The young researchers in Complex Systems Society (yrCSS) invited me to talk on scientific writing at Palma de Mallorca on October 15, 2022. It was really great to speak to an active & interested audience!
Here are the slides — I hope you find them helpful!
There is a video recording of the whole talk as well, available on YouTube. Go check it out.
In his recent post, Petter Holme presents an entertaining inner dialogue about whether one should market one’s scientific output or not. Much of this centers around the concept of stories — and the discussion on whether we should publish papers that have storylike narratives or just plain data has been going on for a while.
Being an advocate of papers-as-stories, let me add another point of view to the mix.
I feel that there are two dimensions here. The first one is the axis from facts to fiction, and being scientists, we all know where we should place ourselves here. The second dimension is about pure data versus understanding/insight, and it is this dimension that in my view necessitates some storytelling.
Let me explain my reasoning by starting from pure data. Suppose I have carried out an experiment/done some simulations/analyzed a bunch of data I found on the Internet. Now, if I wanted my output to be pure data, I could just release the numbers as tables or graphs or whatever, and maybe an explanation on how the experiments or simulations were carried out. Pure data — no story.
However, my pure data would probably not make sense to many people, if any. To take a step in the direction of meaning, I should at least explain what the research question is that the experiment/simulations/analysis project was designed to answer. I might also feel compelled to tell how the data answer this question, i.e., to give the numbers some meaning.
Notice the elements of a story sneaking in? There is a question, there is an answer.
But even after these additions, only an expert reader would be able to see the meaning in what I have done. For anyone else, more would be needed — why should this question be asked? What is the context for the question? And why should one care about the results?
Add these elements, and we have arrived at the typical structure of a scientific paper that begins with an introduction and ends with a discussion. We have also strayed pretty far from pure data, and are now firmly in the realm of stories. First, we introduce the world and the characters that inhabit it, then we create tension with an open question, and release this tension with an answer.
But such stories of science are not works of fiction; they are told with facts. This, to me, is why papers should be stories — stories provide clarity, understanding, and meaning. They help the reader to connect the dots. Of course, one can and should release pure data too: numbers, results, code, everything. But these only get their meaning through stories.
I was recently interviewed by Daniel Shea for his podcast Scholarly Communications — you can listen to the interview here: https://newbooksnetwork.com/how-to-write-a-scientific-paper
We discussed my writing book and writing in general. This was a very enjoyable discussion & Daniel had plenty of good points and new perspectives that I could immediately agree with — do have a listen, highly recommended!
![How to Write and Excellent Master's Thesis [slideset cover]](https://jarisaramaki.files.wordpress.com/2020/03/mastersthesis-1.jpg)
I was asked to give a talk on how to write a Master’s Thesis at our department’s Comms & Coffee event this morning; here are the slides.
This talk is an adapted version of my paper-writing system (no, I haven’t written a book about writing Master’s theses, at least not yet). You’ll notice that companies & businesses are mentioned—Aalto is a technical university, so many MSc theses are in fact done as interns/trainees in companies.
I hope the slides are useful. Feel free to share with your students!
Are you looking for a master’s programme? Admission is now open for our Life Science Technologies master’s programme at Aalto University, Finland, Europe; there is a Complex Systems major within the program and I am the responsible professor. And I am looking for talented and motivated students from all parts of the world!
What’s in the major? A lot of interesting and cool things: network science, data science, machine learning, nonlinear dynamics, to mention a few! Here’s why networks are the thing. And if you want to know more about what complex systems are, just have a look at previous posts in this blog, e.g. on mobile-telephone calls, ants, and the immune system.
Here is a complete list of courses in the complex systems major for this winter (only minor changes coming in 2020).
There is a lot of freedom in designing your own curriculum: there are many courses to choose from, including courses by other Life Science Technologies majors. This makes it possible to mix and match: want a combination of machine learning and complex networks? Check. Want to be a network neuroscientist? Check. Want to get a broad training in data science? Check.
Note: even though the programme is called Life Science Technologies, you can almost completely avoid anything that begins with “bio” if you so wish. As an example, I have students who focus on social networks, computational social science, or public transport networks, and who I believe haven’t taken any biomedical courses. But, if bio is your thing instead, there are plenty of opportunities here too!
One more thing: the doctoral track. If you are talented and your grades are excellent, you can apply to the doctoral track where your final target is not the master’s degree but a PhD; your studies are tailored towards that goal and you’ll get to spend time as intern in our research groups, with the aim of publishing the first journal article(s) of your thesis already before you get the master’s degree.
So, what are you waiting for? Apply here! The deadline is on Jan 3rd, 2020.
Last winter, on a speaking trip to Norrköping, someone asked me to write about skills (and meta-skills) that scientists and PhD students need, beyond writing papers. Turns out that this is a lot more difficult than writing about writing, where the end product—a scientific paper—is something tangible and amenable to analysis: how do great introductions look like? How do the greatest writers finish their papers? It is much more difficult to write, say, about learning to be creative, which is what I shall try to do here. But what would be more important for aspiring scientists than creativity?
Science is all about creativity: coming up with the right questions, developing clever methods to answer those questions, and connecting the answers in imaginative ways to learn something greater. But we rarely talk about creativity as a skill—often, people view it as something that you either have or don’t have, just like an ear for music or an eye for design. And just like with music and design, this view is wrong: everything can be learned. So how do you learn to be creative?
Before attempting to answer this question, let’s take the bull by the horns and ask what creativity is. If by creativity we mean the ability to bring forth ideas that are entirely new, we are immediately hit by a very difficult, philosophical question: where do new ideas come from? At least to us (recovering ex-) physicists, the emergence of something that wasn’t there before is kind of strange: aren’t there conservation laws that forbid this kind of travesty from happening? What is it that gives birth to new information (because that is what happens when a new idea emerges, whether it is a question or an answer)?
If physics doesn’t provide us with answers, let’s drop it for a while and put on the hat of a biologist: in the realm of living things, don’t new things gradually emerge, driven by the slow Darwinian evolution? Notice the word “gradually”—biological evolution is slow tinkering, a process where existing forms and shapes and organs are gradually transformed into something new, of dinosaurs developing feathers that eventually help some of them to learn to fly, of finches’ beak shapes adapting to their habitats. So in biological evolution, everything that is “new” is built on top of a lot of something old, and this happens slowly: a slow expansion into the adjacent possible, if you’ve read your Kauffman.
Are there some other natural processes where new forms emerge more rapidly? The human immune system provides a great example. Somewhat surprisingly, not all our cells carry the same sets of genes: the T and B cells of our immune system, our ultimate smart weapons against viruses and other invaders, display an enormous diversity of different receptors that recognise those invaders. This diversity results from those cells carrying some randomised (but not too randomised) parts of our genome. The precursor cells that eventually become T and B cells have strings of different modules in their genetic code, and in the process of randomisation, some of those modules are randomly picked and joined together (the rest are discarded). Then, a bit of extra randomness (extra letters, deleted letters, and so on) is added to their junction. So to arrive at new kinds of receptors, our bodies randomly merge things that are known to work (those receptor modules) and then add some noise on top. Again, “new” equals “old, but with added something.”
Let’s now return back to creativity, in the context of science or otherwise. The above examples point out that the old rhyme—“something old, something new, something borrowed, something blue”—is scientifically highly accurate, except for the blue bit perhaps. In other words, the things that we think are new are in fact modifications and clever combinations of old things, with perhaps some small amount of additional randomness. Ideas do not live in a vacuum, they emerge because of other ideas.
Therefore, creativity is the ability to merge existing ideas in new ways (while possibly adding a magic ingredient on top).
This brings us to a fairly simple recipe for feeding one’s creativity: collect lots of things that can be combined/transmogrified into something new, and then just combine them! In other words, first, feed your head with lots of information—and not just any information, but preferably pieces of information that haven’t yet been combined.
To maximise the chance of something entirely new emerging out of this process, your input information—the stuff that you feed your head with—should be diverse enough. There are, however, different possibilities: on the one hand, if you know everything that there is to know about your field, you can probably see where the holes are and combine bits of your knowledge in order to fill them. On the other hand, if you know enough about a lot of fields, you might be able to spot connections between them (think of, say, network neuroscience, applying network theory to problems of neuroscience). There are different styles here, but even if you choose to go deep instead of wide, do keep the diversity of input information in mind: just for fun, learn some mathematical techniques that people do not (yet) use in your field! You never know, those might turn out to be useful later.
To be continued…
“Any word you have to hunt for in a thesaurus is the wrong word. There are no exceptions to this rule.” ― Stephen King
This post is a chapter from the book “How to Write a Scientific Paper.”
After you have written your abstract, the next task is to consider the title of your paper. If the abstract is a compressed version of your storyline, the title of your paper is even more so. Titles are hard—it is often surprisingly difficult to come up with a short, informative, and catchy title. For me, this has at times felt like the hardest part of writing a paper.
The title of the paper serves a dual purpose: it delivers information by telling readers what your paper is about, and it serves as a marketing tool that makes others want to read your paper. Unfortunately, unlike the abstract, there is no general-purpose formula to follow when thinking of a title. There are, however, some points that you should consider.
The title has to be in perfect sync with the abstract—they have to tell the same story. Make sure that your title and abstract use the same words and concepts. Also, make sure that everything that is mentioned in the title is discussed in the abstract.
Use words that everyone in your target audience can understand. Avoid subfield-specific jargon. Simply does it! The paper’s title should only contain concepts that can be understood on their own, without any explanation. While there is some room in the abstract for explaining one or two important concepts in brief, there is no such luxury in the title: the reader should already be familiar with every word used in it.
The title should be focused and clear. If it is possible to give away the main result in the title, do so. Avoid vague titles, such as “Investigating Problem X with Method Y”. Instead, go for something more concrete: “Investigating Problem X with Method Y Reveals Z.”
A small request: please never, ever use a title of the “Towards Understanding Problem X” variety. Just don’t do it. Pretty please. If your research is worth publishing, you have arrived somewhere. Just be confident and tell the reader where this is, instead of telling them where you would rather have gone! It is OK to say something about the bigger picture in the title, as long as your key point plays a leading role. But to keep your title concise, it may be better to describe long-term goals elsewhere in the paper.
It helps if the title is catchy as well as informative. But do not exaggerate—consider how your title will look 10 years from now. Will it stand the test of time? If the title is too gimmicky or contains a joke that becomes stale after you’ve heard it a few times, it won’t. You should also avoid jargon and buzzwords that may go out of fashion before the paper gets published.
Consider search engines and online search. Your paper needs to be found if it is to be read, so the title should contain the right keywords or search terms. As a network scientist, I almost always include the word “network” in my paper titles, even if this makes the title longer or if other network scientists would understand the title perfectly well without networks being explicitly mentioned. Without the word “network”, they would not necessarily find my paper when they hunt online for new reading material.
Keep your title short. Research has shown that shorter titles attract more citations—see Letchford et al., R. Soc. Open Sci. 2(8):150266 (2015). This should not come as a big surprise: long and cluttered titles are not as contagious as simple, focused ones. If the title is convoluted and hard to grasp, then the paper probably is too.
Sometimes there are field-specific conventions that you should be familiar with. In some biomedical fields, for example, the paper’s title often expresses just the key result—“Transcription Factor X is Involved in Process Y”—and the titles can be fairly long. In some areas of physics and computer science, shorter and less informative titles are the norm. Have a look at other papers in your field, and try to imitate their best titles.
If you get stuck at this point and find it hard to decide on the title, it might be easier to initially lower your bar a bit. Just come up with some candidate titles that do not have to be perfect. Then ask your colleagues—your fellow PhD students, your supervisor, anyone—to have a look at the list and to pick the most promising candidates for refinement and final polishing.
Get the ebook from your favourite digital store! Paperbacks are available too (Amazon only!)
One more cheatsheet for scientific writing: this time the topic is reviewers’ comments—what to do when your receive the dreaded email from the editor with reviews of your manuscript?
For a hi-res PDF, click here!
Here is the second cheatsheet on how to revise the first draft of your scientific paper, focusing on sentences and words. (Here is the first one if you missed it). Enjoy!
For a hi-res PDF, please click here!
Want more? In my book How to Write a Scientific Paper you’ll learn a systematic approach that makes it easier and faster to turn your hard-won results into great papers. Or check out the series of posts that starts here.
Hi all,
as I hinted at earlier, I’ll be releasing a couple of cheatsheets on scientific writing based on the writing series/book, mostly because I love to play with Adobe Illustra^H^H^H because I’m sure you’ll find them useful 🙂
Here’s the first one; click here for a high-res PDF version.
Want more? Get my little writing guide where you’ll learn a systematic approach to writing papers that makes the whole thing easier, faster, and less painful: How to Write a Scientific Paper!
Jari Saramäki 