No, this is not an epic battle. It’s a question that has been bugging me for the past week or two:
What is the difference between a geek and a nerd?
According to Big Think: ‘the words “nerd” and “geek” are often used interchangeably, as if they mean the same thing. They actually don’t: geek – An enthusiast of a particular topic or field. … nerd – A studious intellectual, although again of a particular topic or field.’
But then, “Harry Potter Nerd” sounds a lot better than “Harry Potter Geek,” and I’d like to know which one I am!
And then there was a website claiming that geeks are “socially adapted” nerds. Which I don’t really like as an explanation so I’m ignoring it.
So I asked my friends, and I asked twitter, and I was none the wiser. It seemed that about 50% of the people I know adhere to the description from Big Think above: nerds are the more “academic” of the two. The other half of my friends claim it’s exactly the opposite! Tech geeks, all of them!
A fellow nerd comedian (self-described) said that he uses them interchangeably, “Mainly because that’s how I was described in grade school. Nerd. Geek didn’t show up for me until college.”
Another interpretation was given by David Ashlin on Twitter: “Going by the, admittedly apocryphal, etymology that GEEK=General Electrical Engineering Knowledge, I always differentiated it by theoretical vs practical, as in nerds know things while geeks know how to do things.” What a nerd.
Another source used twitter data to differentiate the two, and created a graph with words commonly associated with “geeky” and “nerdy.” Apparently technology and comic books follow under the geek name while science pursuits, books, and education are more for nerds.
He summarizes: In broad strokes, it seems to me that geeky words are more about stuff (e.g., “#stuff”), while nerdy words are more about ideas (e.g., “hypothesis”). Geeks are fans, and fans collect stuff; nerds are practitioners, and practitioners play with ideas. Of course, geeks can collect ideas and nerds play with stuff, too. Plus, they aren’t two distinct personalities as much as different aspects of personality. Generally, the data seem to affirm my thinking.
I still don’t really know the answer. I consider myself both, depending on the situation but don’t ask me what exactly the differentiating situations are…
What do you think? What is the difference between a geek and a nerd? Share your view in the comments or on the original post on twitter.
A few months ago, I invited the wonderful Kyle Marian to Seattle to give a comedy workshop at GeekGirlCon.
Within 90 minutes, I saw a group of people going from being complete strangers to co-writers, participants going from hesitant to join the activities to laughing, and teenagers going from shy and reserved to stepping up on a stage to talk for 3 minutes; it was amazing to see community and confidence grow in such a short time.
What Kyle did extremely well during this workshop, in my opinion, was create a safe space for people to mess up – which essentially is crucial for building confidence.
Creating a safe space to fail
When you watch a comedy special, it looks so easy. The stand-up comedian moves smoothly between storytelling and jokes, seamlessly adding in crowd work, impeccably times their silences and their words to create space for laughs.
What you don’t see is all the work that went behind it, from jotting down random ideas in a notebook to having jokes fall flat at open mics. Comedy is hard work, and part of that hard work is being okay with things going wrong once in a while.
What I’ve found very useful, from my own experience as well as witnessing the GeekGirlCon workshop, is having a safe space to fail. A space where you don’t have to feel scared to voice out that random idea that you think won’t work, a space with such a supportive audience that by just forgetting what you were going to say, you’ll get an encouraging clap or laugh.
In the workshop, this is what Kyle had created: if an idea didn’t quite work, it wasn’t the end of the world but other participants would help to find a way to make the joke work, add an extra quip, add repetition (three is the charm), all while being super-supportive.
Comedy for Confidence
The first time I stood on a stage for stand-up, I did so through BrightClub Dundee. Two weeks earlier, I had gone through their training – a professional comedian taught us the ins and outs of comedy: how to write jokes but also how to hold the mic like a “real comedian.” I thought I’d just attend the training and maybe be a better presenter.
But after the training, I had an idea for a set and voila, there I was, on a stage, strumming Bruno the Blue Ukelele, adrenaline rushing through my veins.
It’s terrifying and exhilarating. Ask any comedian, they probably still get nervous before getting on a stage, no matter how long they’ve been doing this. But in another way, it really builds confidence. Standing there in front of 10, 30, 50 or 100 people, and getting that first laugh, you feel like you can take on anything.
And it’s even more of a confidence-boost to feel like you’re empowering others.
Geeky Comedy Seattle
So, I started this thing. I wanted to create a space for alternative, geeky, comedy (because that’s what I do) in a city that is, inherently alternative and geeky (Take that, Portland!)
Enter Geeky Comedy Seattle. It’s still early days, but if you want to come to a fail-safe place (as in, it’s a safe place to fail!), you can join us on February 1st month for a workshop and/or open mic, or come see the next show.
Could we bring dinosaurs back to life? Will we ever make contact with aliens? Will robots take over the world?
With these questions in mind, 6 scientists and I-didn’t-really-count-how-many audience members gathered together for the panelThe Science of SciFi, at this year’s GeekGirlCon – a celebration of geekiness in all its glory!
Dr. Daniela Huppenkothen, who studies black holes and asteroids using modern statistical tools and machine learning methods;
Dr. Kim Bott, who studies alien life scientifically (yes, that’s a real thing and it’s called astrobiology);
Dr. Meredith Rawls, who writes software to handle terabytes of nightly data from the Large Synoptic Survey Telescope, which will ultimately become the highest-resolution movie of the night sky ever made;
Dr. Jeanna Wheeler, who works with mice and nematode models to understand diseases like Alzheimer’s and ALS; and
Dr. Jenn Huff, who as an archaeologist focuses on questions like “what can technology we invented and adopted in the past tell us about how we relate to technology now and in the future?”
Guided by questions from the audience, we explored the links between scientific research and science fiction, looking at what advances are being made in fields portrayed in SciFi media, discussing fictional and real research, and what lessons each can learn from the successes and failures in the other.
Here are some of the take-home messages I’d like to share.*
Science fiction makes scientists
One thing that was immediately clear was how science fiction had influenced the panelists in their life. By seeing positive female role models in their favorite science fiction shows and movies – just think Samantha Carter from SG-1, Ellie Sattler in Jurassic Park, Ellie Alloway in Contact, and numerous female characters in the Star Trek franchise, – they had someone to look up to and aspire to be like.
Seeing female characters who were both physically and intellectually adventurous, who were tough and smart, who were well-rounded and passionate, showed the women on the panel, and many female scientists, that they too could be a scientist.
There are several studies showing that having representation matters. If all you ever see is people who are not like you doing a thing, you’ll be less inclined to do that thing. If we can create positive role models, show that STEM professionals come in all stripes, we’ll create a more diverse and exciting research environment.
… but we can still do better!
Despite there being quite a few inspirational science fiction scientists, the overall depiction of scientists and the science they do in movies, series, and books is often – well – inaccurate.
Scientists are not (always) super smart, geeky people who sit around in a lab coat for no apparent reason and solve the science thing within an hour. Oh, not to mention being a very attractive, mid-twenty-year-old with 4 PhDs. Or a software developer spending 30 seconds to find the bug in their software. Because that sounds totally possible, and I know some software engineers.
Let’s also not forget the idea that for scientists in fiction, science is often their whole life. Showing that being super passionate about science, and science only, is the only way to be a good scientist is not a message we want to share. Could we have more well-rounded, realistic, scientists in fiction, please? With hobbies and all?
And while we’re at it, let’s get some science straight: mutated does not equal evil; mutation is the substrate of all the beautiful diversity we have everywhere!
Special acknowledgment to shows that do show good representations of scientists. The Martian depicted a scientist pretty well. And not to pull favorites, but The Expanse has a pretty good portrayal of gravity systems affecting how a body develops. Not to mention that long-haired people in space definitely tie up their hair and that there is space in space – and it takes time, fuel, and pulling Gs to travel through it.
Accuracy versus story
This brings up another question: does science fiction need to be scientifically accurate?
Sometimes science fiction is fun because of the story or the characters. Who doesn’t love some good space magic?
The consensus seemed to be that, as long as things are consistent with the story, and that the movie/series/book isn’t claiming to be super scientifically accurate while totally not actually being so, accuracy is not the most important thing.
Human vs. Tech?
Another point was brought up during the panel: how will future technology shape our future?
It started with a discussion on making designer babies – whether this would be feasible, and what the ethical implications might be. With CRISPR/Cas9 technology making small edits to a genome a lot easier, it does not sound like something too far in the future!
While we are likely to be able to treat serious diseases with a clear genetic cause sometime soon, making genetic super-humans is a whole other deal. We don’t really really know enough about the genetics of intelligence (to name one trait) to make those changes! And if we believe science fiction, making superhumans usually does not end well.
That’s the way it usually seems in SciFi – tech will either be the end of us all or the solution to all our problems!
But if we’re being honest, technology is just heated up rock (quote from Jen). Most of our problems are of social nature, and technology will not be able to solve those.
For example, there are numerous examples of computers in general, and algorithms in particular, increasing inequality. We give computers datasets that are biased, so the automation will also be biased!
Technology is not the solution. It is an agent. We would better ask what humans are going to do with new technology. How will we shape our future?
Honorable quotes (slightly paraphrased):
“Can we ever train humans to be unbiased?” – Jeanna, as a response to the question of whether we can ever make AI/algorithms unbiased.
“I’ve never watched Interstellar, but I’ve read the scientific paper that came out with it.” – Daniela, commenting on how Interstellar felt a little close to her real work.
“If we can’t fix/control our own climate – we’re unlikely to be able to change that of another planet. Also, should we? Do we need another planet?” – Kim and Jeanna commenting on when we’ll be able to terraform another planet. Also, remember that time we *accidentally* left tardigrades to the moon?
“We do have spooky action at a distance” – Kim bot on how quantum entanglement explains how we can transfer information faster than the speed of light. Which is probably as close as we can get to having transporters.
* We talked about a lot more than what I’ve briefly described here. Feel free to reach out to any of the scientists on twitter to find out more or to ask your favorite science-versus-science-fiction questions!
Giving a talk is hard. Giving a talk to the “general public,”* is possibly even harder. What if people don’t care about what you’re talking about? What if you’re not able to explain it in a clear way, without “dumbing it down”? There are many pitfalls to giving a public talk, and from giving and going to quite a few myself, I have a few ideas on how to make sure you nail your next talk!
A mistake I’ve seen quite a lot is diving straight into the data. But that will immediately lose anyone in the audience who is not an expert in [insert topic of talk here]. Here’s an example outline for a [fictional] talk about research on a sciency thing:
Or maybe what you’re talking to contributes to the rising sea level? Great for you! (not so great for the Netherlands though). Use that striking image of cities that will disappear as your other introduction slide.
Or, if you’re like me, your research is (was) about the Physics of Cancer. I like to start talks pointing out that “physics” and “cancer” are not necessarily two concepts that we think about in the same context.
Whatever you’re research is about, there is a reason to care and a very illustrative image to accompany your impassioned exposé of why we should all be caring. You’re learning more about how cells work which can lead to better disease treatment. You’re satisfying our human need to keep on exploring by making better rockets to send into space. You’re leading to a better understanding of how humans interact with each other which will help us all be better to each other.
I don’t know, I’m just spitballing, but your research is important and we should care. And there is most definitely a meme, powerful image, or powerful gif available that shows us why.*** Because, let’s face it, we all like pictures more than words, no?
2. What do we know?
Time to show some numbers. Maybe there are some prediction models and the observations made in the last decades are increasingly matching those (scary) predictions. If you’re giving that talk about sea-level rise, show the climate temperature rise graph. If your talk is on a new and tinier microchip, Moore’s Law is your thing to show. If your talk is on cancer, you can give some numbers on incident rates, or how earlier detection can lead to earlier and better treatment.
In my case, my second slide is an overview of what cancer actually is, followed by an outline of what my talk is about: how understanding the changing mechanical properties of cells and tissue can help us better understand how cancer works, improve diagnostics, and come up with better ways to detect cancer.
In short, set your research into a more general perspective. What is the current view on this subject, and where are the giant gaps in the knowledge. Because that’s where you come in!
Tip: if you ever start a slide with “there’s probably too much data on this slide…”, just don’t. Break it up into multiple slides. Only show the data that matters for what you’re saying. Anything but saying there’s too much data.
3. Time to shine!
This is where you can plug in your stuff. What is new about it? What problem is it solving? What does this new shiny data show?
Some tips to help:
Show the process of your research and tell a story. People really like hearing stories about science is done. Maybe there’s an anecdote about how you were messing around with scotch tape and suddenly discovered graphene. Or about how you were able to hitch a ride to the field study and made an unusual friend. Or how the first time you set up the Atomic Force Microscope, which uses a tiny micro-probe, you broke the tip right when the professor walked into the lab.
Also, don’t “half” introduce a complicated concept. If you need to explain a complicated technique to explain your results, go ahead. But don’t half-mention them and leave the audience wondering what that word (or abbreviation) was all about. Did you know that AFM can refer to Atomic Force Microscopy, Acute Flaccid Myelitis, or the American Film Market?
End your talk by looping it back to the first point. You told us why we should care about the subject, now tell us what your new findings mean for that subject. Add some future perspectives. Add another meme. Add an inspirational quote. Leave time for questions. Or if you’re me, you might take out a ukulele and sing a song.
A final tip, make sure you plan your talk in advance! There is nothing more frustrating than seeing someone rush through their slides because they didn’t do a run through.
If you are in research and early in your career, such as a PhD student or a Post-Doc, you might have the chance to take some science communication training through your institution. I would highly recommend it! There are plenty of resources online as well!
Final thought, I secretly believe that scientists at all levels should take get training and practice about giving engaging presentations, to whichever audience, and learn how to make sure your audience doesn’t get put to sleep.
Good luck! You got this!
* “General public” is the worst blanket description of an audience. Let’s just say that the people who might come to a public lecture are not experts in whatever you are talking about but do have an interest in it (or they wouldn’t be there).
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!
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?
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).
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 also: a 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.
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.
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.”
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.
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.
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*
*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.
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.
Your personal brand is the most stupid way in which you’ve accidentally injured yourself.
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:
“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.
And to quote a slogan from a famous brand: Just do it! (also typically associated with Shia LaBeouf, for other reasons)
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.
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.
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.
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.
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.
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!
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!).
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:
Are they cognitively ‘able’ to process the information you want them to?
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*.
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.
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.
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.
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 construction: if 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:
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.
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:
Very briefly and with an example, these are the steps you’d follow:
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].*
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”)
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]
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].
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].
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.