I’m a model and you know what I mean

Wait, what do I mean?

It’s perhaps a bit of a stereotype, but scientists don’t always know how to talk to non-scientists. To be completely honest, scientists don’t always know how to talk to other scientists! This can partially be attributed to the use of jargon – lingo that is used by a specific group of people that is difficult for people outside that group to understand.

Let me give an example.

If you look up the word “model”, Mirriam-Webster gives 14 different definitions; that’s already cause for misunderstandings without any science coming in!

Meaning number one: “I’m too sexy for my shirt!”

model noun [ mod·​el \ ˈmä-dᵊl ]
9 : one who is employed to display clothes or other merchandise //has appeared as a model in ads for swimsuits

In every-day, fashion lingo, a model is someone who shows off clothes or other merchandise on billboards, in magazines, on the catwalk, in tv ads, etc.

This version of the word model is probably not what any scientist means when they are talking about their model. Except maybe if they’re bragging about that “model they dated back in college,” but we were all dating models then, weren’t we?

dating a model meme: Leonardo DiCaprio saying: I'm dating a model now, she's a simplified representation of reality
I don’t believe you, Leo

Meaning number two: To see what cannot be seen

model noun [ mod·​el \ ˈmä-dᵊl ]
11: a description or analogy used to help visualize something (such as an atom) that cannot be directly observed

Some things we can’t really take a picture of. Or even if we can, it’s difficult to gather any meaningful information from the picture. A model of that thing can help; such as a model of an atom, or our solar system, or the universe. Such a model is usually simplified to allow a clearer understanding, and as a result, it is never 100% accurate.

For example, the model of the atom has gone through many iterations and has become more representative of the physical reality (in so far as we understand it). That doesn’t mean that older models are wrong, they’re often just insufficient. For many purposes, the Bohr model is enough to explain the formation of bonds and many aspects of physics and chemistry even though the quantum model is more details, and is needed to describe more advanced principles (like the types of bonds).

Models of the atom over time, a comic by xkcd
Hovertext: J.J. Thompson won a Nobel Prize for his work in electricity in gases, but was unfairly passed over for his “An atom is plum pudding, and plum pudding is MADE of atoms! Duuuuude.” theory.
From xkcd

Meaning number three: To the computer!

model noun [ mod·​el \ ˈmä-dᵊl ]
12: a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs
alsoa computer simulation based on such a system (e.g. climate models

But not the type (i.e. model) of your computer. Wait. This is confusing.

The model of your computer might be 80NW. But a computer model – or a computer simulation – is a mathematical representation of a system and nowadays those mathematical representations are often running within a computer (because they can do math faster). Basically, a computer model/simulation is a program that is used to predict (hopefully) useful information based on a number of equations (or from learning data in the case of machine learning) that have been predefined.

In my PhD, I created a computer model for how ultrasound interacts with tissue. I told the program the properties of the ultrasound wave (its frequency, its shape, etc.); the properties of the tissue (size and shape, but also how stiff the tissue is); and the boundary conditions (how big the experiment was). After letting it run for some time, it would give me information back that I could use to understand this interaction better and compare it with results from physical experiments.

Graphical output of modelling ultrasound interacting with a penguin
Here is the incredibly useful test model I ran of an ultrasound wave (coming in from the left) interacting with a penguin. Image from PZFlex.

Computer models are very useful. Sometimes we would have to run experiments that are not possible to do physically, due to lack of resources or time or any other reason. Running a computer model is relatively cheap. In other cases, we are trying to make predictions on what will happen in the future, trying to do experiments on the unknown. An example of that is climate models.

Meaning number four:

model noun [ mod·​el \ ˈmä-dᵊl ]
14: ANIMAL MODEL an animal sufficiently like humans in its anatomy, physiology, or response to a pathogen to be used in medical research in order to obtain results that can be extrapolated to human medicine

also a pathological or physiological condition that occurs in such an animal and is similar to one occurring in humans

We are complex organisms, with a bunch of different types of organs and different types of cells that have a bunch of different processes going on at a bunch of different times. Sometimes, researchers can use cell lines – these are cells that have been isolated (often decades ago) and immortalized (they can be grown in petridishes and cultured for quite some time in said petridishes) to study biological processes and the effects of potential new drugs. But these isolated cells never give the whole picture (because they are so isolated), and sometimes animal models are needed for the next phase of genetic studies, cancer research, or drug development.

So unlike what I picture in my mind when I hear “animal model”, this does not mean mice in the bikini special. Rather, certain animals have traits that mimic a human condition or disease in such a way that research is meaningful. Whether it is ethical or not – that is a whole other discussing but just let me say that there are a lot of regulations and the principles of the 3Rs (Replacement, Reduction, and Refinement) are enforced in any proper lab conducting experiments using animal models.

The other note to make is that animal models don’t always give us the full information either. Again, it’s a model, An approximation. But since human experimentation is – well – inhumane, that’s often the only way to study genes and test drugs in a working-body-context.

A note on scientific theories

I don’t remember where I read this but: science theories are models for how the world works. In other words, like any model, they are not perfect! But they are a great way to try and understand the world better with our fairly limited brain capacity*. The fact that they are not perfect is actually really exciting: there is always more to discover, more to learn, more to understand!

In any case, if anyone tells you that they’re a model, if you know what they mean, you might want to ask them to specify… Just to avoid confusion.

*If you find this offensive, remember I’m mostly offending myself.

Definitions are taken directly from Mirriam-Webster dictionary.


Inking Science

A few months ago, my friend Vale asked me to collaborate with her on a project. I remember it going something along the lines of:*

Vale: “So, I’m working on this project and was wondering if you wanted to be part of it.”

Me: “Yeah, of course.”

Vale: …

Me: “Wait, what is the project?”

Say “yes” and ask questions later

Though probably not valid for every situation, I knew that in this case, I would be fine to say yes before knowing what I’d said yes to. If you’ve read any of my other stuff, you know that I’ve done various “scicomm”** projects like developing a “Build a LEGO-microscope” workshop and organizing a lecture series called “The Science of SciFi”. These were both in collaboration with Vale (and occasionally other people). She’s also the one who got me into Bright Club!

It seems that we work well together. And working together on a new project (without even knowing what it was), sounded like a lot of fun.


By now, I (obviously) know what the project is. It all started with #inktober, an art challenge that challenges illustrators to draw something using the medium of ink every day for a whole month (can you guess which?). Vale took up that challenge, and made it even more of a challenge by deciding to bundle her illustrations in a book.

Every drawing is based on a scientist*** that she considers a personal inspiration and is linked to a word from the prompt list. She’d post the result with a short explanation of why she chose that scientist for that prompt. Sometimes they were pretty obvious (at least to me, of course “stretch” is about D’Arcy Thompson!), some rather funny.

 inktober prompt list 2018
Official #inktober prompt list.

And then I come in.

Inspired by her drawing, I write a short text to go along with it. Sometimes it’s an anecdote. Sometimes it’s a quote. Sometimes it’s a short story about the scientist’s life. I try to make it as informative, engaging, unique and fun as I can.

… and then we have a book

Well, almost. We have the drawings. And we have the stories. And now we have a Kickstarter campaign to actually turn it into a book!

What will the book look like: 31 fun facts and stories and 31 full page illustrations
Sketch of what the book is going to look like.

It’s kind of awkward for me to sit here and write about a book I’m involved in, trying to get it made, aka trying to get the campaign funded. Like really, really awkward. So I’ll only do it once****:

Every little helps. Pledging helps, obviously, but spreading the word does too. If you like science, engineering, and math; and if you like amazing art; and if you like stories (and if maybe you also like us)… please share our project and help us make this book a reality!

Both Vale and I have found inspiration in these scientists, and we have found inspiration working on this book together. Hopefully, it will inspire you too.

*end of sappy book promo – I’ll be back next week with the usual science, nerdiness and hopefully some “Eureka!”s*

Inking Science book cover image
Just to remind you ;), you can find the Kickstarter page here:

*Severely paraphrasing. This was months ago. I might have also dreamt it but on the other hand, this project is happening so I guess that means the conversation happened too.

** or “science communication”, which is the umbrella term I use for STEM-related outreach, workshops, talks, and other similar activities.

*** in the broad sense of the word. They could be mathematicians, or engineers, or inventors. Creative STEM-people if you will.

**** on this blog, to be clear. My other social media channels will be swamped! Like, I actually really care about this project and am super excited and want to see it happen!

All of the art work shown in this post is by Valentina, and within the #inkingscience project.


What’s your brand?

Last Monday, I chaired a panel titled “Branding yourself – How creating a brand for yourself can increase your visibility, improve your communication skills, and help you navigate social media” at the Marie Curie Alumni Association Annual Conference. You can watch the full session here, where you can see the lively with panelists Martijn Peters, Nehama Lewis, and Matt Murtha.

You can also read the report from the session on the MCAA Medium Blog.

However, I wanted to take the opportunity of having a blog to outline some thoughts I had before, during and after the session. Let’s call this An incomplete Guide to Branding Yourself  – to be read with a level of skepticism because I am not an expert in this field whatsoever. Also, I rarely take myself seriously, and neither should you.

Personal brand?

If that would actually be my personal brand, the options are numerous. I’m notoriously clumsy and known to trip over nothing on a regular basis. I once tried kicking a football with both feet simultaneously, landed on the ball and fell backward, resulting in a broken arm. I once fell flat on my face while rushing to catch a bus I didn’t really need to catch. More recently (aka, last Tuesday) I sat in front of a bench instead of on it. But that said, I would prefer clumsy not to be my personal brand.

Though – come to think of it – I often use “tall, clumsy and nerdy – not necessarily in that order” when asked for a bio…

Let’s take a few steps back: what is a brand?

Basically, in a marketing context, a brand is what the customer/user/… thinks of a product or a company. It’s everything that is associated with that product (or company). This can mean a recognizable logo or slogan, but also an image of being a “green” company, or a “family friendly” company, or a “quirky” company. Remember Wendy’s witty responses on twitter? That’s all part of the branding.

Which brings me to the following point: What do people typically associate with “science”? A simple google image search doesn’t really look too promising:

Screen shot with the first few image results for "scientists" clip art
95% White. 77% White Dude. 32% Old White Dude. We’re doing great.

“We need to change how the public thinks of science and scientists. We need to change the first thing people think of when they think of science. And the best way to do it? Be the brand.” *

Fair enough, but how do I “be the brand”?

Similar to a “brand”, a “personal brand” is everything that other people might associate with you. Some people have very clear personal brands, just think of famous people like Oprah, or Bill Nye (I hear you automatically thinking: The Science Guy). But you don’t have to become famous to create a personal brand, and becoming famous should not be the goal of creating a personal brand. Creating a personal brand is useful for a number of reasons:

  • Thinking about how you want to brand yourself can help you figure out what makes you unique. You can create a vision of how you want people to perceive you and what your ambitions are in terms of career, or in terms of life in general for that matter. And for personal development, having a goal is always useful,
  • Creating a more visible internet presence helps your visibility. Having a personal brand (or something people “remember you by”) is incredibly useful for networking and landing your dream job.
  • As I said before, you can help market science (or STEM, or whatever field you are in). A powerful way to combat stereotypes is to show the rest of the world how diverse the people in your field are, and you can play your part by being the brand for your field.

With regards to actually creating a personal brand, internet presence and social media are probably the most powerful tools, whether you like it or not. My main tip is to check what turns up on the first page of google when you type in your name. Are you happy with what shows up? If not, use your internet superpowers to change your google presence (or more realistically, clean up Google search results for your name).

If you are okay with things like Twitter or Instagram or blogging, that’s a powerful method to control your brand, i.e. how you are perceived by people that might be looking for you on the interwebs. A general rule for social media is to stay authentic. It’s easy to spot people pretending to be something they’re not. That said, it’s okay to adhere a little bit to the “Fake it till you make it rule” in the sense that you can be who you aspire to be. For example, I’m not a professional science communicator, but it’s what I aspire to be. So I use it on social media, and on my business cards, etc.

Okay, this introduction was very incomplete

— I hear you thinking. And you’d be right. I highly recommend you go check out the video of the panel discussion because it was a very lively – and mildly entertaining – session (if I may say so myself) and while we might not have come up with absolute answers, we discussed topics such as authenticity, time management, and science communication in general.

Photo from during the panel
Look at me professionally being professional and all.

And to quote a slogan from a famous brand: Just do it!
(also typically associated with Shia LaBeouf, for other reasons)


* Background about branding and quote from https://www.forbes.com/sites/paulmsutter/2019/02/10/turning-science-into-a-brand-is-a-good-thing

An example of how to create your personal brand (others available upon Google search, the internet is a marvelous place, people!): https://www.quicksprout.com/the-complete-guide-to-building-your-personal-brand/


Nap Time

I have a track record of falling asleep in inconvenient locations. Basically, if I’m sat down and not actively doing a physical or mental activity, I will doze off.

I fall asleep in the car – fortunately only when in the passenger seat. I fall asleep on the bus and on the train – often resulting in neck pain for the following days. I fall asleep during short plane trips, though not really during long ones – apparently trying to actually fall asleep counts as one of those mental activities that keep me awake.

A snap my “friends” once took of me dozing on a bus. I’m just glad they didn’t draw stuff on my face.

And I fall asleep during lectures and seminars.

I remember it starting in maybe my third year of undergrad, though probably I’ve been caught dozing during classes before [I distinctly remember seeing photographic proof for this, but I can’t find it anymore, so I guess that means it never happened]. To the hilarity of my classmates, and to my own horror and embarrassment, I was not able to stay awake.

Where to sit in class. Conclusion: it doesn’t matter, I’ll fall asleep anyway.

I was reminded of this recently when I attended the local grad seminar. The guy next to me was either accidentally prodding me just when I was dozing off, or was helpfully trying to nudge me awake. I still don’t know which one of the two it was, but because we never exchanged so much as a glance once the seminar was over, I presume it was just all an accidental coincidence. Or a coincidental accident.

I want to point out to everybody who as ever talked at me, that me falling asleep during a talk is not necessarily related to the amount of sleep I’d had, nor a reflection of the quality of the presentation (well, partially, I will elaborate on that in a minute*).

At some point, I even asked for help from a therapist. Her tips to stay awake included: wearing a rubber band on my arm to flick myself with (apparently, the acute pain would give me a short surge of adrenaline), eat something during class (but apples are kind of loud to chew on), or doodle.

It turned out that multitasking did help – a bit. For a little while. But even with elaborate note-taking, which wasn’t my forte – there are countless examples of lecture notes starting out optimistically during the first 15 minutes of class and then trailing off into nonsense and eventually just blank lines – and reading things on my phone – it always seemed a bit rude even though I was literally not paying full attention in order to pay more attention, nothing really worked. The few occasions where I remember staying awake, I either basically wrote a complete comedy set (needless to say, I actually didn’t pay attention to the speaker) or it was because the professor giving the lecture was exceptionally engaging to listen to.

And I really mean exceptionally engaging. Seriously, my demands are unreasonably high. That specific professor taught beginner quantum mechanics to a bunch of quasi-engineers. He just oozed interest in his subject, had just the right amount of quirkiness, and didn’t rely on powerpoint presentations. His classes were all chalk and blackboard. Not that chalk is a requirement for an engaging talk, but the fact that I had to take notes at the same speed as he was teaching, probably helped me stay alert. And awake.

Nevertheless, it seems that a lot of public speaking events in the scientific world, whether it’s lectures or conference talks, are notoriously sleep-inducing.

Comic: you're not allowed to use the sprinkler system to keep your audience awake.
And the subject of many, many jokes.

While I realize my requirements for a talk that would keep me awake are unrealistic (I’ve been in talks that I genuinely found really interesting and still fell asleep), and I am in no way – I repeat: in no way – an expert in public speaking, I do have some suggestions on how to make your (scientific) talk just that tad more engaging**:

Tip number one – Be interested in what you are talking about. I know, that sounds really obvious, but the number of times you get the impression that the speaker doesn’t really believe in the things they are saying happens more than it should. I know that when I had to give talks about things I didn’t really care about, I definitely went into drone mode. I’m sorry for anybody who had to sit through that.

Picture of a drone
No, not this kind of drone. It would’ve been awesome if I’d literally turned into a drone. That would’ve woken people up.

Tip number two – Tell a story. Things are a lot more interesting to listen to if they have a beginning, a middle and an end. And some evil villain you had to fight (which could be a protocol that just wouldn’t go right, or that bug in your code, or your lack of general motivation). The Alan Alda Center for Communicating Science gives workshops on using the power of narrative in scientific communication. You can still do the intro – methods – results – conclusion thing, just make it more of a story. Also, while you’re at it: be honest. If that experiment took months to get right, it’s okay to say so. Everyone in science has been through some kind of struggle to get data, but most people only show the shiny, polished end results. Every time someone showed some intermediate (failed) results in a talk, it’s gotten some laughs.

Tip number three – Experiment. Figure out what works for you. When I’ve had to give talks as a student for classes or during group meetings, which are generally all safe, I’ve treated it as an experiment. I’ve tried different presentation programs. I’ve tried not adding any text on my slides. The latter experiment failed miserably; I completely forgot what I was supposed to talk about, but luckily there was it was not a very important talk and the audience were all people I knew. Don’t try something completely new for your thesis talk, obviously; use “casual” presentations for experimentation.

Picture of me improvising a presentation
“And this slide shows – uhm – random squiggles.” ***

Tip number four – Present a lot. Take every opportunity to practice. Try different types of settings. The only way to gain more confidence in presenting is to actually do it. I know, it sucks, but repetition actually works.

Tip number five – Be you. Add some personality to your talk. If you like to tell jokes, make a joke. If you enjoy adding a meme or two, just to it. Whatever floats your boat. The best talk I ever gave (in my *humble* opinion) involved me singing some songs about cancer and forces. At an actual conference. Obviously, the setting allowed for it, and I checked with the organizers first, but the response I got was overwhelming and a definite confidence boost. I took a risk to put some “me” in the talk, and it paid off.

Tip number six – This one is the most important one, I think. Keep it simple. Imagine you have to give the talk to a bunch of middle-schoolers. You want it to be engaging, you want your research to sound cool, you don’t want to overdo it with jargon and acronyms and walls of text. Even if your audience isn’t actually a bunch of 12-year-olds, this still applies. Be engaging, don’t overcomplicate things, and tell your audience why your research matters! The same rule counts in writing, actually. You can check what “grade” your writing style is for on this site, for example, what I’ve written here is about at great eight (I’m glad, it would have been embarrassing if I didn’t adhere to my own rule!)

We don’t all have to be excellent public speakers. But we can all at least try to not be awful speakers, and we can definitely try to not be sleep-inducing speakers. Well, except if I’m in the audience, then it’s all futile.

* Not sure if it actually will be in a minute, it all depends on your reading speed.
** This list is in no way to be considered a guide on how to make a good presentation. There are plenty of those on the internet (usually they come down to: don’t put too much info in your talk, don’t use too much text – pictures speak louder than words, repeat your take-home message – maximum three main points, and some more things to that effect).
*** This picture, however, shows my excellent photoshop skills!

How to write better in 280 characters or less

Originally posted on the Marie Curie Alumni Medium page on November 30, 2018

How to write better in 280 characters or less

Source: Pixabay

A lot of scientists are on Twitter these days. They tweet about their published work, about their life in the lab, and about the struggles of being in science.

However, it seems that a lot of the scientists are tweeting to each other. While this is not necessarily a bad thing (and a quite effective way to get a bunch of introverts to talk to each other), it clashes a little bit with the idea of Twitter being a medium for science outreach.

If you are a scientist on Twitter, you might be asking yourself: How can I communicate my research in a way that will interest different people/groups? And not just the people I’m already talking to at conferences.

Lucky for you, there is an actual science to “how do I get my tweet retweeted?”

You might be on a grant that stipulates things like “… to get relevant exposure and make the fruit of your work broadly available, outreach activities are a must.” You probably get some guidelines that are pretty “duh”: think about your core message; who is your target audience; how can you make your research catchy, concise and accurate? But how to actually do all these things, you might ask.

It is important to remember that one size does not fit all (it never does!).

Especially when using social media as an outreach tool, it can be easily forgotten that people process information in different ways, so it is important to match your communication to your target audience. One way to look at this from a social psychology perspective, in which some people process information heuristically, and other more systematically.

Very briefly, heuristically refers to the person primarily focussing on the superficial aspects of the message, while systematically refers to the person thinking carefully and deliberately about the content of the message.

And while you might think that your research is the most interesting thing ever, not everybody else will think the same. And even if they do find it interesting, they might not be able to understand it.

People can’t pay attention to everything. And moreover, you know what you are talking about. You have studied it for years. Other people — however — do not.

So when developing a piece of communication, you need to know two key things about your audience:

  1. Are they cognitively ‘able’ to process the information you want them to?
  2. Are they motivated to pay close attention to what you are telling them?

If the answer to both questions is “yes,” you are dealing with an audience that can process information systematically. If one of the answers is “no,” you have a heuristic audience.

And here’s the stinker. The default audience is “low” in ability (as defined as knowledge about that specific topic, not overall) and “low” in motivation*.

  1. Unless you are speaking to an audience comprising entirely of highly educated people, such as colleagues, experts or policymakers, there will be at least one person in your audience that is not an expert on what you are talking about. And remember that in the case of Twitter, the audience could be everybody.
  2. A lot of research may seem abstract or irrelevant to the general public. If it doesn’t affect them directly, why should they care?

It all comes down to this: most people will be processing anything you try to communicate to them mostly heuristically, or at least at first. You’d be the same, I’m sure. This means that the superficial aspects of whatever you are presenting are very important.

  1. Is it from a credible source?
    You may dislike putting the Dr in front of your name but it does make you sound a lot more like you know what you’re talking about.
  2. If using graphics/doing a presentation: colors, font, layout, … are all important!

So if you are tweeting about your research, and you want it to reach more people than just your colleagues, there are a few things you should think of:

  • Establish source credibility: now is your time to “brag” about your degree. You are an expert in your field, it’s okay to say so. It demonstrates both your expertise and your trustworthiness.
  • Physical attractiveness: though probably more important when communicating in person, do you really want to be remembered as “that slob” or do you want to be remembered as “that scientist”. It shouldn’t matter, but sadly, it does.
  • Number of claims/pieces of evidence: the more arguments you have to back up your claim, the more you look like you know what you are talking about!
  • Length of your message: you get 280 characters in a tweet, but you can also create a thread nowadays. Stick to the core message though, if you drift off into the details, people will lose interest.
  • Logical constructionif you construct your claims logically, then it will be easier for people to follow your train of thought.
  • Public consensus: do other people agree with you? Have they found data that supports your findings? It all makes what you say more believable.
  • Visuals: if you use a graphics (or if you are reading this to make better presentations and not just tweets, or to make video-content), pay attention to the colors you use, the fonts (no Comic Sans!), the speed with which the images load (depends on their size)…

Luckily, if you are aiming for an expert audience, the list is a bit shorter (though you will notice some overlap).

In general, the quality of your content is very important.

People will think carefully about what you say or write, so make it convincing. Make sure the claims are backed up by evidence that is both unbiased and extensive. Your claims should be detailed, and supported by other research (citations!). And finally, make sure your claims are logical. Give only the information that is necessary, but all the information that is sufficient to back up your story.

Now, there you go, you know what to do, so get on Twitter and tweet away!

This post is based on the “Strategies for Effective Media Outreach” session by Dr. Nehama Lewis (Board Member, MCAA) at ESOF2018 Toulouse, France

*Source: Petty (1986) “Communication and persuasion: central and peripheral routes to attitude change.” Springer-Verlag, New York.

You can read more about the social psychology that was briefly touched on here:

Heuristic-systematic model of information processing

Elaboration likelihood model

What is this “science” thing?

I’ve felt bad all week. Well, not really all week. And not really bad. I’ve felt a teeny bit guilty for joking that economic sciences is not really a “science”. The “soft” sciences (social sciences, economic sciences, psychology, to name a few) are too often ridiculed by practitioners of the “harder” sciences. I’ve done it too. Last week in fact, as I’ve just said.


If the sciences were ranked (by xkcd)

Most of the “soft” scientists I know don’t really mind too much (yes, I have soft science friends, I *can’t* be an elitist), and they laugh along. But still, I wanted to bring a bit of nuance and perhaps a tiny apology,


especially since this years’ Nobel Prize for Economic Science was awarded for integrating climate change an technological innovations into long-run macroeconomic analysis. Two subjects that are kind-of STEM-related.
Therefore, no matter how you might be willing to rank them, something can be considered science (from the Latin word scientia – “knowledge”) if the scientific method is applied.

What’s this scientific method?

The scientific method is a way to approach a problem or question by following this – or any similar – flowchart:

The Scientific Method: Obervation, Question, Hypothesis, Experiment, Analysis, Conclusion

Example of a scientific method flowchart

Very briefly and with an example, these are the steps you’d follow:

  1. Observation
    This can be anything you observe.
    Example: People seem a lot friendlier here in [town A]. When I pass people on the street, people smile at me more than they did when I was in [town B].*
  2. Question
    From that observation, you can formulate a well-defined question, a problem you would like to know the answer to. Science is simply the pursuit of knowledge, you know.
    Example: Are people more friendly in [town A] than in [town B]? (if friendly is defined as “smiling at people on the street”)
  3. Hypothesis
    You probably have a little bit of data (from your observations) that allow you to formulate the answer you would expect. This possible answer is something you can test: is what you assumed true or false?
    Example: People in [town A] smile more on to passers-by than in [town B]
  4. Experiment
    Now it is time to collect your data.
    Example: I’d go to [town A], walk around in the center for – say – 30 minutes and count how many people I pass on the street (and actually make eye contact with) and how many people smiled at me. I’d then do the same for [town B].
  5. Analysis
    When you have collected all your data, sit down and perform some analysis. Usually, statistics are the thing to apply.
    Example: I’d calculate the ratio of smiling people in each town, let’s say 17 out of 59 (29%) of people smiled at me in [town A], while 34 out of 81 (42%) people smiled in [town B].
  6. Conclusion
    Example: I reject my hypothesis; people in [town A] are not friendlier than people in [town B].
    This last step is checking if my hypothesis was correct (it wasn’t). Rejecting the hypothesis means I can go back and change my hypothesis and start again. If my hypothesis was correct, yay – I’ve done science!

Well, in reality, there is even more to it (both for rejecting and accepting an hypothesis).
In this example, there are many faults. Was my definition of “friendliness” correct? Were there factors I didn’t account for, like a bit of spinach between my teeth that caused more people to smile (or laugh) at me? More importantly, if I repeat the experiment, do I get the same result**? Was my experiment well designed; maybe there are better ways to test this same hypothesis?

Back and forth and back and forth and back and forth again.

Science is a very iterative process. Hypotheses are constantly being reformulated and retested. It is actually impossible to be 100% a hypothesis is true. The real science is when you try every which way to disprove your hypothesis. It is after a lot of back and forth and iteration, that a theory about something can be formulated. But you should know that in the scientific lingo, a theory has nothing to do with guesswork. It is the result of several repeats of observations and experiments that are generally accepted as reliable accounts of the world around us. ***

Scientist vs. engineer

I’d also like to note that science and engineering are quite different things. A scientist wants to know how things work while an engineer kind of just wants to make things work.
For example: engineers built the large hadron collider; scientists use it to study elementary particles.
Though it should be said that a lot of scientists have a bit of engineering in them, and vice versa, so this is probably a giant simpification.


* I just know that this is just because I look funny.
** Typically, at least three repeats showing the same conclusion are necessary to accept a hypothesis.
*** More about scientific theory here: https://www.gotscience.org/2015/10/theory-vs-hypothesis-vs-law-explained/

I was at the Friggin’ Fringe

Almost three years ago, I mentioned – in a passing comment – the Edinburgh Fringe (“a ridiculously elaborate comedy festival that is held in Edinburgh every August, for almost a whole month”). Specifically, I talked about how much “Nerd Comedy” there was at the Fringe. This year was no different.
Well, I guess the difference was that, instead of going to the Fringe for a day or two, I was at the Fringe for a whole week. In fact, I was part of a show.

I still can barely believe it.

And of course, I was in a nerdy show.

Anyway, it was absolutely amazing. We had a total of 162 people come to our show over the course of 5 days, which was an absolute amazing turnout. We got a lot of laughs. We sometimes lost our track (or the chords) but that was just part of the charm. We made a lot of silly faces. Well, I did.


Some of Valerie’s many faces (I actually look worried a lot of the time)

For me, it was mostly a lot of adrenaline. I know this barely constitutes as a proper blog post about doing a Fringe show, but I just wanted to have mentioned it. While I’m at it, let me thank Matt, Coren, and Yana for being such amazing co-stars; Valentina for the amazing organization; and MCAA for putting me on a stage.

There will be a video for those that unfortunately had to miss it, at some point in the pretty near future. So if you were like *damn, can’t believe I missed that,* there’s no need to worry!


Note: wow, that is a lot of pictures of me, it’s quite unsetteling. I’m so sorry.

EduTourism (II)

I had just submitted my PhD thesis for review (*mini-applause for myself*) and decided that the two months I had before my PhD viva (or PhD defence) would probably drive me half-insane and maybe I needed an extended break somewhere very far away.

So I went very far away: I booked a trip to Australia. However, still being me (as in: a science communication addict?) and considering my previous experience as an edutourist, I emailed a few universities to let them know I would be around and willing to volunteer at any scicomm event they might have. One university replied. I also signed up to an Australian mailing list and answered a call for volunteers.

So, in between my actual travels, I ended up doing some public outreach slash science communication down under. And boy, it was fun.

The university that replied to my spontaneous volunteering was LaTrobe University in Melbourne, where I had the opportunity to talk to a year 9 class (which are, I’m guessing, 14-year-olds?) about my research and my experience as a PhD student. I slightly changed a previous talk of mine (mostly left out the singing; oh yes, I went to a conference and brought my ukulele once, it was marvellous) and spoke to a class of maybe 30 students about the Physics of Cancer in general, and how my research sort of fits into that field. The students seemed very interested and asked some questions about what it’s like to do a PhD and if all that travelling isn’t very tiring. As thanks, I received a gift card which was super useful because I used it to buy a raincoat. Apparently, it does rain in Australia.


La Trobe Institute for Molecular Science

The other event I attended was the Science and Engineering Challenge, which is a national competition organised by the University of Newcastle that challenges teams of high school students (I’m guessing 14-year-olds?) to do a range of different tasks related to engineering and science, such as building a water turbine, a suspension bridge, a catapult, creating an encrypted code or building an earthquake-proof structure. I helped out at the Sydney event for two days.


Students at the final challenge: suspension bridge. It was very suspenseful.

Apart from the fact that I wasn’t allowed to take part myself – I would have loved to build a water turbine and catapult – it was absolutely amazing. My role was to facilitate the aforementioned activities (one for each day I was there), and it was really interesting to see the creativity and competitiveness of the students. Sometimes, the more unexpected design was more efficient, sometimes the group with the most extensive and thought-out plan ran out of time and couldn’t finish their idea. It was up to me to encourage the students to think both logically and out of the box without actually really helping (or so I tried).

As with many science outreach activities, the event relied on volunteers from universities. But more unusually, there were also volunteers from the Rotary and from companies (on Thursday I was there, a bank). This made for an interesting range of ages and backgrounds, which in my opinion was a wonderful extra touch and helped bring home the message that a) one of the most important skills for STEM* is creativity, b) with a STEM degree, you don’t necessarily have to stay in STEM, you can go into a whole range of careers, and c) STEM is really awesome, considering all these people – not all them working in or studying a STEM subject – that give up their time to come help at the event.

Anyway, I went on holiday for 5 weeks all on my lonesome and having a few days of scicomming in between was really fun.

Thank you Jess from LaTrobe University for the opportunity to speak to the y9 class and the tour of the university, and Terry from Newcastle University for signing me up for the Science and Engineering Challenge.

* Science, Technology, Engineering and Mathematics

Polymath (πολυμαθής)

Sometimes I feel like I was born in the wrong era.

Usually, this feeling is music-related. Now that I have renewed access to my dad’s old record collection (and a record player, #Hipster), I can’t help but feel that rock music from the ’70s and ’80s surpasses anything being made now. Comparing music from the “olden days” to music now is of course not entirely fair; what still remains has already withstood the test of time, current music hasn’t had to (yet).

Music aside, my wrong-time-feeling also applies to how I feel about science and research. Nowadays, scientific discoveries seem to always be the result of hard work of an entire team of scientists for countless years. There is so much knowledge and information out there, it seems imperative to find one’s own little niche and specialise, specialise, specialise. It is impossible to be a master of all.

However, I long for the golden old days of the polymaths and the homines universalis when academics were interested in all fields. They were allowed, or even required, to branch out, study all sciences, not to mention humanities, linguistics and arts. I’m speaking of people like Galileo Galilei and Leonardo Da Vinci. My favourite person, D’Arcy Thompson, would also be considered a polymath.

A polymath is defined as someone with “knowledge of various matters, drawn from all kinds of studies ranging freely through all the fields of the disciplines, as far as the human mind, with unwearied industry, is able to pursue them” (1). I noticed while perusing the wikipedeia page, that the examples given of Renaissance Men are indeed all men. Even if I was born in the right era to be an homo universalis, I would still have been born the wrong gender.

However, there are at least a few examples of female polymaths, and I wanted to introduce you to one of them: Dorothy Wrinch. Just in case you wanted a more nuanced example.
Dorothy Maud Wrinch (12 September 1894 – 11 February 1976)
Dorothy Wrinch was a mathematician by training but also showed interest in physics, biochemistry and philosophy. She is someone who – even though I’ve only recently heard of her – is an excellent example of the homo universalis I wish I could be. She was also a friend of D’Arcy Thompson, though if I remember correctly, they mostly upheld a written correspondence.

In any case, Dorothy is known for her mathematical approaches to explaining biological structures, such as DNA and proteins. Most notably, she proposed a mathematical model for protein structure that – albeit later disproved – set the stage for biomathematical approaches to structural biology, and mathematical interpretations of X-ray crystallography.
She was a founding member of the Theoretical Biology Club, a group of scientists who believed that an interdisciplinary approach of philosophy, mathematics, physics, chemistry and biology, could lead to the understanding and investigation of living organisms.

She is described as “a brilliant and controversial figure who played a part in the beginnings of much of present research in molecular biology.  (…) I like to think of her as she was when I first knew her, gay, enthusiastic and adventurous, courageous in face of much misfortune and very kind.” (2)
Actually, come to think of it, maybe Dorothy was born in the wrong era. Nowadays, using mathematical approaches to protein structure is practically commonplace. Though I’m not quite sure how well philosophy would fit in.

Anyway, I still feel that interdisciplinary research, and having broad interests, is not the easiest path to go down. But as long as we have inspirational people to look up to, past and present, we know it is worth a try.

(Wow, went way overboard with the #Inspirational stuff towards the end there.)

(1) As defined by Wower, from Wikipedia.
(2) Dorothy Crowfoot Hodgkin (Wrinch’s obituary in 1976).
An updated version of this post was published on the Marie Curie Alumni Association blog on March 19, 2019.



Now a scientist/engineer hybrid, I used to be one of those kids that really liked playing with LEGO. Surprisingly – or maybe not really – I have been able to incorporate LEGO into both my work and my favourite extracurricular activity (public engagement):

As it turns out, I’m not the only one who loves LEGO and science. Who knew? 

1. A truly Lego®-like modular microfluidics platform

Inspired by LEGO, researchers have created a modular system that can be used to build microfluidic channels. In microfluidics, the goal is to control fluids in small channels (micro-sized, usually). It has been used in the development of inkjet printers and is now an interdisciplinary field that will allow things such as high-throughput screening and lab-on-a-chip technology. The major advantage is that low volumes can be used.

The LEGO-microfluidics try to solve one of the problems in the field: microfluidic systems aren’t really versatile, and 3D microfluidic systems are quite difficult to make. By creating LEGO-type PDMS blocks with microfluidic channels, blocks can be stacked easily in 3D networks, but also easily changed around to create a whole range of different configurations.

Screen Shot 2017-11-03 at 13.35.59.png

Example of a LEGO-like microfluidic system.

While I’m not entirely convinced yet that the production of these blocks is simple, and I have doubts about the alignment of the different channels as well as sealing the interfaces between the different blocks (you don’t want everything to leak out), I always love creative solutions and especially if they are inspired by my favourite block toy! While it might not be super-useful in a research context, it can be used as a public engagement tool to show off some nanotechnology. How can we use microfluidic channels for mixing small amounts of liquid, or separating them out? What are the different types of flow, for example, laminar?

All of this and more, soon near you (perhaps).

2. Liquid-handling Lego robots and experiments for STEM education and research

Another example is a liquid handling tool that has been built using LEGO pieces. LEGO does have quite some educational kits that teach about robots, mechatronics and programming, that allow easy conversion to the development of STEM education tools when in the hands of creative minds. In this case, a pipetting robot was developed that can be used in biology, biochemistry or chemistry demonstrations or workshops.

Using this tool can allow for a very educational and interdisciplinary (+1 for the buzzword) workshop that combines engineering (building and programming the robot) and science (experiments such as performing delusions, measuring pH using a pH indicator or anything).

Screen Shot 2017-11-03 at 13.56.07.png

The pipetting bit of the liquid-handling robot, compared to a lab pipette.

All of this and more, soon near you (maybe).


These were just two examples of geeky scientists and engineers proving that you are never too old to play with LEGO. Even if the box says ages 4-99.

Sources and suggested links:
The LEGO-microscope is based on http://legoscope.squarespace.com/
LEGO-microscope pictures by Rolf Black.
The two papers I have referred to and copied images from are:

LEGO education runs various different events and competitions, including the FIRST LEGO LEAGUE, that challenges teams of 9-to-14-year-olds to build and programme robots to complete specific tasks. I helped out at a tournament last year and it was awesome. And not just because my badge said: “Robot Practice Table Supervisor”.