SLEAP and the Science of Movement

Ask A Biologist Podcast, Vol 115
Podcast Interview with
Stick figure, with symbols used for human body part notation.

Dr. Biology:

This is Ask A Biologist a program about the living world and I'm Dr. Biology. What might look like a typo in the episode title is actually not. We're not going to be talking about sleep. Like when you go to bed or take a nap. We're going to talk about SLEAP, S-L-E-A-P or social, leap, estimates, animal, poses. This is a software being used by scientists to track animal and plant movement. And it looks like it can also be used by dancers and engineers to track human movement. SLEAP is also an example of how A.I. is being used by scientists to study animal behavior. 

Now, if you think this is the first tool that's been used to record animal movement, you'd be mistaken. Turns out humans have been using a symbolic language, called labanotation, to record human movement, including dance performances since 1928. My two guests are collaborating on some research that is investigating these tools and their use. 

Talmo Pereira is the developer of SLEAP and Salk Fellow at the Salk Institute for Biological Studies. And Valerie Williams? She's a professor in the Department of Dance at The Ohio State University and an expert in labanotation. Given the background of these two guests, we cover a lot of topics from artificial intelligence to animal movement and tracking, as well as human movement and dance. 

This episode is part of a series from the SICB 2022 conference. You can listen to other episodes we recorded at the conference with the help of the Spatial-temporal Dynamics and Animal Communications Group. So, let's get started. And head over to the conference to catch up on some SLEAP. 

[conference background sounds] 

Welcome to both of you to Ask A Biologist.

Talmo:

Thank you.

Valarie:

It's great to be here 

Dr. Biology:

All right. When I stopped in to watch your lecture, I was introduced to something I was not expecting, and it was basically another written language. Let's talk a little bit about this language and what it's used for. 

Valarie:

So, labanotation is one of three different theories created in the 1920s in Europe, and it was advanced by several women here in the United States and in England during the thirties and forties, and fifties. And the two systems sort of separated during World War II. One became Kinetography Labon in Europe. And one became Labanotation in the United States. 

And it's really used for analyzing human movement, although we have had notators who have analyzed bugs before and it's been applied to dances from around the world. We have a huge collection at Ohio State University, special collections of different scores. And it also is typically used for recording Western theatrical dances. And one of the things that it does is take symbols that have been created over time. 

And every time we need a new symbol, we just create one much like words and we score them in time and horizontally over body parts. And we can notate as many people as are on the stage or in the environment, and it's used internationally.

Dr. Biology:

For the listeners, we'll put in the chapter now to put an image in there so you can see a little snippet of this kind of notation because it really is a lot of symbols that go into it. Now, you said you make up words even if we make up words. We have to start to agree on them. Is there some kind of a consensus that the word that Valerie made up we're going to keep? 

Valarie:

Yes, we have an international council called the International Council of Kinetography Labanotation Notation of which I am the vice-chairperson. And we meet every two years. This coming summer we are meeting in Budapest, Hungary, and it brings together about 200 notators from around the world, from the Americas, from Asia, from Europe and from Oceana. We get together and we have research proposals for new symbols, and we read them and embody them because part of it's not just writing down dance, but it is the reverse as well. You have to have your friends, your notator friends, read and embody what you've written. Otherwise, it just sort of sits there.

Dr. Biology:

So, when you say embody you mean act out, actually move?

Valarie:

Yes. You have to literally get up and move. Just like yesterday, we had our audience members get up and actually experience what the idea of places as it's represented by a rectangular form, or how you would move a singular body part on the staff. And so, yeah, we call it embodied cognition or embodied knowing.

Dr. Biology:

The notation process is it takes training. My understanding it takes about a year. Right.

Valarie:

For Labanotation?

Dr. Biology:

Yeah.

Valarie:

For the professional training after you've done about eight years of other certification.

Dr. Biology:

Okay. So, it takes a long time, right? And so, we're not going to have a lot of note-takers out there. We have scientists that are very interested in animal movement and how that relates to behavior and other things. So, Talmo you've taken this need for recording and understanding movement to a whole new level. So, let's talk a little bit about what you're doing.

Talmo:

Yeah, sure thing to take a step back and put it into context. The place that I'm coming from is that as a neuroscientist, I want to understand what the output of the brain is. And when we think about it evolutionarily, the reason why we have brains is so that we can move around in our environment and be able to survive better in response to what we're perceiving around us. 

So, when we think about things like complex behavior, really what it's made up of is a series of movements just like dance is, but it's also what animals are doing all the time. So, when we started thinking about how do we go about quantifying you know, putting some numbers down that describe movement? Well, at the time, I was certainly not aware of something like Labanotation, but we thought that we'd be able to break it down by first coming up with a way to take videos. So, images of our animals and just for the very first pass being able to go from the videos to motion capture essentially. 

My early inspirations were thinking about what people do in Hollywood I think I might have seen like a behind the scenes clip for how they're animating Shrek. And you have all these little markers, you have all these points that define where all the body parts are of the motion of that character. And it's all based on capturing the movement of real humans. So,, we thought, why can't we do this with animals in the lab? And the first problem that we ran into was that, well, I started working with fruit flies. They're really, really tiny. And we can't really ask them to wear motion capture suit you like we can, you know, professional actors in Hollywood.

Talmo:

So, the next step was then to turn to artificial intelligence. And around this time, artificial intelligence was really kind of making a big comeback with the advent of what's called deep learning. It's a form of machine learning or essentially a way to give a computer algorithm an image and have it be able to tell you the locations of the body, parts of your animal or person without having to actually have markers on them at all.

Dr. Biology:

When we talk about deep learning, we talk about A.I. and machine learning for that matter, there's some confusion because they're similarities but they're not all the same. In essence, there's the ability to pre-trained a computer, give it a set of images, and tell it what those images are. Or what they're doing. But then there's the other part. Once you do that and you have the computer start to learn by looking at a lot of images. So, where do we fit with your work?

Talmo:

Yeah, that's a great question. That was kind of one of the core challenges that we had to solve in order to be able to do this type of quantification of motion with the animals. There's really not a ton of examples that we could give our computers in order to be able to train them to predict their emotions. And so, part of all we had to do is to come up with some clever engineering tricks where essentially, we created a program that would allow us to throw in a video of our animal this could be any animal. 

And we could, you know, take a cell phone video of your cat and you could just load it up, click on the body parts that you want to track, go to a new image, click a few body parts, and then you ask it to try to learn the relationship between the image and the location of the body parts. And it'll do its thing for a little bit and then return to you with its best guesses where the body parts are in the images that you haven't labeled. Then what you're able to do in our program is to import those guesses and then just fix where I got them wrong. And so, this is what we call human-in-the-loop training. 

So, it's a real collaboration between the program and the user. And what we've designed it to be able to do is to, you know, not require you'd have to do any programming. So, the idea is that we have all this super sophisticated A.I. under the hood powering the technology, but we really designed it for biologists at the end of the day, so that you're a researcher, all you need to do is be able to load up a video of your animal and do some clicking.

Dr. Biology:

The software has a name, right?

Talmo:

It's called SLEAP. You can check it out at Sleap.ai. and that's spelled SLEAP.

Dr. Biology:

Right. Please do not get it confused. Right. [laughter]

Dr. Biology:

And it's Open-Source, right? Anybody can use it.

Talmo:

Open-Source and freely available with plenty of tutorials and documentation.

Dr. Biology:

Would it be possible for kids to record their cat doing things and use SLEAP? 

Talmo:

It is 100% possible. All you really need is a laptop. You might need a little bit of assistance, perhaps with just, you know, installing the program, but it works pretty much anywhere, and you don't really need any special resources to be able to use it.

Dr. Biology:

Hmm, we might have to see if we can create some kind of experiment or challenge that we give our listeners.

Talmo:

Yeah, absolutely. We've had a lot of fun with sleep where we've been able to track all sorts of things. Some of them have led to some kind of pretty interesting new scientific directions but some of them have been just for fun, you know, just taking videos of people's pets off of social media and, you know, tracking them while they're doing something funny or even just applying it to very creative new applications. Like, you know, we definitely designed this to study the movement of animals in the context of neuroscience. But people have really taken it and ran with it to do all sorts of type of movement tracking. Like, for example, with plants, it turns out that plants do move.

Dr. Biology:

Yeah.

Talmo:

They move quite a bit Just a different a different time scale. 

Dr. Biology:

Right, right. And often it's tracking the sun. Yeah, very good. Valerie, I'm curious how did you and Talmo get connected?

Valarie:

Well, I think we have a mutual colleague, Jess Cornwall. And I had been pulled into an article that she was doing on sort of examining different ways that we produce the output of movement in graphical forms And I thought that was kind of the end of our collaboration. But then she said, Oh, I have something else now. I want you to meet this guy, Talmo Pereira, and he has this new lab at Salk, and he does motion capture type of experiences. And I know you've done some motion capture in the background years ago, so maybe you two should talk. So, we started Zooming about six to eight weeks ago and have spent the last week together making a really interesting sort of analysis of what we both do and where the overlaps are.

Dr. Biology:

There was one particular slide where it had basically you animated Talmo's SLEAP on one side and your annotation on the right.

Valarie:

It was our fruit fly soloist.

Talmo:

Mm hmm.

Dr. Biology:

Yeah. And it turns out it works quite well. It's almost like I'm like you're checking each other.

Valarie:

Right. And in fact, that was kind of our first foray into seeing what we just both do and sort of see where we do have some overlaps. And so that video Talmo just sent me in real-time. And when it came back to him, the notation, he said, I think you're missing a few legs or the timing is slightly off.

Valarie:

And he said, but you got this in real-time. And I said, yes, I was your human-computer. So, then he slowed it down and sent back some additional footage. And then we were able to match it a little more succinctly. But I think there's still some little glitches there.

Talmo:

I will say, though, like it is absolutely, really unbelievable how accurate she could have been. I was assuming she was going frame by frame, which is typically what we do in the lab, but she was actually annotating that in real-time. It's insane. I mean, these animals move so quickly. There's so many legs. It's absolutely incredible what Valerie can do.

Dr. Biology:

But actually, get you back to the human mind in the brain. How well it really works. Let's talk about animals and movement and the brain. The key piece is there. I suppose if you can't avoid a predator, be a problem. If you can't find a mate, it could be a problem. Let's talk a little bit about the brain and animal movement.

Talmo:

Yeah, that's my favorite topic. So, a lot of the classical work and a lot of the thoughts that shaped the current thinking of how the study of animal movement, animal behavior comes from the field of ethology. And if you're not familiar, ethology is the study of natural animal behavior. It was awarded a Nobel Prize quite a few years ago now. And it's sort of a bit of a spiritual predecessor of both neuroscience and biology. So, it kind of comes from both fields. And if you want to picture what ethologists kind of looked like. Well, think of field biologists, you know, sitting in a bush with a notepad and rigorously taking down notes, perhaps not in Labanotation, but with their own systematized ways to describe the behavior or effectively really the movements of animals in their natural setting. 

They then take those notes back to the lab and try to come up with models and theories for why animals are moving in particular ways, such as are they trying to fulfill particular desires or needs like reproduction or food-seeking, and then try to come up with hypotheses for, well, what kinds of biological mechanisms might be driving that?

If you have indeed something, like a need, like hunger, well then there must exist somewhere in your biology, a hunger center, something that creates that drive for hunger and translates that into emotions that help you to fulfill that need. So, this all comes from this sort of background and what's happened over the past about probably less than ten years or so is a huge revolution in the creation of a new sub-discipline called computational ethology which aims to take advantage of modern advances in computer science, particularly in artificial intelligence and computer vision to attempt to automate and be more objective about quantifying motion by leveraging, again, these algorithms.

Dr. Biology:

You bring up the term A.I. artificial intelligence. And today there's both the promise and the. 

Talmo:

Perils.

Dr. Biology:

Perils. Yeah, that's a good alliteration. [laugher] Promise and perils of artificial intelligence and this is something that is curious. I don't think anybody believes that we're not going to continue down this path. The question is, how do we do a better job of managing what we're creating? And so, you know, there are terms that some people may have come across. Code bias is one of them. 

We have a tendency to generate algorithms, primers for the training for a computer that can bias a computer in a certain way. I'm going to ask both of you, because normally you're going to be from the pure science side. But before I get to you, I'm actually going to I'm looking at Valerie here because I don't know how much use time you spent with A.I. and it might be the last six weeks and you might be saying, no, I've been doing it for decades. I'm really curious about your vision of A.I. and in particular how this might impact dance in that sense.

Valarie:

Well, I think it impacts dance if it's useful. One of the things as an educator, I always strive to do with my students is to help them understand how something's useful to them. If it's not useful to them, then we don't need to be doing it. And that applies even in our classes. When I teach my students Labanotation analysis, looking at how we do something, why we do something. What are the characteristics behind the motivation to do it? We really have to understand why it's useful to them. 

So, I think if you're looking at artificial intelligence, it would only be from my background of moving. And how can it help us move more efficiently or more effectively? How can we perform with it? Over the years, I've seen performances that have been Oh goodness. Broadband or put through Cat five down at USC back in the 2004, 2005. I danced with a cyborg in 1999 down at UT Austin. I mean, I think there's lots of things that we do that are really interesting to advancing our creative activity. But at the end of the day, I think we really have to understand how it's useful and effective for us personally. And for us as dancers.

Dr. Biology:

Right. And we focused a lot on dance, but you did mention movement and efficiency of movement. And so, it's not all about dance. It's also about work, how you can do things safer and work.

Valarie:

Right. And actually, that's one of the reasons that Rudolph Laban, who created his systems of effort, Labanotation, and Labamovement analysis, even came up with a theory. He was asked to observe people working in factories and on the working lines of creating different things and was analyzing the movement to help them understand how to create the same movement more efficiently and effectively. And how to essentially, like, take out the time that it takes to move your arm forward and making sure you're using the right muscles rather than going around and using maybe your trapezoid and sort of tightening up and making it more slowly to move your arm forward or forward middle to pick up something. So, yes, once again, how is it useful, and is it effective for me as a person?

Dr. Biology:

Right. And I could even see it not only there for efficiency but also for safety.

Valarie:

Right. 

Dr. Biology:

You know, carpal tunnel syndrome, repetitive motion, we have to figure out how to address that. So, Talmo, you have to have been asked about your view of A.I.. What do you think? What's the future?

Talmo:

I'm generally in favor of A.I... I feel a little bit biased, of course, as a researcher, but I'll definitely acknowledge that in recent years, we've seen lots of examples of A.I. being applied to very nefarious ends. Right. And even perhaps when it's not intended necessarily to do so, I think the most prominent examples might be racial bias in facial recognition. That was certainly a problem. 

But I do feel like a lot of progress has been made in that domain. And I think at least within the science, within the research community of computer scientists, there is not only a greater acknowledgment of the importance of correcting for algorithmic bias but a lot more openness and frankly, even standards being set at different academic venues for reporting the bias metrics of your models. Right. 

Google developed this model card system where just like you have a nutrition label on your food. Now, the idea is that whatever you're releasing machine learning model, a neural network that can do something like facial recognition, you should also be reporting alongside it, not just its performance on your data set, but how well it's doing when you break it down by different demographic criteria. In that way, it kind of gives you a way to check yourself and be open about how well your algorithms are doing across different cross-sections of society. And I think that's going quite a long way towards improving the transparency that these algorithms can provide. 

Now, of course, all that does is it says, Well if I'm using a neural network to power my Snapchat filter so that I can get dog ears in my selfies, that hopefully, it'll work just as well regardless of my skin tone, but it doesn't change how people are going to use it to their own means. And I think that perhaps the most prominent example of this was in how A.I. was used to drive bias and misinformation in the 2016 U.S. presidential election, where it's now very well known that certain research firms were hired to essentially scrape Facebook data and leverage publicly available Facebook data to be able to predict people's psychometric profiles in order to give people a way to target population down to like very, very precise localization in order to drive political messaging. That is blatantly false. And it's kind of gotten us to where we are today in our political landscape. Unfortunately.

Dr. Biology:

Right. It's going to be something we're going to have to deal with...

Talmo:

Yeah.

Dr. Biology:

without a doubt.

Talmo:

Yeah. So, I would say at least, you know, in our domain of movement analysis. So, a lot of the progress in the technology for doing this, using computer algorithms, for tracking people's motions, you know, can come from some pretty dubious sources, even though the engineering advances are applicable across the board. But for example, a lot of work that comes out of China is quite advanced and they have really, really clever ways of tracking motion. 

But unfortunately, a lot of it is being applied in situations like tracking Uyghurs as they navigate public spaces. So, they can identify people that they want to target or track or censor, really, you know, eliminating the notion of privacy in public spaces and promoting censorship. 

But on the flip side, in our lab, we take the exact same algorithms, the exact same types of computer programs, and we use it to study how the brain works. We use it to study how different mutations can lead to cancer and how we can ensure that our treatments aren't harming people more than they're helping. And we use it to study plant movement and growth in order to solve climate change. 

So, it's always a double-edged sword, but I think this kind of derives a little bit from this idea that movement, biological motion is such a universal quantity, and all you're really doing is creating new tools to measure it.

Dr. Biology:

With this collaboration that you had. What are some of the things you've learned?

Talmo:

Yeah. So, as a neuroscientist, again, one of the reasons to study motion is that it gives us some intuition about what's happening inside the brain. And if you want to think of it, imagine coming home and you see your mom and she's got her hands you know, on her hips. She's got that stern face. And before she even says anything, you know, you're in for it, right?

Talmo:

You're in trouble. You know, she's mad at you. Even before she says any words. And so this idea of body language, communicating what's inside of your mind is really kind of what drives our work in studying animal movement, because we can't ask the animals what they're thinking either. So, a lot of our work has dealt with thinking about how we describe movement in a way that's richer and more expressive.

And even though we can use artificial intelligence to track movements and extract the locations of the body parts of our animals within our videos, that doesn't tell us really what they're doing. It just tells us where their body parts are. And going from motion capture to a rich description of movement turns out to be a pretty difficult problem because, you know, we don't often agree about what a movement is. We might not even recognize it even when it's human motion, as we learn from, you know, how difficult it is to become certified, to do a Labanotation and how much training it requires.

So, you might imagine how difficult it might be then to describe a movement of a fruit fly or a mouse that is not seen as much as human motions And so when Valerie and I started talking, I had this huge aha moment when she began to describe exactly how Labanotation gives you precisely this highly expressive language that allows you to reproduce what we can pull out from the motion capture.

But at the same time, it's a symbolic language, it's extremely systematic. It's been developed over many years. It was developed for, you know, optimizing it movements in industrial settings, but also has been used now to record and archive dances. We know that it's extremely expressive, robust, and capable of describing very complex movements.

And this is exactly the kind of language and system that we've been missing in the study of biological motion and in ethology and behavioral neuroscience. And so I'm very excited to be learning about how these systems for describing human motion works, especially because we're now thinking of a million different ways of then coming up with algorithms that can use the same kinds of systems and apply it to describe animal movements in ways that are a lot less biased than our previous system of sitting in front of a video of our animal and just trying to write down free form notes about what they're doing.

Dr. Biology:

Now, when I have guests on Ask a Biologist, you never get to leave without answering three questions. I'll start with Valerie. Was there an aha moment as you were growing up where you knew you're going to be a dancer?

Valarie:

I'm not sure there was an aha moment, but the moment I got accepted into the Juilliard School at age 17, I knew I was going to go.

Dr. Biology:

You're classically trained?

Valarie:

Yes. They took 16 people that year from around the world. And we were trained in classical ballet technique and pointe and American modern dance and world dance forms like classical Indian dance, Bharatanatyam, flamenco, jazz, and tap. So, we ran the gamut as people who can output any type of motion you throw at us.

Dr. Biology:

But before you got accepted there's that inner spirit, right? That's an inner core that says, Hey, I'm a dancer. Do you remember when you just realized that's my life?

Valarie:

It was always my life. From age five, it was always what I did. And in fact, even in high school, I didn't really have a chance to do much on the weekends. I was always being driven by my mother to Dallas, which was about 75 miles away. I grew up in rural East Texas, and I was in our local dance studio there. And at about ninth grade, my teachers told my parents that, you know what, I think she might have potential. So, you need to get her into a big academy in Dallas. And so, at that point, my parents sort of rearranged their schedules, and my mom drove me, gosh, five days a week, and she's an English teacher. She brought her English papers with her and graded and waited for me for 4 hours a night and drove back. And I did my homework in the car. So, probably from about 10th grade on, I knew that was probably what I was going to be doing.

Dr. Biology:

Yeah. Excellent. All right. Talmo for you. Was there an aha moment that you just said, you know, I'm a scientist. This is the way for me?

Talmo:

Um, yeah. I think it was a combination of different influences. So, I was born and raised very poor in Brazil, and we always saw education as a way out And there's a lot of socioeconomic immobility in Brazil. So, when I had the opportunity to immigrate to the U.S. in high school, I was extremely motivated and I always felt that science is going to be the direction I was going to go. But I think that the looking back, one of the core reasons why I ended up being a computational neuroscientist was really that you might not have expected it. 

And I'm sure you don't get a lot of these on your show, but I was a huge nerd in middle school [laughter] didn't get out a ton. And in fact, what I spent an extra portion amount of time doing in middle school was playing computer games. And at the time, I was playing these online RPGs with my online friends. And I started getting into computer programing around that time, like when I was 12 in order just to be able to program little bots and cheats for my game, kind of automate the gameplay. Give me a little bit of an advantage at the time. And I very much did not think that was going to lead to anything productive or not useful to science. 

But then as I came to learn, we actually need all these sorts of tools quite a lot in our inner field. So, I think that probably, you know, an aha moment was later on then in college when I realized that hey actually people need the computer algorithms to do a lot of these things and that's when I realized that, you know, all that time I spent in middle school that I thought was, you know being wasted on helping me play my games, using my computer is something that, you know, were skills that I could then use to help me play my, you know, science game today.

Dr. Biology:

Excellent. So, now I'm going to do the - I don't know if it's evil, but I take it all away. You're not going to be a scientist. You're not going to be an educator because a lot of my scientists love teaching. I'm giving you the ability to be anyone or do anything. I'm not giving you superpowers. I'm just giving you the ability to pick some other career you might have always wanted to do if you weren't a scientist.

Talmo:

Yeah, that's a great question. Well, I mean, it's a bit of a copout since I do, you know, computational work all the time. But I did consider for quite a bit becoming a software engineer of different sorts and thinking about it now. Maybe one thing that could be cool would be to apply this sort of like computational thinking to different fields. I definitely had some interest in computational linguistics for a time, analyzing how speech and language is produced, but from a computational perspective. And I think just generally building computer-based tools to interact with the world is just such a cool idea that I think I'd probably end up pursuing that kind of no matter, no matter which area to apply it to.

Dr. Biology:

But what if I was even more cruel and I took away the computer? [laugther]

Talmo:

Take away the computer oh, what is life Um, yeah. Well, if you don't think this way, then at the very minimum, I'd love to be a seaman to have a boat and, you know, have some sort of maritime-related profession.

Dr. Biology:

Oh, do you have a boat now?

Talmo:

I do not have a boat. That's definitely.

Dr. Biology:

I mean, when you're in San Diego.

Talmo:

I am in San Diego. They're quite expensive.

Dr. Biology:

You just get a smaller one, right?

Talmo:

All right. Yeah. Maybe I'll start with the dinghies. Yeah.

Dr. Biology:

Well, Valerie, you know what I'm going to do for you as I to take away all the dance?

Talmo:

No moving. [laugher]

Valarie:

No moving. [laugher] 

Dr. Biology:

Well, I'll let you move. No dance. And you don't get to be a professor. What would you do? What would you be?

Valarie:

Well, part of growing up in rural America without a lot of people around. It's like 20 miles to the closest gas station. Not even a blinking light in our road. I did have the opportunity to spend a of time on ranches and riding horses and participating in deherding cattle. So, I do enjoy being outside, and I enjoy the open sky and just being able to be around horses and cows and dogs, really. So, I think at some point I would love to be able to do that again if I couldn't dance. And you still feel like you're moving when you're on a horse. So, it does give you that sense of freedom.

Dr. Biology:

So you're going to be a rancher.

Valarie:

I don't know if I want to be the rancher, but I could definitely be a ranch hand ranching Okay. [laughter]

Dr. Biology:

All right. Well, yeah, that seems fair. All right, then the last question. What advice would you have for someone who wants to get into the world of dance because you're the dancer?

Valarie:

Yeah, I guess I would even just sort of open it out even more generally. And I tell my friends and my students and my family members, if they ask when they're trying to figure out should I really give this a shot? I always say if you're going to look back on it and say, wow, I wish I really would have tried that.

Valarie:

Then you need to do it. 

Dr. Biology:

Well, I like it. That's a tough one to follow, Talmo, but I bet you have some advice for a future scientist a future gamer that's out making bots and cheats for their games. [laughter]

Talmo:

Yeah, I think maybe a more positive takeaway from that is follow your passions but if I had to give, you know, one very practical piece of advice for any, you know, potentially aspiring scientist, it's, you know, regardless of your field, learn how to code. I thought that that wasn't going to be important early on. And, you know, I think throughout my different career stages, that's always been something that we thought that, oh, maybe as a biologist you don't really need to learn how to code and things like that. But every single place that I've been at, every stage, undergrad, grad school, or so forth, you think you cannot do science these days without incorporating computer programing? 

Having computational and quantitative literacy is crucial, even if all you want to do is pipette samples all day because, at the end of the day, that data needs to get analyzed. And the traditional model of just having sort of like an in-house statistician the numbers guy that you just had your data over and he'll give you back a figure, it just doesn't work anymore. You can't do your work at a reasonable pace without knowing how to work with numbers and work with data. And the beauty of it is that these days computer programing is a lot more accessible than it was certainly when I was growing up. 

There's so many resources available. Picking up Python is something that you can do as early as elementary school. I've had students in my lab who are sophomores at high school, but they started coding, you know, way back in middle school, even doing A.I, like little A.I. projects just kind of for fun. And it really doesn't take that much training. You don't need to have a degree in computer science to do computer programing. 

In fact, I often discourage people from taking something like an introductory computer science class if they're coming from biology or another scientific discipline and hoping to learn, essentially coding, scientific computing, scientific programming, programming, in general, is a very different thing than computer science and I think once you realize that, you'll see that the barrier of entry is much lower. You can just start looking at these tutorials online and figuring out how to code just to start doing some fun stuff on your own. 

You can start with a laptop and a webcam and start doing all sorts of fun little projects. Try to reproduce the filters that you have in Snapchat or Tick Tock or something. And you'd be amazed at how that those skills are going to transfer to science and you will be in extremely high demand. Believe me.

Dr. Biology:

I couldn't agree more And with that, I want to thank my guests for joining me on Ask A Biologist. And I can see from my watch we had fun with the conversation because we ran long, but I hope that's okay with the listeners. 

And in case you didn't know by now, you have been listening to Ask A Biologist, and my guests have been computational biologist, Talmo Pereira, a Salk Fellow at the Salk Institute for Biological Studies. And Valerie Williams, a professor in the Department of Dance at The Ohio State University and an expert in Labanotation. 

For this episode, we will be including a lot of links and images and the episode notes and transcript. This includes information about SLEAP and Labanotation. For the explorer and experimenters out there, you can download the software and try some experiments at home or your school. Maybe there are even some hidden behaviors your pets have that you can use SLEAP to discover. 

The Ask A Biologist podcast is produced on the campus of Arizona State University and is recorded in the Grassroots Studio housed in the School of Life Sciences which is an academic unit of The College of Liberal Arts and Sciences. But for this show, we're at the annual Research Conference for the Society of Integrative and Comparative Biology. And remember, even though our program is not broadcast live, you can still send us your questions about biology using our companion website. The address is askabiologist.asu.edu, or you can just Google the words Ask A Biologist. 

As always. I'm Dr. Biology, and I hope you're staying safe and healthy.

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Bibliographic details:

  • Article: SLEAP and the Science of Movement
  • Episode number: 115
  • Author(s): Dr. Biology
  • Publisher: Arizona State University School of Life Sciences Ask A Biologist
  • Date published: April 9, 2022
  • Date accessed: September 23, 2022
  • Link: https://askabiologist.asu.edu/sleap-and-science-movement

APA Style

Dr. Biology. (2022, April 09). SLEAP and the Science of Movement (115) [Audio podcast Episode.] In Ask A Biologist Podcast. Arizona State University School of Life Sciences Ask A Biologist. https://askabiologist.asu.edu/sleap-and-science-movement

American Psychological Association. For more info, see http://owl.english.purdue.edu/owl/resource/560/10/

Chicago Manual of Style

Dr. Biology. "SLEAP and the Science of Movement." Produced by Arizona State University School of Life Sciences Ask A Biologist. Ask A Biologist Podcast. April 9, 2022. Podcast, MP3 audio. https://askabiologist.asu.edu/sleap-and-science-movement.

MLA Style

"SLEAP and the Science of Movement." Ask A Biologist Podcast from Arizona State University School of Life Sciences Ask A Biologist, 09 April, 2022, askabiologist.asu.edu/sleap-and-science-movement.

Modern Language Association, 7th Ed. For more info, see http://owl.english.purdue.edu/owl/resource/747/08/

Inspired by Hollywood motion capture technology, SLEAP captures animal movement and behavior for the tiniest animals.

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