Atilla Szantner: My name is Atilla Szantner and I’m the CEO and co-founder of Massively Multiplayer Online Science, also known MMOS, and we are a small Swiss start-up who came up five years ago with this idea of connecting citizen science with major triple-A video games.
And so we are kind of the link between the scientific world and the game developers.
Jerome Waldispuhl: So I’m Jerome Waldispuhl. I’m an associate professor of computer science at McGill University in Montreal, Canada, and my lab works in buying my leagues. Typically, we’re developing algorithms to study biological data.
And I’ve been interested for the last decades, actually, in developing games or programs too, that allows people to contribute to research and help to enhance the performances of algorithms for the analysis of biological data.
Ryan Brinkman: I’m Ryan Brinkman, I’m a professor in medical genetics at the University of British Columbia. My day job is as a distinguished scientist. That just means I’m old, I guess, at British at the British Colombia Cancer, RBC Cancer.
And my background is flow cytometry bioinformatics, which is the flow cytometry data is the focus of the data that we’re pushing through today.
Alexander McNamara: And so we’ve got lucky to have all three of you on today because you are all working on Project Discovery. I just wonder if you’d be able to explain to me what you know, what the project is, where it has been and what the what you’re working on now.
AS: I’ll start a little bit about talking about the history of Project Discovery, because this is a story which is going on for five years now and basically five years ago, we started with a very specific idea, which is connecting citizen science, which is crowdsourced scientific activities, with video games. With triple-A video games with already big, already established player communities.
And the motivation was to solve the long term engagement activity problems with citizen science. But that was our initial idea. That’s why we created our small, small Start-Up. And we connected or contacted the CCP immediately five years ago. CCP was super interested, intrigued by the idea and receptive to it. And we set up Project Discovery.
The first project Discovery was together with the Human Protein Atlas. Then in 2017, we started to work together with the University of Geneva to search for exoplanets in the game. And just recently we started a new project which involves flow cytometry.
AM: And so what was it about that drew to computer games that made you think, OK, well, this would be a really good way that we could use gamers to help, help us solve these scientific problems?
AS: So I think I think it’s unquestionable that video games are the most engaging form of entertainment of the day. We see how, how people are using more and more video games, how more and more people have access to video games. It’s incomparable to any other sort of medium that we digest.
Also, it’s a very active format or form of entertainment. So it gives you a special, special possibility to integrate something where you want the players to interact with your problem.
Basically, what we’re doing is we are approaching online communities, in this case, gamers, and we try to smuggle some real-life activity into their everyday digital life. And that’s what Project Discovery is.
AM: And you say that there’s been two projects so far that have you know, you’ve started on those two projects and you’ve got to what were they, you know? How did they go and how did you come about creating these projects before?
AS: The first move was together with the Swedish research team, the Human Protein Atlas. They are curating a big 15 million image database. It’s an open database for researchers of microscopic images of cells and tissue samples. And, well, the first setup was an experimental one. Nobody has ever done this in the video game industry.
So we’re looking around in our, through our network, in the research institutions to find good candidate projects for citizen science activities. We tried to convince game developers to embrace this feature and, you know, implement these kind of mini-games in their in their virtual worlds.
And so the whole process was sort of experimental. We ended up with a very interesting setup where us a small entity created a sort of middleware which standardises scientific data and gives an API to game developers, which has proven to be a really good approach because we could offer this service to other gaming companies. Like recently, we started our collaboration with Gearbox.
And so after the inception of the idea, less than a year later, we already launched the first project together with CCP. That was Project Discovery proteins. It was running for one and a half year roughly. We collected 33 million classifications from players, which is just mind-numbing. It’s really unprecedented. Then we switched to another project together with the University of Geneva, which was introducing light curves in the game. Life curves are these, you know, luminosity versus time graphs of stars where we ask players to look for dips in this curve, which might signify a transit event and which might which might lead to a discovery of a new exoplanet around that star. And there we collected more than 250 million classifications. Again, it completely incredible.
And this Monday, that’s when we started our new project with this team, who are here today and whose aim is to use the flow cytometry technology to get a better understanding of it, about COVID.
AM So, you know, that’s a big goal that we’ve got there. The first one is the human genome. The second one. You’re just searching for exoplanets. And now you’re using the technology and the gaming mechanism. What is it exactly you’re looking for a cure or information or, you know, things that can help produce a vaccine for COVID-19?
RB: Maybe I can talk a bit about that, because we’re on our side, we’re being we’re on the biology side. So there’s a couple things going on, you know. One of the challenges with this data It’s really complex data. It’s 20 dimensions. And the scientists have to go through this data because we’re limited to looking at data on either on screens, either gaming or the scientists are doing the same kind of thing. And so with these computer screens, we only see two dimensions at a time of this 20-dimensional data. So it’s, they’re trying to navigate through this at two dimensions at a time. And it’s a really complex analysis.
And it’s also there’s some subjectivity. Subjectivity and how the scientists do the analysis, because essentially they’re drawing circles around dots and they so there can be variability in scientists. And it’s also time-consuming. It could take anywhere from five minutes to maybe an hour and 15 minutes to analyse just one sample. And to get the answers that they want on that one sample. And if you doing a clinical trial, you may have thousands of samples just in one trial and is in the news in all these companies right now are trying to develop vaccines and therapies for COVID. And so there’s hundreds of these trials going on at a time.
So it’s just masses of amount, amounts of data. And we know from work we’ve done before with other groups that when they do these clinical trials, they can take them up to three months to get the data back from the analysis once they come off the machine that analyses this.
And when people are dying, at a very alarming rate, you know, waiting three months for data to come back. It is it’s really not an option.
And so by leveraging this crowdsourcing, I’m one of the things we can we hope is that by putting this infrastructure in place, both for this pandemic and for the next one, we know this is not going to be the last pandemic we will ever face, that there’s a possibility to get answers much quicker and that’s gonna be lifesaving.
So that that’s one of the early goals. The other thing is it’s a very again, this complex data set. I said it’s, you know, 20 dimensions that people are trying to go through. And so scientists are limited in how much of that space they can actually explore both based on time, they just want the time to go through 20-dimensional data, trying to explore it slowly, hundreds of thousands of different plots on the screen that they have to go through. For one sample, if they want to explore the full dimensionality of that.
And so by leveraging this crowdsourcing, we have ability to really, you know, with, you know, 20,000 eyes on one single data set, you’ve the ability to generate much more data much more quickly.
And with that data, we have the opportunity to find discoveries that would have never been found with traditional analysis, because we’re just mining the data much more effectively. So we can discover things that are happening in the immune system. We talk a bit more about the biology that would have just been missed. And we can discover it much faster.
And the other thing is the data that can be generated from this project will help us develop better tools and algorithms to do this the next time around. There’s been lots of computer tools that developed to do this analysis, but when you developing these machine learning kind of algorithms, it’s really dependent on the training data you have to develop these machine learning algorithms, how well they’re going to perform.
And there’s a really a dearth, an absence of training data that help us develop these algorithms. And we’re going to generate that kind of data in spades with this project.
AM: I mean, there’s a lot so there’s a lot to take in there. I mean, I guess one of the first things that we should probably establish is, you know. What game is it that that people are playing? And what is the game mechanics that they’re doing? And then obviously, how does that translate into all of this data? This, you know, scientific data that you can then use.
JW: Well, the problem we try to address here is commonly referred to as the clustering problem. The idea is to try to group data into two plots that, that are similar or that should belong to the same entity. So that’s the game as it is presented. It’s an image when there’s plenty of dots inside, but some region are more dense than others and that the density, the shape or just the shape of distribution with data tells us if these things should go together or should be grouped into a different cluster, as we name it.
And the purpose of the game is we show you this image and you have to separate the regions of this image to say this is one group of dots, this is another group of dots. And by the description I’m just making here, you understand that it’s it can be sometimes very intuitive and there’s no perfect algorithm for data mining. If the cluster should be big or rather very small or tight and so on and so forth.
So we rely a lot on the visual perception of people and seeing how these things are distributed on your screen and afterward on the agreement that many players will have together to try to extract a consensus and capture the wisdom of the crowd. Data mining, if there is indeed a group of things that should be grouped together here, are not.
So the problem as I frame it is very general. And that’s why my group has been working on it for a while. But I think it was about five years ago, something like this, Ryan shoot me an email saying, OK, what you’re doing here is cool clustering and I think have the perfect data for you. And we realised at this point or Ryan explained to me all the all his work and realise that different clustering is perfectly fitted to the flow cytometry data analysis that he’s doing in his lab.
AM: So you say flow cytometry. Can you just explain what flow cytometry is? Because I’m that sounds like it’s a pretty critical part of this.
RB: Yeah. And not a lot of people have heard about it. Even I didn’t hear about it when I when I started my job at the Cancer Agency and I saw what these people are doing. And it’s like, what the heck is that? It looks kind of interesting. And why are they doing it? Because it looks like something computers can do.
So the way the way this technology works is you sneak up on someone, poke them with and you don’t take their blood. And the reason why we’re using flow cytometry to look at these, these blood samples. It’s the word flow cytometry. Where cyto means cell and flow is things flowing in a liquid. And so the flow cytometer is ideally suited technology to look at different cells in the immune system and what they are.
And so we have, you know, in your white blood cells, you have the cells that you’re the white blood cells that you’re born with that give you some of your innate immune system, the immune system you’re born with that just recognises things that are in your body that aren’t you that shouldn’t be there. It helps your body attack that.
And the other, you also have the different cells in your blood that recognise oh, I’ve seen this infection before. I see it again now. I can mount a very rapid response to that.
And so there’s many different cells that are present in your immune system that mediate are important for attacking these infections such as COVID. And a lot of the problems that we’re seeing in COVID right now is this huge inflammation in the immune system that they call this a cytokine storm. And so your immune system just goes crazy trying to attack this virus.
And so with flow cytometry, we can look inside the blood of these people and see what’s actually happening at the cellular level, what cells are changing, what cells are disappearing, what cells are being attacked, what cells are activated. And we have to find these different cell populations in this 20-dimensional space because there’s so many different kinds of cells, immune cells in your body.
And this flow cytometry technology lets us pull out the specific ones that are changing under different conditions that she is used for cancer. It’s used for HIV.
Any kinds of diseases of the immune system are using this technology. Now we’re applying this technology to COVID to help solve this problem and to develop therapies.
AM: And so, you know, I guess the technology of how that works, specifically, you’re just collecting the data and then you’re feeding that into the game and then letting it loose to the players within the game to then have a look and say what you know, where it looks like this clusters of data. Exactly.
RB: Exactly. We’re taking them, the scientists. Well, the data’s really complex. So, as I mentioned, it’s 20-dimensional data and there’s a lot of biology that’s involved in. And when the scientists are looking at this data because the biologists understand that we think in this disease that natural killer cells might be important. And so they’re really going to be focussed on trying to find this one particular clump of cells in this huge dimensionality data. And so they’re designed very quick ways to navigate through this data to find these one or two or three or 20 populations that they’re interested in.
Gamers don’t understand immune systems in the different cell populations. And what’s important and that’s OK, because this is the cool thing that working with Atilla and Jerome, has happened, is they don’t have to because well, because we have the power of the crowd, we don’t have to shoot them through and explain to them what path they have to navigate through this 20-dimensional data because we have so many people, we just say here is everything. Find everything in all possible combinations and then we’ll sort it out later.
And so that will give them all the different projection of this data to look at and they’ll find everything. And so they’ll find the stuff the scientists want to find. They’ll find stuff that is probably not important because has nothing to do with disease. But the cool thing is they’ll find stuff that scientists might not have looked for because they’re because of time or because they’re looking where the light is. And we’ll have the chance to discover this.
JW: I just thought maybe if I just on top of that, I think that’s very important what Ryan just said, because through crowdsourcing, we’ve be able to have a look at his data as it has never been done before. And potentially what we hope is like we have, we find a better way to navigate these data, to be able to extract its full potential and eventually realise the promises that all this technology can deliver to us.
AM: So I guess it’s a case that when you when you collect the samples and the data, all the data, that you just have no way of filtering through it and you’re using the power of the people who are playing the game to be able to filter that for you in a way that you hope. I guess you hope in an algorithm or an AI would be able to do in the future.
RB: Exactly. And there are lots of algorithms that have been developed so far today, and they’re really great for doing discovery kinds of problems where you say, I want to know what’s different between all the sick people and the healthy people.
But when you try to apply those same algorithms to do clinical reports, to say, I’m really interested in finding NK cells because these algorithms are, for the most part, unsupervised, which means they try they don’t they’re not giving a lot of information about the data that they’re looking at when they’re trying to find these clusters. And so they have to adjust for the size, shape and distribution of all these different cell populations in very high dimensional space.
And all these cell populations look a little different in how they’re stretched out or squished. And so these uncivilised algorithms, really, they’re they don’t have the performance when you try and do clinical reports, and so they’re not really used for that.
But human eyes or our eyes are designed to find patterns. And we, you know, we’ve evolved to detect patterns. So we don’t get eaten by tigers.
We’re trying to apply this really awesome pattern recognition that humans have to datasets to help build better algorithms to learn how much how humans have been trained over millions of years of evolution to find these patterns and then say, hey, look at where these humans are doing. This is how they find these patterns and show that to computers and go. Oh, I get it. And then they’ll go off and do the same kind of thing.
AM: And presumably for that’s what you need to a significant amount of human eyes looking at that data.
RB: Yes, we need lots of data. And we also need them to do a good job, because if the humans are gaming with one hand and shooting space aliens while at the same time to try and draw clusters around dots, we may not get the best data. So we need lots of data and we need lots of good data.
So one of the things that CCP and the groups, they have been involved in teaching the laypeople in the gamer’s how to do the job that scientists are doing. And that’s, that’s really interesting.
AM: So how do you go about choosing a game to feed this mechanic in? And then once that’s in, how do you go about making it accessible to, as you say, people who are shooting starships to then go off and, you know, be scientists, as it were?
AS: So, you know, in the last five years, my big part of my job was to go to gaming conferences and events and talk to game the developers and publishers and convince them that this is something super important. This is amazing to do. This is a lot of fun, working together with scientists, bringing this to the player community. And it brings a lot of value to the game.
And so mostly we were we’re aiming for bigger games because as Ryan was saying, we need a lot of players, a lot of activity in this citizen science projects. In general citizen science is based on this idea that we have to hand out the same tasks to many people, and then we do some sort of aggregation or a measure of the whole thing to see who are the guys who are off and who other guys who are doing a good job, too. And this way we can we can provide very high-quality output.
So we are looking for bigger games with potentially hundreds of thousands or millions of players. In the case of Eve Online, Eve has roughly half a million active players every month. So it’s a big game. We have a big player community who can contribute to this project. Not to mention that it is you know, Eve player players are crazy, crazy, interesting bunch of people. They say that 99 per cent of them are science buffs and they used to solving very difficult problems. They love to be challenged. And they really welcome the Project Discovery.
The other project that most recently was together with Borderlands, which is a completely different kind of game, a bigger game, but the much, much diverse audience. And again, there we work together with Jerome to bring in other scientific problem there. But that’s one thing.
Also, we have to find places in the gameplay where such an activity works. So, for example, in Eve Online, there is a lot of idle time. You’re waiting for your buddies to come with their spaceship to fight. And while you’re waiting, you just open project discovery and solve a couple of task and this stuff takes, I don’t know, ten, ten seconds, 20 seconds, something like this. So every time you have a little bit of free time, then you can contribute to scientific processes.
This might not work that well in another game. Like if you take League of Legends while of playing. You know, you need a hundred and fifty per cent of your attention. There’s no way you’re going to solve any problems while you’re playing. Although, you know, if you take these more big games like League of Legends, there is always this network synchronisation part where we’re waiting for half a minute or two minutes for other players to load the client. It would be a perfect place to add some scientific activity to contribute something really meaningful, in the meantime.
Yeah, yeah. That’s that. These are the games that we’re looking for who can provide massive, massive crowds.
AM: And I guess the hope is that they the same enthusiasm that the Eve Online crowd players took to the previous discovery missions, they will take that to the new one, especially, I guess, given the fact that there’s a global pandemic on at the moment.
AS: I think I think this is a this is an extra layer of motivation, so to say, simply because the situation what we are in as humanity.
In general is really interesting, because there has been extensive surveys of why people participated in the first place in any citizen science activities, not just the games. And these surveys show that the primary motivation is to help scientific progress. I think it’s super important because that’s a very solid foundation for this project that people want to help. There is an intrinsic motivation.
Of course, in games, you know, putting or taking away this effect, what we have with COVID right now, we have this additional layer, which is rewards, in-game rewards are very attached to your two or favourite virtual world that you’re playing with. And we are sort of thanking players for contributing with in-game rewards, which connects this whole activity to the to the bigger picture, to the lore, the narrative.
AM: So this is sort of dual psychological effect that not only helping the planet, you’re also getting some in-game rewards out of it as well.
AS: Exactly. Exactly. Well, you know, it was really interesting because with the first Project Discovery, CCP game designers created a new in-game currency for just Project discovery, which was called Analysis credits. Now, you could use this Analysis credits to go to a special shop, buy something and then sell it on the market and get the main in-game currency, which is Isk. What we’ve seen, there were many very active players who never bothered to spend this Analysis credits, which is a clear sign that they were not doing it for the reward, but rather for because they wanted to help the science.
RB: And the uptake has been incredible when they when they pitched, when we start talking about how much how many answers we could get. I was that really it? And I wasn’t sure. And I think so far we’ve exceeded expectations. I think that you guys have a bit more. And so the numbers, I think we had like 500,000 puzzle solves on the first day.
AM: So as a scientist, you must be as serious as a scientist. You must be really, really thankful. This is huge amounts of data that’s coming in almost instantaneously.
RB: It’s actually we’re a bit worried that we’re running out of data. And so my team is busily trying to get trying to get more data as we speak because we have to keep that bucket full.
They’re just going at through so fast. We thought, oh, that’s be OK. Well, a couple days to generate the next round and is like, oh my gosh, you’re running out.
AM: So I guess this is great news, obviously for you to have all this data. So what do you know? What can you do this? Now you’ve got this data. Now, what can you do with it now? And how long will it be before you can actually say, OK, these. This is the science coming off the back of, you know, players of a game doing some spare science in their time.
RB: Science is a long road, and it’s frustrating for people. It’s like, why don’t we have a vaccine yet? Just to design these flow cytometry studies takes weeks easily. And then you have to go to the patients and poke him with needles and collect all their blood and have them get ethics approvals for what you’re doing. You have to organise scientists in clinicians. And so just to generate this data is a massive amount of time. And so a lot of these studies are just starting out.
So we have this infrastructure in place and some of the data is starting to filter in. But these are the first studies. We now we have data from the very first outbreaks in Italy. That’s a data that we’re getting today. And that, that story was weeks ago, months, a couple months ago, really. Right. And that data is just now filtering into our system. And so a lot these clinical trials, we don’t have the data yet. They’re just getting up to speed.
And once we put this data through the system as Atilla has mentioned, we have to massage that. We have to take, you know, hundreds of 100 players analysis of the same dataset and figure out what the best way to put that together to get the one best answer. We’ve never done that before. So we’re developing the algorithms at the same time as the players generate the data to help us understand what they’ve done.
And then next thing, we have to start looking then after that’s all done. We had to tease apart these massive datasets to try to find out what’s changed. And also trying to overlap that with what the stuff the scientists has looked for.
So, so we’re not going to get any answers next week and probably not even next month. Very early on, we’re gonna be able to give the scientists data to help put that in their hands as soon as quick as possible so they can put their expertise on this soon.
So one of the big ideas we have from this project is not just the people that we have here involved in looking at this data. All this data is can be made available for everybody to look at in the community. Other scientists, not just ourselves. We’re gonna expose everything in an open science kind of way.
So we’re crowdsourcing the ideas. We’re in a crowdsource scientists to look at this data that we’ve crowdsourced gamers to look at. And so hopefully with more eyes, more scientists’ eyes on this data that more gamers eyes have looked at, we can get to the answers quicker. But it’s science and we have no we don’t know when that next discovery is going to come.
JW: And if I can just add on top of this, it’s like what is very interesting, I think in a project as well as you can guess from what Ryan mentioned like there’s different levels. I mean, you said that there’s the urgencies of finding something for COVID, and something like this, and we’re trying to build a technology to invent this, but we have to get prepared also for that for the next outbreaks potentially and all the technology and the infrastructure that we’re building here potentially can be very can be reused afterwards to apply this technology much more faster, for using flow cytometry, at a large scale on emergency uses.
And so here we’re speaking about COVID, but it can be another SARS later. And Ryan’s using that for cancer, cancer patients. You can make a breakthrough in terms of how we use this technology in day to day.
RB: But there’s a general problem that scientists, you know, everyone’s been asking, when is the cure going to happen and when’s the next big discovery? I think no scientist will ever tell you, oh, we’re gonna have the answer for you next week. It’s a science. It’s if we knew how it was going to be done, it wouldn’t be science. Science is about discovery. Right. And so things can happen very unexpectedly.
You know, you look at that, that bread on the table and see there’s mould growing and then, boom, you’re done. And sometimes these things take years. What we’re looking for that mould in COVID, and there’s some things that we need to look for and we hopefully get answers very quick, but it’s not going to be by the time this podcast goes live.
JW: And this is why the key to what CCP does, is really groundbreaking and changes the way we can potentially do science on large scale. Right. As Atilla was mentioning earlier, it’s not the first project they’re engaging, but they really built an infrastructure. How can we accelerate science by engaging all the communities of gamer there? That turned out to be a very enthusiastic science and very skilled in many complex tasks.
AM: Yeah, it seems like this is something that obviously that the work you’re doing is groundbreaking, but seems like something that could be applied to lots of other things going forward. And, you know, at the moment, you’re using this to you say you search for exoplanets and now you search a cure for COVID. But you know, what other applications could we be using this crowdsourced citizen science for? What other things are out there that we could put this application of citizen science towards?
RB: So besides other applications flow cytometry data is used everywhere. It’s for cancer is for disease of the immune system. We have cures for cancer now that have to be developed one patient at a time. And so aside from COVID, it is just so much flow cytometry data that that’s affecting so many people. Anybody who has leukaemia, lymphoma, it’s everything that’s shot through flow cytometry
So this technology has been put in place for COVID but there’s so many diseases are affecting millions of people that we can apply this technology for. Aside from the citizen science stuff that Jerome and Atilla can talk about.
AS: I think, generally speaking, in life sciences, we see a lot of cases where the data is relatively easy to acquire or well, easy is probably not the good word because it’s really, a bleeding-edge technology that they use. But let’s say we can acquire a very large data sets.
On the other end of the story, we start to have very good machine learning algorithms. But as Ryan was explaining, we need that link to that. So we need training data to make these machine learning algorithms really effective.
And that’s what players can provide. So we can feed this big data sets that comes from life sciences and then and then improve these machine learning algorithms for life sciences.
JW: Yeah, just who basically emphasise indeed, what Atilla said, now we see we have the emergence in the last few years about the broad use of the machine learning and AI in many different fields. Is a very powerful technique, but they always rely on a large data set they can be trained on and potentially, I mean as large as possible to make the technique reliable.
And this is a very timely project that we had to put on because for many problems in complex in life science, indeed, we’re missing this data that needs the workforce of a scientist. Spending hours doing these things by hand and at very small volume and the infrastructure that was deployed by Project Discovery basically enables us to scale this. To scale this up to the level of very large communities and really unlock the potential of artificial intelligence techniques.
RB: So basically, anytime a scientist is looking at a picture and is doing interpretation of that, that kind of that data is applicable to this crowdsourcing. So, you know, x-ray images or anything like that. At the same kind of ideas apply where the human is interpreting any image to get an answer. Those are complex problems for algorithms to do. And this is kind of thing that humans can help train those algorithms.
AM: Do you think we’d ever get to the point where the algorithms would start to be able to learn from that and get better themselves without having the need for quite a huge number of players to be able to look at the data?
RB: I think trying to identify in Instagram pictures if the person is holding a cat or a dog, the kind of information that you used to make those kinds of decisions is very different than if you’re looking at a flow cytometry data where we’re trying to do dots in space.
And so you really need to have algorithms trained for the type of data that you’re looking at, otherwise, it’s not going to perform as well.
AM : And so, you know, obviously, you said that it’s not a you know, science is a long process and it and it takes its time. But, you know, you’ve. The outbreak of coronavirus has been a recent thing. It’s only been, what, since the beginning of the year. I think the point I was looking at is obviously you’ve got this technology now we be able to swiftly move on. You know, if the data changes. So, you know, we discover something that we haven’t known before about COVID 19, or will you be able to adjust what you’re doing to build sort of hone in on, actually, this is what we need to target on?
AS: I think I think it’s this third edition of Project Discovery was very interesting from that perspective. From the very beginning, CCP had this approach that they wanted to create sort of framework inside the game that is capable of taking citizen science, microtasks and showing it to players.
And because they knew that, you know, it doesn’t matter which project we start with we’ll eventually change it to something else. So in the game, we already have sort of infrastructure.
As I was saying on our side, we from the beginning created an API for game developers. So that piece of software is continuously improving, but that’s also in place. So right now, what we’ve seen that was quite amazing that basically in a couple of months time, we managed to bring it into the live game from having the idea of using flow cytometry data to help these research efforts. And that’s I think that is something very, very special.
RB: And not only did they develop the interface to it, they made it look really cool. And so that’s important. You know, if you just just have a boring, you know, run of the mill interface that scientists are used to, it is not going to be engaging. But they made it look really cool.
And so if you’re in the game, it’s like this is this is it’s fun. They make it fun and interesting. And they and they tie it to the game. They just the way they present the palettes of colours and the extra graphics they show on the side, they spacified what scientists are doing in their cool laboratories and made it look like a game. And it is doing the same kind of thing that the scientists doing in the laboratories.
AM: It is just amazing to see is that kind of the key to making these citizen science projects work, making it seem like really natural for the environment that they’re in?
AS: That’s exactly because I think that’s the power of this whole setup. I mean, this is why we started in the first place. We knew that if we managed to do something like this, this is sort of very organic integration, so this task becomes part of the game, part of the virtual world that you love so much that you spend so much time in that is connected to the lore and the reward system, then we can have that kind of activity, what we see actually in Project Discovery and these citizen science projects.
RB: They even took one of the scientists that developed the data, Andrea our collaborator in Italy who provided some of the COVID data, they actually took him and made him an avatar in the game. And he walks through the players teaching them how to do the analysis.
And I saw that and am like peace out. I’m done. That’s the coolest thing I could ever be involved with. It was just such an amazing thing to see. And so they’re really integrating the science directly into the game. You’re having the guy who’s he’s a frontline clinician who’s working on COVID data, teaching the players how to do his job. How cool is that?
AS: It’s really interesting what Ryan was saying, because I think that’s an important part of this project, is that it’s not just about the data, because the data is all of this super interesting and very valuable for research. But it’s a unique opportunity to do science outreach.
Just think about this project. I mean, Ryan said even he didn’t know about flow cytometry when he started to work at the lab. Now we have potentially millions of people in the game and through these interviews who learn a lot about flow cytometry, why is it important that these kind of this kind of knowledge or this kind of science outreach helps these research efforts, because then they have much more sort of support from the general public.
And I see with all these project discoveries, we always have researchers very involved in the process. For example, the last one with exoplanet research, we were honoured to have Michel Mayor to be the face of the project. So you also got an NPC in-game, a non-player character in-game, but he also came in-person to Eve Fanfest first, which is the annual player convention in Reykjavik, where we have three or 4,000 players coming every year. And he gave a presentation about how he discovered the first exoplanet, you know, for which, by the way, he got the Nobel prize last year.
And I think this thing that the researcher of his calibre is coming to gamer convention talking about his research. I think it clearly shows that scientists taking this opportunity very, very seriously.
RB: Anything we can do to help everyday people understand science and help them see that this isn’t fake science. This is how discoveries are made. This is where, this is how data is generated. This is all that’s involved.
A better appreciation for what’s what scientists are doing and how complex it is and the and the work that’s done to make discoveries. I think it’s going to, it lifts up everybody, and especially when you can get them involved. It’s just such a fantastic thing.
This podcast was supported by brilliant.org, helping people build quantitative skills in maths, science, and computer science with fun and challenging interactive explorations.
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