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.
I gave a keynote talk at the Complex Networks 2019 conference in Lisbon—here are the slides, if you are interested.
If you are interested in temporal networks in general, here are some pointers:
We are looking for a postdoc (2 years) to work on the intersection of complex systems/networks, transport engineering, human mobility, the science of cities, and data science.
This position is related to ongoing collaboration between my group and prof. Milos Mladenovic’s (Twitter: milosplanner) transport engineering group (both at Aalto University, Helsinki area, Finland).
We want to bridge the gap between network science and transport engineering, including city planning and public transport network planning; for our earlier joint works, see, e.g.,
What we can offer:
What we expect:
The call is open until 20th of December; the applications will be processed and (Skype) interviews with shortlisted candidates will be conducted in January 2020.
Please email a single combined PDF document containing 1) a cover letter, 2) your CV and publication list, 3) contact details for two references, to jari.saramaki@aalto.fi, with “Mobility postdoc” in the topic.
[Update: the book is now available, go get it!]
Rejoice! There is a new book on temporal networks coming out soon — Temporal Network Theory, edited by Petter Holme & myself. A lot has happened in temporal-network research since our first edited volume (Temporal Networks, 2013); in this new volume, we wanted to focus on the theoretical side of things & invited contributions from many pioneering scientists & groups.
As a teaser trailer, here is a list of the book’s chapters:
[This post has been co-written with sci comms coordinator Anu Haapala].
If you happen to come across research results that are worth sharing with the general public through online publications or traditional newspapers, you’ll usually need to approach them with a press release. As the inner workings of press releases are notoriously difficult for scientists to grasp (what, you have to present things in the wrong order??) and as no-one actually teaches scientists how to write them, I have teamed up with our specialist Anu to provide some help.
Of course, if you live in fairyland, your university has a scientific communications team that simply reads your paper, understands its content and implications better than you, and compresses all this into a readable, exciting press release that instantly makes you a media superstar. But if you live in the same world as the rest of us, you might actually have to work a bit with said comms team as your results might not be as comprehensible to non-specialists as you think. Also, it might help if you’d understand what it is that the comms people are trying to achieve — what is their output? And, more often than not, you might, unfortunately, even need to write the press release yourself because there are not enough comms people around… so how should you do it?
There are two key things to understand here: 1) the intended audience and 2) the structure of the press release.
Let’s begin with the audience. In fact, a press release has two audiences: the first is the journalists who act as gatekeepers, and the second is their audience, the general public or its subsets such as tech-savvy readers or wannabe astronomers or similar. The gatekeeper role of the journalists comes from their need to serve their own audience: they only publish your story if they think it is of interest to their audience.
This has direct consequences on the form and structure of a scientific press release. First, the press release has to be written in a way that journalists are used to seeing and they can make best use of it (which is very different from scientific writing!), and second, its language and content should be comprehensible to laypersons.
The way journalists would write any news story – and the way you should write your press release too – is to put the most important thing first, followed by other things in decreasing order of importance. This inverted-pyramid structure has historical reasons: there is limited space in a newspaper, and shortening a story is easier if the editor can just chop off a few last paragraphs without doing much damage to the central point. At the same time, this structure makes the story more readable: the readers do not need to wonder what the point of the story is if this point is the first thing that they encounter. In other words, they see immediately what all the fuss is about and whether they want to read more about it.
The problem is that we scientists are really not used to writing this way: it almost physically hurts us to give away the main result immediately, in the very first sentence, without lengthy motivation or background or methods or anything to prep the reader with. But no pain, no gain: this is what you should do. Always begin with the main result, formulated in plain language that even your grandmother who never went to high school can understand. This is difficult, we know, so coming up with the proper words might take quite some time. But it’s worth it.
After introducing the main result, you should tell why the results matter and what follows from them, again in plain language and using only words that your audience can understand. What is now possible? What new and wonderful things can now be achieved? How has your result made the world a better place? And after this, you can continue to add in paragraphs in decreasing order of importance (to your target audience!). These paragraphs can add further details to your result, talk about the setting where it was obtained (your research group, an international collaboration…), sketch some future directions, and so on. It is probably safe to leave methods last, unless they contain something that would be especially interesting to your target audience (of non-scientists, remember!).
Journalists are used to killing their darlings, though, and you should, too. This means that you should critically evaluate each paragraph you write. If any of them seems unnecessary or trivial to anyone outside your own research community, don’t hesitate to press the delete button. News desks receive dozens of press releases each day, which means that journalists are ready to give their precious time only to a selected few. The shorter and snappier your press release is, the more likely journalists are to read your release through and publish it as such.
Leaving blanks in the right places can even encourage them to grab their phones and call you with follow-up questions. For this very reason, always remember to include your phone number and email address at the end of the press release. Journalists want to contact you, the specialist, directly and right now instead of trying to catch you through your university comms for days. (Believe us, they can hardly imagine anything more frustrating than an interviewee who is playing hide and seek!) So when you send out a press release, make sure you do it at a time when you are actually available to pick up your phone and discuss your research, even if just for five minutes.
What should not be the first thing that you leave out of your press release, however, are quotes. Good press releases contain an element of human interest in the form of quotes, things that you or a colleague of yours say about your results or research. “We had never thought about X until we figured out that Y”, says N.N., a postdoctoral scientist. “Then, the solution practically presented itself, and we knew how to do Z.” Quotes are an easy way to build bridges from one topic to another in the storyline of your press release: it might be even easier to use a quote than to write something up as a full paragraph (see the example above). In addition, humans (your readers) are always interested in other humans, so quotes make your press release more appealing.
Please remember that your press release is NOT a scientific publication: it does not need to tell everything (like the details of your methods). That’s what your original paper is for. You should leave out things that are too difficult (or too boring) for the intended audience. You may need to invent analogies or to simplify your results a lot: as long as you are truthful, this is perfectly fine! The only thing to avoid is over-generalization or exaggerating your work (despite some sci comms folks and some journalists craving for sexy headlines): make everything simpler, but keep it real. Also, send your release out in a format that is easy to copy, paste, and edit. Most comms teams use centralized press release services, but if you cannot access one, send out a simple email message! This is much better than hiding your release in an attachment: here, creating a nice-looking PDF will only slow you down.
Finally, timing counts too. Remember your first target audience: the gatekeeper journalists. Journalists want NEWS, they want things that happen right now, and they want news before their competitors! This means that you should send out your press release so that as soon as the result is out (some journals have press embargoes), they can run their story. A week or a month later won’t do; it’s very hard to make a journalist interested in a result that was published weeks ago. So as soon as you know your publication date, contact your sci comms people and start preparing your press release.
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…
Jari Saramäki 