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.

Enough with the shameless self-promotion.

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 panel The Science of SciFi, at this year’s GeekGirlCon – a celebration of geekiness in all its glory!

The panel consisted of local researchers from Seattle 500 women scientists:

• 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!

Thank you to 500 women scientists (especially the Seattle pod) & the Marie Curie Alumni Association (MCAA) for their support in setting up this panel. Thank you to GeekGirlCon for hosting us!

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:

1. Set the stage

Tell the audience why they should care. Maybe your research is on ice shelf stability and there was something recently in the news about a city-sized chunk of ice breaking off an Antarctic ice shelf. Lucky you! (not so lucky for the ice shelf though). Use that powerful image as your first slide!

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?

4. Conclusions

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).

** I’m kidding. I’m technically from New Jersey.

*** Yes, I know I used a wordcloud as an example.

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

How to write better in 280 characters or less

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 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:

Heuristic-systematic model of information processing

Elaboration likelihood model