure of ROI consistency that has a value of one if all the voxels that make up the ROI have identical time series (making the ROI functionally homogeneous, which is good), and a value of zero if the voxels do not correlate at all (making that ROI a bad idea, in general).

Distribution of consistency for ROIs as brain network nodes
[Figure from our paper in Network Neuroscience]

We found that consistency varied broadly between ROIs. While a few ROIs were quite consistent (values around 0.6), many were not (values around 0.2).  There were many low-consistency ROIs in three commonly used brain atlases.

From the viewpoint of network analysis, the existence of many low-consistency ROIs is a bit alarming.  We also observed strong links between low-consistency ROIs – how should this be interpreted? These links may be an artefact, as they disappear if we look at the voxel-level signals. This means that the source of the problem is probably the averaging of voxel signals into ROI time series. While this averaging can reduce noise, it can also remove the signal: at one extreme, if one subpopulation of voxels goes up while another goes down, the average signal is flat. More generally, if a ROI consists of many functionally different subareas, their average signal is not necessarily representative of anything.

In conclusion, we would recommend being careful with functional brain networks constructed using ROIs; at least, it would be good to go back to the voxel-level data to verify that the obtained results are indeed meaningful.

For details, see our recent paper in Network Neuroscience.

This post was co-written by Onerva Korhonen, Enrico Glerean & Jari Saramäki.

[PS: The definition of brain network nodes is not the only complicated issue in the study of functional brain networks. Even before one has to worry about node selection, a possible distortion has already taken place: preprocessing of the measurement data. We’ll continue this story soon.]

How to write a great abstract

[This post continues my “self-help” series on how to write a scientific paper for PhD students; the first post can be found here]

The first thing that you should do when starting a new research paper is to write the abstract. I also recommend spending a lot of time writing it. This will pay off later.

Writing the abstract first may seem unconventional, but it makes sense. This is because the abstract is the storyline of the paper in miniature form. It determines the rest. Once you have composed your abstract, you have decided on your story, and the rest of the paper is much easier to write.

So how do you write a good abstract? One common mistake is to view the abstract as an information container, whose only aim is to let the reader know what the author has done. An abstract written in this way reads like “we did X and the result was Y. Then we did Z and …” It becomes a boring list of results. Two important things are missing: context and excitement!

Think of a Hollywood movie. It begins with the setup phase, where the setting and the characters are introduced. You can only follow the story if you understand the setting and know the characters (context), and you will only care about the story if you care about the characters (excitement). The same applies to any research paper and its abstract: the reader must understand the context and care enough about the problem to read on and to find out how the problem was solved.

After the setup, a typical film script continues to the confrontation phase. There is trouble; there an issue that the characters have to solve. The resolution of the confrontation marks the high point of excitement in the story. After outworking the story, some brief epilogue may follow, providing closure.

Great papers and great abstracts follow a similar arc: from setup to confrontation and from resolution to closure. The storyline can be seen as hourglass-shaped: presenting the broad setting, introducing a more narrow problem and its solution, and returning to the broader picture again. It is not a coincidence that this is exactly how every Nature abstract reads.

How to write a great abstract: the hourglass shape

The script that every Nature abstract has to follow, sentence by sentence, begins with a few sentences on general context and the broader topic. Then, the abstract narrows down to more specific context (again a few sentences), before funnelling to its narrowest point: the exact research question addressed by the paper. This has to be followed by the solution of the problem: the key result. Then the abstract broadens again, first addressing the implications of the result to the paper’s field of science, and then discussing the impact beyond that particular field. Setup, confrontation, resolution, closure.

Even if you are not writing a Nature paper (and you probably aren’t), the above is still a great recipe for a successful abstract, and my suggestion is to always follow its spirit.

Of course, depending on your field and the chosen journal, the breadth of the top and the bottom of the hourglass may need to be adjusted. Instead of a context where your result contributes to solving mankind’s most pressing problems, your playing field may be just your particular field of science or its subfield. For a specialist journal, you don’t need to begin your abstract with a sentence on the importance of your field – the readers already know it. Nevertheless, it pays off to consider the broadest context you can honestly think of. Don’t exaggerate, but try to take a broader perspective. Why is your research question important –– why does it matter? The answer to this question is your context; it should directly translate to the first and last sentences of your abstract.

Next in the series: The Importance of Focus