 Okay, and we do. Hi, hello everyone, and welcome to another SysSexVision talk. As usual, I would like to remind you that this talk is part of the WorldWrite NOIR Initiative. It's a global platform created already one year ago, and it has allowed us to continue exchanging or work and ideas during the pandemic. There are many live talks and podcasts on various topics. So just take the time to go on the website, check the different folks, and make sure you will find something of interest for you. Today, we would like to offer you something a bit different than usual, and something that will not only solely cover vision research. Today, instead of presenting data or sharing our newest results, we would like to engage our audience with the topics that we hold dear. It's a topic that we consider necessary for the good development of research and science in general. It's a topic which is known by many, but still underappreciated. So if you click on this video to join us, you know that today we're going to talk about open-source hardware. And to do so, I am very glad to be able to receive my friend and colleague, André Mayas-Chagas. So André studied biology at the University of São Paulo in Brazil. He then moved to Germany, where he obtained his neuroscience master, and then more recently his PhD, which combined neuroscience and open-source hardware. It was mostly based on open-source hardware, to be honest. For years, he's been volunteering at Trend in Africa, an NGO charity, which supports scientific capacity building across Africa, where he operates as an open-science coordinator. He is now a research bioengineer at the University of Sussex, where he provides support to research labs in the neuroscience department by developing various research equipment. Also on his spare time, he offers consistency services around open-science hardware through the platform, Prometheus Science, is also an editor at PLOS for the open-source toolkit. And to finish, he started Open Neuroscience, a network of collaborators trying to keep track and create interesting open-source projects relating to neurosciences. And these we're going to discuss a little bit later. So, hello André, how are you doing? And first question, where do you find the time to sleep? Hi, Max, hi everyone. First of all, thanks for making the time and the space for me to talk about something that is mentioned I hold quite dear, which is open-source tools and open-source hardware for research. I guess leaping is for everybody overrated. And I mean, all of these, like I started and I work on, but these are all collaborative efforts, right? So it's not just me, but a bunch of wonderful people all around the world who are actually pushing these agendas forward, right? Very well. So, let's start. So, André, we keep hearing about open-source, open access, open science, et cetera. Can you give us a definition? Can you describe us what is open-source and in all case, what is open-source hardware? Yeah, absolutely. So, open-source, there is a very formal definition for it. It started with software back in the 60s, but how I like to describe it that it's easy to understand is basically something that we've always been doing, right? So you, and you can think about this when you, let's say you cook a certain recipe and you write down like what you cooked and you have your recipe book and so on and you wanna share this with others. But as you share the steps to make a recipe, a cooking recipe and you're sharing this in a way that you tell people you can do whatever you want with this, you can cook it yourself, you can give it to another friend, you can actually bake cakes and sell or you can make this recipe and sell it for a little bit of money, you can alter the recipe. This is basically open-source, right? So open-source is this idea of take the blueprint of something, it could be code, it could be equipment, it could be a cooking recipe and shared with others using the certain guidelines that like you showed the screen. And then of course hardware has its specific guidelines and software has its specific guidelines and so on. But the point is we share an exchange knowledge by sharing the ways we've done things. Yeah, I think this is my definition of open-source. So it's technically an exchange of resources and knowledge about a particular topic. And exactly, right? And if you think about academia, this is super fitting to academia, right? Because we have like all of these public-funded institutions and all of this money that's poured in from taxpayers' money. And we should basically freely share this back to the public because they paid for this in the first place anyways, right? And there were a lot of, there is a lot of proof that sharing things freely in this way, be open access or be open-source actually speeds up the pace of innovation and increases the replicability of science and makes things actually better. Yeah, okay, that I fully agree with. But here we're mostly talking about open science, let's say. In our case, why should we care as scientists, as a researcher about open-source hardware? Why should we put such an interest and investment onto it? So I mean, there are a lot of reasons why and a couple of them are basically A makes you a better researcher in the sense that if you understand your tools, if I have something that it's open-source, I can read the design files, I can learn everything in there, how it works to minimal details, right? And if I know these, I know exactly what my machines are able to do because nowadays it's impossible to do science without equipment. And so if I know what they're able to do, I know exactly what kind of data is coming out of them and I know if they're out of calibration, I know if they're not behaving the way they should, right? And so this is the first thing. So you can nicely know when your devices are properly working. This is one, but then two is if I know exactly how something works and I have a different question that I want to ask, I can easily change this device to actually make it record the data that I needed to record for my new question. So in a way, you as a researcher, you become able to ask questions and adjust your equipment around it and not the way around where we have nowadays oh, I have this two photomicroscope that is only able to do let's say four hertz per as a recording speed, right? And so if I know, if I have an open source version of my microscope and I can actually change the frame rate or put faster Galvo mirrors or something like that, I can actually then say, okay, I can now record from this cows, like this new indicators that are faster except of being stuck with my four hertz black box to photomicroscope that I bought off a company that doesn't share with me the inner workings and the design files. Yeah, but in your example, you're taking something which is high tech, which is something very specific that not everybody will be able to access. Will you have an example of, I don't know, some things that in common lab you will be limited by and invest time in as a reproduce or develop? Absolutely. We have, I think, two very good examples of two things that are used a lot in research labs and they're namely, so one, micro pipettes. And as we know, these needs micro pipettes are super used in cell biology, in molecular biology. And recently as you're showing, there is a paper that describes an open source hardware model of a pipette that actually fits or complies with accuracy standards, with ISO accuracy standards. So basically what this says is that this pipette that has syringes and 3D printed parts is basically able to perform as well as the commercially available ones within this ISO standard. And so the nice thing about this, which the benefit that this brings is as I mentioned, you could in principle calibrate this pipette every day before start your daily routine in the lab, which is not something people do when they have a commercially available one because either they don't have access to a calibration service or it's too expensive or they don't have the time because the pipettes need to go out, be calibrated and come back and then they lose time with this move back and forth of the pipettes. And so this already by itself is a good thing because they are like orders of magnitude cheaper than commercially available ones and you could build many of them. So for instance, if you think about education, you could have together with other tools that we're gonna talk about, you could have a molecular biology lab for education where each group of students get a small kids and they're actually able to do the experiments and amplify DNA and actually do all the normal steps inside a molecular biology lab or a neuroscience lab. So you think that if we invest into open source hardware for example, is it devices that just reproduce what we have in our labs? Like a pipette for example. They're very common, but very expensive and have a more expensive mind at the end of the time. If we have the capacity to produce them to reproduce them and convert them ourselves we can bring those kind of tools earlier in a biological education so we can go further with our own education. And- Absolutely, yeah. So I mean, we gave the example of the pipette and then a lot of people are gonna say, oh, pipettes are not that expensive from the point of view of a research lab but then I can give you another example which are microscopes. So if you take a fluorescent microscope it costs you at least $5,000 to $10,000 to set up a fluorescence microscope. And so of course you wouldn't put these in a bachelor classroom until 60 students at a time to go and have a crack at the fluorescent microscopes. What we have in the literature are several open source microscopes that actually can do fluorescence for under $100, right? And so you could actually build these microscopes and have 10 of them and still be saving money as compared to a commercially available microscope which means, again, education, yes. And then you can have all these people testing and doing experiments even in a classroom in high school but also, and this plugs to another point of open source hardware you can have a massive parallelization of experiments, right? So if I have only one fluorescent microscope and 10 people in the lab I'm gonna have this booking system where somebody's gonna end up coming late evenings, the weekends and so on. If I have 10 fluorescent microscopes on the other hand then basically each person in the lab can have their own and do experiments in their own time or you could think about experiments that require much more data and you just have them running in parallel. And you can have read in and see in your data to support more accurately. Exactly, and you can even replicate experiments much easier. And so this goes, again, in line with the philosophy of science that for something to be true it needs to be replicable. You need to get the same results over and over again. Doesn't matter where you are, right? So this also increases how you can make your data trustworthy in a way. So it's not like a one-off, oh, I found this and maybe it's an artifact but because it's so time-consuming to reproduce this we just have to take it for granted the way it is. I mean, I fully agree with that. Once again, I consider that open science and open access replicability of data should be more common. But in all case, I mean, let's say I have a lab why should I bother, let's say, investing time into developing or into reproducing such devices? Do we have, I don't know, for example, here in the department of Sussex, you recently fixed a perfusion pump. Yeah. Was this an example? Yeah, so this is actually a very funny example and I've been talking about this for a while now because it really still boggles my mind. So we have this little perfusion pump, yeah, that one that is being shown and this thing broke in the lab and although it's a simple piece of equipment it's a vital piece of equipment inside of the photomicroscope because this basically removes solution that is perfusing a certain biological tissue. Without this, the whole experiment and the whole microscope is stopped because you cannot remove the solutions and you cannot perfuse your tissue. And so this thing broke and it took me, basically you're seeing here a quote for getting a new one and it took me two days and three emails to get this quote. And this already kind of upsets me because this is not online and everything in the world is the prices are online except for our scientific equipment. We're not going to go into the discussion of scientific company's business models but this is already a little bit irritating and wasting my time. And so the other thing is these two days and now as you can see the lead times to get this was for this pump specifically was about 12 weeks. So if I would be only relying on this company to send me this and the company is actually not in international companies here actually very close by Sussex. It would take me 12 weeks of a microscope without being able to do experiments. And if you consider that a PhD project is about three years this is a significant amount of time for being stopped. And here we're only considering the good case that these only broke once. Right. And talking about the city device that it does. And so, and then because my position here is about fixing devices and getting them up and running and producing new ones. I said, you know what? Let me take a look at this closely. So I bring this thing upside down. And as you can see, I mean, you cannot see on this image but as you come closer to the little black box which is the pump. I hope you can read where it says acquiring equipment, which is now highlighted. And so basically what this company did was they put an aquarium pump in a very nice casing with dysplexic glass sheets, right? They put some tubings, an early mire, a pressure gouge and now they're charging me 750 pounds for this with 12 weeks lead time. This must be said that this was way before the pandemic started, right? So it's not about like the pandemic is making this lower or anything like this is when best case scenario the word is working properly. And so what I did was, you know what? If this is an apartment pump, I'm pretty sure I can find something online to replace it. And this is what I did. And this is what you can see here how much it costs for an aquarium pump to do the same job out of eBay by today, get it tomorrow if you pay a little bit extra for shipping. And so this is why we should care or this is one of the reasons why we should care about how our tools are working, where we're getting them from, how is the supply chain working and so on. Yeah, that's a silly example. I mean, I'm intrigued by that but see here for example, that's accurate where you're describing. You're just describing a do-it-yourself practice when you're not afraid of taking the seal out of the box and see what's inside. And if you consider why do they have a pressure gauche for that, I mean, that doesn't make sense but it's not really the open source fields of you talked about earlier. Do you have something which is maybe a little more state of the art or something which is more reliable in the lab? Yeah, absolutely. So, and this is not work for me, which must be said this is there is a device that was built in Tom Badden's laboratory in collaboration with a lab in South Africa which is called open spritzer. And this is basically a device that delivers little air puffs and the air puffs are really so tiny that you can put them in a tube. And if the other end of your tube, you have a capillary with liquid you can deliver picoliter injections. And so this is used all the time to inject intracellularly solutions into cells. And so here you can see the repository where this is described and all the steps needed to build one of these are stored. And also the data, this was published as a paper but then on the repository you also put figures from the paper and it's showing how it performs against the market known brand which is a picol spritzer. And what they show in this paper is that it performs as well or even better sometimes than a picol spritzer. And so this is a little bit more high tech, so to say but it's again showing that you can be much faster with development of experiments and much more affordable which means you can have several of these open spritzers in place of just having one picol spritzer. So you could have in all of your rigs you could have one as a default. Yeah, okay, so picol spritzer is about 25,000, 3,000. 3,000, 2,500 you mean? That's what I mean, sorry. And so and I think if I remember correctly the open spritzer is about 350 pounds or something like this. Yeah, something like that, yeah. So it's again, like an order of magnitude cheaper than the other one. Okay. Yeah. Oh, sorry, keep going. No, I was going to say, and this is just one example, another example which is, and again, like here we're still in the realm of reproducing things that are already existing. If we think about what I mentioned before, design your experiments and then thinking about, think about making the tools, we have this other stimulator and this is totally fitting to the vision series. We recently, this was already 2019, we published in collaboration with Thomas Euler's lab in Germany, the open visual stimulator. And this is a projector. It's similar to a regular projector that you could buy from Amazon. And what is different is that we made it in a way that you can actually put pretty much any type of LEDs in it. And what you're seeing here in pictures is a system like, can you stop there? Yeah, please. What you're seeing here is basically the system where we have two projectors working together at the same time, which allows us here, it's only depicted for LEDs. So for zebrafish specifically where you have RGB and UV, but you could do up to six different LEDs. So basically you have a multi-spectral spatial stimulator that can do up to independently six different LEDs. And what needs to be said here is that this is a new thing. So there weren't any, as far as I know, there weren't any special stimulators that were able to take any type of LEDs and easily make them match the spectral distribution of cones in a specific species. So you have a lot of dedicated spatial stimulators for primates, but if you compare them to zebrafish, they have three cones, while zebrafish have four different types, and they don't have UV. And so if you use a primate stimulator to do zebrafish research, you are coming short in terms of what types of questions you can do, how well you're stimulating the organism that you're trying to study, and so on. So this is another point for open hardware in terms of that we can bring research forward by doing things that were not possible before. Okay, but excuse me to interrupt you here. So what you're showing here, it's technically a part of the science process where you have to develop your own tool and into your part of the R&D process. But what is the openness in that? I mean, you created that, but that's not there. What is the point of making that open? That would be my main question. Let's say that I'm another visual neuroscientist, and I need to adapt that to, I don't know here for example, is that for zebrafish? Yeah. Apparently according to you, I can modulate it to whatever I can, but what is the point of making it open? What will be the point of our colleague in vision research to invest time reproducing it? So what we did, what we tried to do was to put the documentation online in a way that everybody can reproduce it. So there is the paper that already described and this was published in eLife. Big shout out to the eLife guys because right now it's a super cool way to publish papers there. And we published all the step by step on how to build it, what parts are needed. There is a view of materials, how you can calibrate it, how you can make sure that it's really, really properly working. And so in the lab, we're using this for zebrafish, but as well for chick redness, right? And so this already shows one of the points is that initially we started with zebrafish and then already got picked up in the lab for chick redness, we have a different spectra, wavelength spectra for the avian coles, right? And the point is that as we published the paper, we're hoping that the community is gonna pick this up and derive it to other species. It's already been used for mice in tubing it, where it actually initially started its development. And we're hoping as more people contact us and getting touch, we can help them and guide them through, okay, maybe, let's say I wanna do this super cool experiment with the mantis shrimp, which also has like crazy spectra for their vision system. And we could help them with six different LEDs to stimulate, this is of course like me talking on hypothetical at this moment, but this is what we hope for, that we said like a landmark and a starting point for the community to build upon. And so this brings another point of open source, which is we took the initial effort of developing this, hopefully somebody's gonna come up and profit from this in the sense that they will derive it, make it better and share it back to the community. So they profit it because they didn't need to start from zero and we profit it because whatever they developed better in you, we didn't need to develop ourselves. So we get two for the price of one, so to say. So if I transfer what you're saying, it means that wherever and whenever you have a lab that is developing a new tool, that they own research, most of the time it will appear in their methods, in the papers that says, okay, we developed whatever tool to accomplish for the study, but the thing is that most of the time they don't share the blueprint or the approach or the successes and failures in terms of developing the equipment. So what we're claiming here is that on top of just sharing our data or results, we should also so share our methodology protocol to have been actually been able to reproduce it and speed up the innovation cycle that we're currently in. Yeah. So sorry to interrupt you, but I think this is, yeah, this is very fitting with this. I think it's a historical reason why our methods got shorter and shorter, right? And this is like a whole different discussion and I don't wanna go into details about that, but what we know is that big impact journals have very little space for methods and you have then this online methods, supplementary material and so on, which kind of encourage this practice of putting very, very short methods and very short descriptions, which is detrimental. And so luckily for us, what is happening is that now technology is evolving to a point where you have more space and internet bandwidth and so on. So you can actually share things by yourself in a repository and be super detailed. And so you can have texts, you can have photos, you can have videos, you can have really a step-by-step guide and even if the high impact journal that you're sending these to doesn't have the space for that. You can still share your repository as a link and then people can go there and say, look, this is how we did it. This is the step-by-step and you can like really, really follow on it. And yeah, one little flow in there. I mean, I read a lot of papers and I try to reproduce most of them. You and I will review some open source paper and most of the time we fail to reproduce it because sometimes a code will be missing or they didn't forget because they upload the first draft and correct it. But to put simply, claiming that something is open source doesn't make it complete. So how can you compel people to actually follow a guideline? Because there's plenty of repositories. I can't keep back the amount of repositories but they're not always the same. Absolutely. So this is the beauty and the curse, a little bit of open source. The diversity of it. Because you have people that are building tools all over the world and they have the preferred platforms. You have GitHub, you have Instructables, you have Hackaday and so on. And so to cover this problem, this very nice group of people got together for a number of years now, got together and made the open source hardware association as you're showing. And what they developed was a certification program for open source hardware. And this is a very low entry certification process. I mean low barrier entry certification process where you basically put all your things in a repository. And because they have a definition online, they're already telling you what they expect. And they have a definition of what is open source hardware. And so they tell you what do they expect in terms of what files are you sharing for people to be able to reproduce your design. And so once you go in there and you say, okay, I need to share, these are just examples, share files that are editable, right? So this would be similar to instead of sharing a PDF or something, you would share the word doc of that, right? Of course you can modify a PDF and actually translate it back into something editable, but that's hard work. So what you want to do is share the editable file from the get go. So not going on that much longer, but the point is if journals and academia in general would, for instance, pick up the open source hardware association certification process, we would have a very nice starting point, a very nice standard for open source hardware and academia where if the hardware that has been described in the paper is certified, I already know what to expect. But I know that the files are gonna be properly there and so on because the certification process of the open source hardware association, make sure that all of the files are there and are present and are in a certain format. And the benefit for researchers here would be there are already many companies and many people who are building their own tools outside of academia certifying their projects. And so this would get academia closer to let's say the general public and to companies and to people who are interested in hardware, but not necessarily in this very small space that is academia. And then again, this is again the idea of like this is public money. It needs to be brought back to the public. This would be a nice way of actually getting attention that we've public money, we're building these cool tools that then the public could reproduce them. And that can benefit the public. Actually, we have recent examples of good practice in open hardware. We have the kind of ventilators that we developed during the COVID time. So this is actually a fantastic example. Unfortunately, during the pandemic, we've learned a lot of things that showed us that our supply chains are not super reliable the way we like to think they are. So we had this problem, the pandemic started in China, a lot of lockdowns there, a lot of things got delayed. There was a shortage of personal protection equipment and so on. And so what a lot of people did was actually started building these things locally. And here you're seeing a paper that we published in the beginning of 2020. This was together with Tombadan, Lucia, Pietro, Lugodino, and Jenny Molloy. This is again, trying to make, and this was very fast. We had to write it in a very short amount of time. At that moment in time during the pandemic, there were all these groups popping up, building things as complex as ventilators. So these are life-saving machines that are really needed and really shortage in a lot of places and really expensive. And so all of these groups started building these ventilators and some of them are actually already in use or in testing in hospitals. And the benefit of them is that, I mean, you can see here that you have all of these different models, but like I think the biggest benefit of them is that because they are showing how to build them and they're open source, you can actually see the list of materials and easily source them locally because it's much easier to get locally these small components and small electronic components that might already be stocked in certain shops even before the pandemic started, then it is to get a complete built ventilator shipped from somewhere across the world. And so this again goes in line with, and this going back to the idea of equipment in academia, if you think about, I'm originally, as you mentioned, like I did my bachelor's in Brazil, this is where I'm from. And I remember back then that even when you had the grand money to buy something, you would get stopped in customs and then there was a big delay and then the prices for custom taxes would be super high and then the equipment would be more expensive. And then when you finally got it, it worked for a number of years, but then if it eventually broke, or if you needed an upgrade, you wouldn't have a local customer service to fix it. And so you would have either to ship it all the way back and then super expensive a lot of time, same problem with customs and so on, or you would have to put it to the side and buy a new one. And just throw it away what it can be fixed for. Yeah, exactly. That's a common problem. And it's a common problem and it's a common problem for, if you think about electronics in the world in general, right? How many times have we seen like a broken phone with a broken screen and then you toss it and you buy a new one? And the reason for this is that these things are normally made hard to repair and so on and luckily for us, and this is I think tangently related to this is that Europe has just passed this right to repair law where companies supplying electronics and making electro electronics for home, they need to make tools or equipment that is now easy to repair or the spare parts to repair them are available for at least 10 years. And so this helps with electronic waste, also helps with being able to not throw away a big gigantic tool just because like a little tiny resistor or a little fuse broke inside it and so on. And again, it's much faster to replace a part inside than it is to get a new one. So you fight companies against program obsolescence and they can be applied to scientific equipment. Equipment as well. In principle, you should be able to apply that to scientific equipment as well. Well, I completely agree with you. And what we can say now is that with the democratization of electronics, the exploration of most patents, the fact that we have access to multiple repository online, technically we can say that innovation cycle just go back to researchers. But the thing is that we still, it's still underappreciated. So I do you encourage all years of fellow researchers, I do you implement in order to research this kind of practice. I think it's always easier to convince people or to show people the benefit of something if you can solve a problem or if they can solve a problem that they have. So what I normally suggest is that if somebody is interested in starting to learn how to build tools and they see the benefit, they just don't know where to start, right? I tell them, look, find a small problem that you have. There are all these tutorials online. There are all these electronic kits. You can buy a little kit for 50 bucks and you can put things together, let's say a temperature sensor that when you put it together with all the electronics, it's gonna send you a message, an SMS or an email whenever the temperature in a room goes below a certain value or above. And so with this, you could create a, let's say a protection system for lasers in a two-photo microscope. If your room temperature goes above a certain level, you get a notification saying, look, that room is above a certain level. You might as well go there and turn off the laser, otherwise it's gonna damage things. And so by building this little device for, let's say 10 bucks, you're gonna save a very expensive laser from being damaged. Yeah, but the time I will, let's say I have PhD students is naive about most of the things. The time it will spend trying to understand how that works. Would that be worth it instead of just buying a 50-term sensor? Yeah, so I mean, I think it is worth it because when you learn these things, you're not just learning how to put a temperature sensor together. And so of course it needs to be weighed, how much time you need to build these things and so on. But my point here is, biologists will need more and more to know how to program. And a lot of what we do with hardware and electronics is based or it has a big programming component. So learning these, you could use a tool to learn how to program and then these are gonna use for the rest of your life, data analysis, automation of certain pipelines and so on and so on. So you start low, but you implement it more and more along user research and then you can innovate because you know it's accessible. It's an incremental process, right? I studied biology and I didn't start building visual stimulators. I started with little tools and little things that then got, because then you learn these things and you get packaged, then you can improve and do things differently. So it's not really that much time consuming to start. You need maybe like an hour a day with a little electronic skill to get started. But another way that this would be entirely very much helped is if there was top down investment, right? If universities and institutes and departments would say, okay, we need to come up with let's say an open science module for our PhD students or for grad students, where you teach them all of these little aspects of open science, open access, open data, reproducibility, how do you put code together and share with others, best practices in keeping your data for a long time and so on and so on, electronics and all of these things. These will make them better researchers. These will make the results of research better and more replicable. Yeah, it's definitely good. It's easy, good scientific practice. And yeah, absolutely. And there is a very big return of investment here because as you are more independent from needing to get something that it's already made, research can be faster and you can test different things and you can go in different directions. And the other thing is that these are normally life skills in the sense of if you know how to program Python, there is a huge market for Python programmers outside of academia. And as we know, we are putting out more PhDs or people are graduating with PhDs in a higher pace than we have permanent positions in universities to absorb them. So we know that people are gonna leave academia anyways, not everybody's gonna stay. And so if they have skills that are desirable by the market, then they are better off as well. Yeah, right. But back to academia. So you consider that maybe it's a duty of universities to propose or to suggest some kind of more technical courses, interdisciplinary modules? Yeah, I mean, absolutely. So in a way, the idea of a university campus was that made that you would be the biology closest engineering department and then it would be like a short walk so that you could talk to an engineer and actually build something together to do your experiments. And right, unfortunately not. This could be revisited maybe. And the other point is that, of course, I don't think necessarily you need the universities to take care of this. Even students could start with little clubs as we had here in Sussex, where we had the coding club and all the Zork shops. Again, like a big, big, big entry barrier would be lowered if there was a space from top to bottom to say, look, this is a space where you can come and create and so on. And we do know of places that are starting with this. So you do have interdisciplinary labs. There are places in Paris that are like this. Like this is something that we're trying to create here in Sussex as well, where you have a lab and a space for people to come and learn things as part of their studies. And this could be seen as, let's say, interdisciplinary, let's say PhD. Part of your PhD is actually developing the tools that are gonna guide the question that you had. And so there is a component of engineering could partner up with an engineering lab. And then part of it is actually collecting the data. And so you could make this to fit quite nicely with the PhD track in a way. Yeah, I agree. But I mean, what I meant to say is you cannot invest time on something that you ignore exists. So at first you have to make clear to, let's say those new scientists, those PhDs, master that, I mean, how many times do we see people following old laboratory practice that date from two decades ago? You cannot improve on a protocol where you don't know that you need to improve on it and the solution is actually active. What I mean by that is right now starting a new position at the University of Sussex where you develop different kind of tools. And actually right now you're trying to create a new lab, an open science lab. We want to share with us, I mean, are you trying to alleviate locally? I mean, I guess you'll be the best example to show how you convince those people that we need to invest time and effort. All right, I mean, this is so far still like a moonshot project, right? Because I'm still like trying to put things together and know exactly how it's gonna work. But the idea is what I meant. So we need to create a space where there will be knowledge and expertise for people to learn about all these different aspects of open science, including hardware. And so of course people don't know what they don't know, but if you come to a place where there is, when you first come for a PhD, there is every semester, let's say a course that is maybe mandatory or not, but there is a course dedicated to telling people, look, this is what we can do nowadays with the open source technologies that are available. This definitely then opens up the mind to say, okay, I can actually do this and there is a supporting space here that is gonna guide me through it, which is already what's happening here in Sussex. Because there are already a lot of projects that are getting started where we're building new things for the labs that are things that are not, that were either very expensive or things that were not existing before. Yeah, fully agree. And like I said, it's the beginning of the talk. You created some kind of repository of repositories. Yeah, so open neuroscience, you mean? Yeah. Yeah, so open neuroscience was something that started many years ago and it was a way for me to keep track of all the point source projects that are there, but I think we have somebody here especially to talk about this who has been collaborating with open neuroscience. Hi, Ceci. Hi, everyone. How are you? Thank you for having me. Yes, I've been collaborating for over a year now. I am Ceciah Herbert. I'm a PhD student at the University of Buenos Aires in Argentina. And please excuse the noise or some construction being going on here. So I hope you can hear me well. Let me just share my slides. I should do it. So today I wanted to tell you a bit about open neuroscience. This is an initiative that creates open source projects related to neuroscience on openneuroscience.com. Projects are not only hardware that we might be discussing just now, but also software, data and educational resources and others. The database is user driven by the community. So while we are on the team and we are on the lookout for projects all the time, anyone can contribute to the site. And of course, you can browse all of these projects freely. What we do is basically we check each submission to make sure it is at least barely related to neuroscience, but more importantly that it really is open, meaning that the source is ready to be available. And Sandra was just explaining. Then we publish it on the site and announce it on Twitter. So at this point, especially after the talk we just had, you're probably thinking, okay, this is great. It's so relevant and it can be so useful. So how can I help? So let me just make an aside here. I was telling you, yeah. I'm sorry to interrupt you, but I think we can see that your authors notes, your presenters notes. Yeah, that's fine. Excuse me. That's absolutely fine. Okay. Well, that was my script for today. What I was saying is that a year ago, I was maybe sitting where you are just now, right? Listening to under talk and saying, this is great, I wanna jump in. So what you can do to help open neuroscience, you can, first of all, submit a project if you have one or if you know of any project that fit the bill. This is our upload form. It only takes a few minutes. You can also spread the word about the site as well as use it. You can follow us on Twitter. That's where I just said that we announce all the projects and you can collaborate over on GitHub by solving issues that there are always some issues, right? Another thing you can do is contact us over at Open Neuroscience at gmail.com. If you'd like to collaborate further and maybe work on some organizational aspects. And you can also give a talk. So give or attend a talk. This is our new seminar series that starts this week. It's going to be every Friday at 15 GMT on the Worldwide Science Platform. So we're both on the Neurotrack and on the newly opened open source track of the seminars over at Worldwide. And please join us. This is the schedule for April. We're starting this Friday, as I'd say with Antonia Farel about giving him talk about a platform for automation of cell biology experiments. And we have a few other speakers lined up for the next few months and you might be the next one. So this is the form to schedule the talks and we hope to see you there. I really want to apologize about the noise. They just started when I had to speak. Thank you so much. All right. Thanks for that, Ceci. You will find all the necessary links in the description of this video. So you will find all that we shared, all the necessary links and also links for open neuroscience repositories. And yeah, of course, we will share networks and programs, upcoming talks. So I hope to see most of you during those talks. Yes, please. So do we have questions? I have one stupid question. You're muted. Hey, Andre, my boss won't buy a printer. What can I do? Well, that's a tough one. A lot of the times I say, and please don't quote me. I mean, this is going to be recorded. So people are going to quote me on this. Sometimes it's better to ask for forgiveness than to ask for permission. And printers nowadays are at the rate of $200 on eBay. And they're actually not shitty printers. They're actually okayish printers. You can actually print stuff with them. And at this, at this level of cost, you could get together with other people and everybody pitches in a little bit and buy a printer for the department. And the benefit of it is that the return on the investment of the printer comes really after you print it for as little as five hours, right? Because you can print things that are way more expensive to buy than it is to print them. And so really quickly, you can get your money's worth for the printer and the filament robots. And there are papers actually showing this return of investment into open investing on open source hardware and in financing open source hardware. And so I think all of these links are in the description of the video. And so people can take a look at them. But yeah, so my recommendation would be maybe get together with friends, either buy a printer or you can find a local maker space. There are a lot of fab labs around you. There are a lot of fab labs around and maker spaces and in a lot of places of the world. And so you can actually go there, print things, get help, meet the community. And then you can bring those to the lab to actually try to make the point. We printed all of these. This is much better and much faster than what we had. Therefore, here's the evidence to buy a printer. We have Eric from Newcastle with us. So he asked, do you know the source site that is mainly dedicated for use of open hardware in higher education teaching? Ideally with neuroscience-related hardware, for example, EMG, ECG, which can dismantle, there's a B missing, which can be dismantled after the practicals. Yeah, absolutely. So I mean, open neuroscience has a lot of educational projects. But I think for what he mentions, backyard brains would be a very good candidate. They are actually a company that is using neuroscience tools for education. And all of their tools are open source. And so you have an EMG amplifier, which is this big. And it's powered by batteries. And they have a lot of cool experiments and tutorials online. And so people can build stuff directly. And they don't need to disassemble them because they're small and you can put them in a backpack and then take them all, basically. Actually, backyard brain is a very good example. I can only encourage you to see a great gauge talk on that. He made a couple of talks. He's mostly focused on what is the opposite of higher education, lower education. High school, I would say. So yeah, so not education in universities. But you can find all kinds of education tools that can be used at different levels. So that would be a good start, yes. Yeah, although we also use their tools in training courses for people who had never had experience with electrophysiology. And so you can actually do their experiments in a higher education setting as well. Just want to emphasize that what you refer to is trend in Africa. So when you organize workshop in Africa, those are useful for regular resources. Yeah, no, but I mean, this could also do anywhere, right? Because let's think about the bachelors here in neuroscience. So you can take people into the labs. I mean, normally you would take people into the labs and show them the big behavioral rig and so on, but then they don't get to actually like do the measurements and put things and so on. So you could build a little electrophysiology rig, let's say maybe 10 of them have people in groups of three people and get actually get them to record the data and analyze and do like little experiments with electrophysiological data. So you don't really need to be just in Africa. I think this is a very important point. Like this stuff is not just where you don't have access, but really can we change the way we teach things by having more hands-on because we have access to equipment or more equipment? I agree. That's something we haven't talked about, but the philosophy behind open source hardware actually allows universities labs in lower income countries to actually have an equal access to science and protocol and they can actually produce a same level of data that has the same... Absolutely. So there is a democratization and increasing accessibility for research equipment. Well, thank you for that. I don't see any other questions. I guess we covered most of it. I can only recommend the audience to keep track on the Open Neuroscience series that will start this Friday. I guess you will definitely find an episode project that will fit your need or will find of interest. So take a look at the website. Take a look at the upcoming series. We will advertise it and we hope to see you there. Yes, please. A final word? Well, thank you everybody for their time. Thank you for everybody who's been collaborating and picking up on this until not long ago seemed crazy and hippy ideas. And I think it's important to say that this is gaining a lot of momentum. So there is more and more open source hardware coming out. There are two dedicated journals now for open source hardware. There is Hardware X and the Journal of Open Hardware. Plus already also does a lot of open hardware publications. And there is a wonderful community online called the GOSH community where people should have a look. I don't know if you're going to have links on the description of the... I will add it. Yeah, okay. And so the GOSH community is really a global community talking about open science hardware. And the last thing that I would like to say is together with Julieta Arancio and Alex Cuchera, I'm creating or we're creating a mentoring program. We're not going to be mentoring people, but we are organizing a 14-week online course on best practices of open hardware where there are going to be mentors who are specialists in the field of open source hardware to guide people through what are the best practices in open hardware, how to foster a community, and what are the best ways to build tools and to interact with this community. So we are right now revamping our curriculum, but we hope to launch this soon. So please have a look at the website, which Maxime is showing now, openhardware.space. I think we're going to have a link on the description as well. We have. Yeah. And so this is a wonderful place for starting projects, for mature projects, for all types of open source hardware projects. All right. Thank you. Thanks, everyone. See you soon. Thank you.