 Hello, everybody. Thank you for joining us today. So as you know, we are having a very interesting and exciting session today. The one million voices initiative presentation about introducing students in science fabricology. I'll just do a very brief introduction to the session and then hand over immediately. So we're having this event today and presenting is Rosie, who is the managing director of the citizen science center in Zurich. The outline was also included in your invite will have an introduction to general terms as well as values principles and benefits of science. Then we will look into a few interesting examples and then go to thoughts and options for how we can make sense and science work for our ecology. In that context, I had already shared some of the results and some of the process of what we have discussed previously and with the regional teams, and also some other people who related to the project. And I hope she will be able to base her recommendations and thoughts on that. And then at towards the end of our presentation, which should last about 30 minutes, we will have a great interactive segment where we can ask questions and get more insights. I think I skipped the recap on the project because we are familiar with it. We do not have external people. And I'll hand straight forward from here. Thank you. Thank you, Lisa. I will start sharing my screen. Here you go. You should be seeing this. Hello everybody. And thank you for the opportunity to introduce to you one of my passions. This is also definitely citizen science that I am now doing as the managing director of the citizen science center in Zurich, which is an initiative of the University of Zurich and et age during. And I will tell you a little bit more about the center at the end of this presentation. So, citizen science. I know you have heard the concept because I actually looked at some of the video and documentation from the past, but let me just remind you. Mainly a few concepts just to have to agree somehow on a common language when especially when we talk about the projects on what we are talking about. And citizen science has been around since a lot of time I mean there are projects actually that are going on since 1900, you know, about birds, even though the term itself was coined only in 1979. Anyway, is the scientific work, which is undertaken by the members of the general public together with academic scientists. Actually, this is the definition of the Oxford dictionary, but you do find a lot of other definition around, you know, if you if you like Google citizen science. There's another one and they are slightly different. Often, they are very typical of a particular scientific domain or of a particular, you know, area. I really like this one, which is in a Swiss, you know, governmental document that is is a broad range of activities where people produce scientific knowledge outside of traditional scientific institutions. And I think that this definition reflect a little bit more the fact that is hard to define citizen science because it really covers a lot a lot of practices. All of these definitions though have a few characteristics in common. First of all, you always have in citizen science project public participation, which means people that decide to join this kind of project on a voluntary base and they are not academic scientists in the particular field of the citizen science project. Another thing is voluntary contribution is very rare for citizen science project to involve the money to involve payment, even though it happens especially in developing countries. Most of the time we're talking about voluntary contribution and the third one is that these projects produce science so it's not science education or outreach, even though science education is is part of citizen science. But the idea is really to produce knowledge new knowledge science based. Sorry. So, when we talk about citizen science project one of the first thing that you may wonder about is, we are talking about citizens and scientists collaborating. But how much do they actually collaborate and this really depends from the kind of projects you have a little bit of everything. The vast majority I have to admit is projects which are a little bit top down, which means that scientists are the one that, you know, do all of the work in terms of finding the research question designing the protocol designing you know what you want to ask to people and then you basically ask people to contribute. So, you know the collaboration is a little bit one way. Then there are the collaborative projects where the collaboration between the two community is a little bit deeper. You know citizens may participate on some of the analysis some of them may join the publication they can help dissemination. But there is a little bit more of interaction. And then there is the third kind, which is the kind that we really promote in Zurich. This is the kind of citizen science that actually everybody's kind of dreaming to do, even though it's not that these obvious is when the two communities. The scientific community and the so called citizens or participants do collaborate from the very beginning from thinking about the problem. Deciding how you want to solve it designing the protocol this designing the experiment doing the analysis doing the publication. Co-creation is somehow, you know what we are all aiming at, even though I have to say that citizens are happy to participate and they do participate in all of these projects, you know, even the contributory one which is I said, you know, top down. Most of the people don't really care. I mean most of the people really enjoy contributing to these projects in any way. Another way to look at these projects is not so much, you know, how, how much and how the communities are really working together. But it's mainly the way you are involved in the crowd. So how, what are you asking people to do, how are you asking them to collaborate. Again, you have a little bit of everything, honestly. And I think that what I'm doing here is really an oversimplification and sorry about doing that. But because the reality is way more complicated, but let's say for the sake of this presentation of this short introduction. There are two main group of projects that are the projects where you are asking people to collect the data for you. And this can be data in terms of like images, you know, geolocated picture description, you know, surveys can also be samples, you know, samples of water samples of air. But basically the role of the citizens is the one to to be your sensor is also called the volunteer sensing that they are the sensor for you and they collect data. So I will give you a few example and and I have a lot of examples. So I will go very quickly through them and hopefully later on you will have questions, you know, if you want to know more details and you will have the slides as well. So let's go back and check out this project see more detail. So I'm a tour list. This is a very, very famous platform and citizen science traditionally is very, very strong in in biodiversity in everything that has to do with nature people love taking picture of insects of animals of plants. So that tour list is a database where you can, you know, contribute out of your smartphone this kind of pictures and just very quickly have a look at the numbers here. This is a very well known platform that really contributes data for scientific publications and to scientific database. So this is something about more than three million people that are contributing to this platform, you know, 50 million different observation and working with, you know, hundreds of thousands of different species. This is another thing that people love again animals somehow related but is more some kind of, you know, ecosystem services if you want. There's a lot of projects about bees. This is just an example of them. And this is just people contributing photos you see the explanation in the in the right in the left, sorry, bottom corner of the page that just take a picture. You see it geolocated and you are mapping, you know, bumble bees in this case all over the states and also monitoring the decline of the population. Another kind of projects always based on on smartphone and I have to say that the vast majority is nowadays of this kind of data contribution comes from from a smartphone contribution. NASA that organize this and this is basically many different things that you can do to understand the microclimatic variation. So you can contribute images of clouds, you can contribute, you know, presence of trees, evolution of trees along the seasons. This is a part of land cover. And actually, a lot of these kinds of projects are actually a lot I will say increasingly, let's say, and considering you know how much we use information from satellites, how much we use images from satellites in, in, you know, in monitoring a lot of aspect of nature, and a lot of citizen science projects do what is called the ground truth thing, which means basically verifying that what you are looking at from your satellite images has been identified, you know, in the correct way. And again, a lot about weather, a lot about, you know, temperature and precipitation also because again it is increasingly easier to collect this kind of information with your smartphone. This is a project about water freshwater watch. Again, look at the numbers that they are is more than 8000 participants and they are monitoring almost 3000 by now water bodies all over the place. It's nice because some aspect of this project involved citizens kind of adopting a certain river or a certain lake, and really going back regularly every year to provide information about about the river. And there is a lot of citizen science in water resources and hydrology and I'm just showing these, which is a compendium a little bit of existing, you know, projects on the field. What we do most of the time is projects that cover monitoring of precipitation, water quantity and quality in rivers or lakes, soil moisture level of flood and risk management, and that you can see I mean you can really find them quite spread all over the globe. Other projects. Now, they are not based on photos from from your smartphone but actually citizens collect samples, and they send the samples to researchers, and we have a lot of projects of this kind, both for water as we just mentioned but for her as well. Or in this case, you know, pests, insects. This is a pretty famous project where people collect specimen of this particular past which is this cabbage butterfly, and they send them to the researchers that do both some kind of mapping of invasion roads, but also they look at the structure of the genetic structure of the past. Talking about soil. There is this project that has been reproduced in many different places, you know in the states in Australia and now very recently was launched in Switzerland in Zurich. It's very simple and more than providing real detailed scientific evidence is somehow more to raise awareness, especially among farmer of the importance of the quality of soil and the health of soil. And it's simply, you know, people that are bearing under cut on underwear and then basically digging them up after a certain number of weeks and then basically you look at the the composition of your underwear and this is related in a scientific way, actually to the health of the soil. So that's it for the examples about data collection. Now let's move to the second big chunk of projects, which is about data analysis. So, in this case, you are not most of the time, you know, working with your smartphone these are projects that are better done with a web interface with a big monitor. Because what you are using is like the brain power of people is not citizens and sensor is not citizens that go out for you is citizens that use their brain power for you. So one of the projects is basically citizens doing tasks which are still quite difficult for computers, even though as we all know artificial intelligence is like quickly catching up but there are, you know, many things where humans are still much better than machines or humans are still very useful in training the machines or teaching to the machines and this is like image analysis, pattern recognition, transcription, translation of document or mapping. A few examples as well of this kind of projects. So this is a project based on camera trap. And this particular one was in our platform in Zurich, but there are a lot of similar projects, especially in Africa monitoring, you know, bio animals, you know, in in in parks, especially in Serengeti and in many national parks actually in Africa. This one was in Switzerland and this group of researchers was looking at weasels and the decline of the weasels population in Switzerland so they placed camera trap a little bit everywhere in Switzerland and they captured many, many, you know, thousands of these clips. And the question was what is in the clip. So most of the time was a mouse as you see in this particular one. But basically the idea is that you sit in front of the computer you look at the clip and then on the right side of the screen. You have this menu of different possibilities which is very detailed, well described, and then you basically enter there what you recognize in the video. This is a project of forest monitoring is just one of the many that exist. What they do is they look at images from satellite and they look at sign of the forestation. And for them, look at signs of illegal deforestation so basically again they go back to the same area over and over to see if the extent of the forest changes. This is again camera trap. This is a project in Kenya. In this case, these, they are looking at this prickly pear which is a cactus which is, you know, a kind of plant. This is a study which has been imported and is not really original is an invasive plant for what concern most of the of Africa and basically they are asking people to to basically look at the at the different mammals that are captured by by the cameras. Again, if you look at the number. When you do this kind of studies, you have a lot of data. Okay, in this case, you know you see they have almost 600,000 classifications that need to be done. And they are asking this is a very new project in a in a platform called universe, which is basically a lot of contributors based in the states. They already have, you know, 3500 volunteers, they completed 2000s of them but you know there are many more to go. And this again is a very typical citizen science project of this kind. This is, I mean is is very hardly connectable with with agriculture or food, but I think it is just an example to give you the breath of this kind of project this is linguistic it's like it's a totally different fields. And there are many projects in linguistic. This particular one was studying, you know, all the dialect German dialect based on hand written documents. And so the idea was as a citizens you could either transcribe what was hand written into a digital format, or you could try to translate the dialogue. A similar project exists for, you know, a lot of, let's say document based project that you can present them PDF and desk people to look at the content to look at the format to translate to transcribe to really do a lot with this kind of documents. Last example again is very little to do with agriculture, however, and this is just an example of a different way of doing citizen science, which is always, you know, based on a computer, but that introduces the concept of gamification. And in this particular case that what you're doing is you're playing with a 3d puzzle. So, you know, a lot of kids do that a lot of very young people, they have no idea what they're doing but they're having a lot of fun. And what they're doing is reality is that they are mapping that the 3d structure of neurons. They're actually helping scientists to understand the human brain. But again, they are playing and this is something that is very attractive for a lot of people, and especially if you have a kind of subject or study where you can use this approach. So, enough with examples. Let's go back a little bit to the theory. So what are the benefits of citizen science, why you should, you know, spend your time into organizing this kind of projects that, and, you know, it's not, you know, as simple as it may seem. And I will touch some of it later. But the benefits for scientists are obvious. You know, I mentioned the thing of resources. You are comparing the typical research group of, you know, typical professor or researchers at the university with a few postdocs, a few PhD students. A limited amount of resources versus potentially, and I say potentially because of obviously not the case but potentially everybody out there contributing to your study. So if your study needs, you know, data from wide geographical extensions, or if it needs data for a long period of time, the benefits of citizen science are obvious. And also in terms of dissemination and impact, of course, you can have way more visibilities where you have this kind of approaches. And another aspect which is very important, even though it's often underestimated, is that when you interact with the citizens, you really get on board a new perspective. They have a totally different way of looking at the research question and whatever you are doing in a way that is very different from what, you know, a typical scientist would do. So the conversation between the two is really interesting and it really brings a lot of richness to the research. In terms of citizens, you know, why should they spend their time, you know, looking at your images or translating your document, and there are a lot of studies for that. There are a lot of publications, a lot of literature. And, you know, the answers actually of the citizens are very, very consistent. The big main reason is that people feel, you know, some personal satisfaction in contributing to something bigger than them. They are contributing to science. So they're not wasting their time. They're really doing something bigger. And so you should actually make sure that this is the case and that you're not wasting their time. But this is another question. Then they learn, you know, they learn about the topic of the project, which most of the time is something they're very passionate about because people join projects according to their interests. So they learn more, they learn better, they communicate with them with the scientists. There is a social dimension and networking dimension because these projects are very often they have a part of, you know, forums or like discussions where people can get in touch with each other. And so people get the chance to get in touch with like-minded people with people that have the same interest or maybe they share the same concern on a certain issue. And then again, sometimes it's just fun. Some of these projects are very well designed that they are fun. They're nice to look at. They're nice that they have this gamification and competition component. So people enjoy doing that. I mentioned here that equality because I'm sure is probably was going to be one of the first question that I get. I always get this question and this is, you know, something that has always been an issue for the methodology because it's very easy somehow to say, you know, how can you trust data provided by people that have no training, that they have no knowledge versus scientists that have spent years and years in preparing on a certain topic. And again, there are many studies is not meaning saying that you can go and check the literature and all of these studies have shown that actually the quality of the citizen contribution equals the one or professional scientists. So here this happens when the methodology has the same level of accuracy as the traditional methods, which means that you should know what you are doing. Citizen science is not something that you can improvise that I mean that there is a way to do it. There is a way to design your protocol there are ways and things that you can ask to citizens or not. The idea is there is a methodology and if you follow the methodology, then the quality of the data is is equal to the one or professional scientists. So I will spend the last just a few minutes about the citizen science in Zurich because hopefully can be useful for you as well. The center has been research has been created basically by the student institution to support both citizens and scientists who wants to start this kind of citizen science project. We are keen about co creation, if you remember what I said at the beginning, we are keen about having project that produce excellent science and we are also keen about supporting projects that contribute to sustainable development. How do we support you. We work on four different areas. The first one is the methodology as I said, you know you should know how to do this project. And most of the time when people have an idea about the citizen science project, they don't know about it. And we are there to help you we are there to provide the you know tips and tricks for doing this kind of research. We also have a citizen science Academy participatory science Academy, which works at the university level at the institutional level to provide courses and training to students, and they also provide seed grants to projects. Then we work with tools, we provide some tools open free that anybody can use I will mention two of them in the next slide. Because as you know if you just look at the examples you immediately understand that that usually you need, you know, either a web app or a smartphone app if you want to engage with this kind of projects. And then there is community building and community building is a very important aspect of this project there are a lot of people, especially I have to say, most of the scientists let's say that reach out to us and they think that, you know people are just out there, and they have better to do than come and contribute to your project. This is obviously not true people have a lot of you know better things to do and you are competing with platforms like, you know, Instagram or Twitter or Facebook. So, there is a way you have to look for your community you have to reach out you have to be proactive. And once you have captured their attention, you have to retain them in your project so that they keep coming back and they keep contributing. So community management is really a very big and very important part of this kind of projects. We have a full time community manager again we will not do community management for you but we can help you with the basics. And then the networking part is more for partnership, you know grant proposals, if you want to work with us, but also in the sense that we do work with a wider network of citizen science. You may be aware there are citizen science network. You know, almost everywhere that is the European citizen science network the American one the Australian one, one in Asia and a newborn that the one the citizen science Africa that was just, you know, very recently created them. And we work with all of them, you know have been around for a while. I've been working with some of these people. And the idea is, if you come to us and you need contacts or you need a particular expertise that maybe we don't have because obviously we don't know everything far from that. Chances are that we do know somebody that has been working in the field and we are very happy to put you in contact with the right people with people with experience with other tools maybe that are different from the one that we provide. And now very quickly our tools. The project builder is for the web based projects. So the idea is, you have digital data can be PDF from social media images video clips and in this platform, you can create projects that involve the crowd into the analysis. It's very easy is there is a step by step process, you create your project and you publish it that this is just an example this were researchers from Milano working with social media with tweets. And basically, you see that the interface of this kind of projects is very, very consistent very standard on the right side, you have the data in this case is a tweet with an image. And on the left side is whatever you want to ask people to do in this case is just answering a few questions. And then the other tool that we have is a smartphone app, the citizen science logger. Again, is exactly the same principle is open is free anybody can use it. You can via a web dashboard that you can create your app. Your app can be a survey can be collecting images again collecting video clip. You decide these in this web interface with a very simple again step by step. And then with one click, you have your app deployed both in Android and iOS so very powerful. It's very simple to use. And this is an example again the interface is simple if you want something super fancy you can always get to that later on, but especially to pilot your project to test this project, we really encourage you to use this free and available tool. Again, simple but you can really do everything you need. And that's it I will stop here and and open the floor to questions. Thank you so much Rosie. This was very interesting and I'm not sure whether you're seeing the top but I think people are really enjoying themselves. Interesting presentation. Thank you so much. Yes, the people are clapping for you. Wonderful. Thank you very much. Yeah. Do you want to take a yeah do you want to take a brief minute or can I go ahead immediately and ask my questions before opening to the rest of the participants. Sure, go ahead. Okay, so let me start by going back to my slide that I skipped in the beginning. Just to put us all back on one page so I so I skipped the brief recap of the project. So just to like remind you as well like what our objective is and what matters to us and then I will ask questions about your presentation in relation to our project. Our overall goal of our project, as you know, is to develop a tool or a series of tools that enable the farmers produces or produce organizations consumers or other potential and users to inclusively participate in agriculture movements to support sustain about the option of and contribute to the collection cooperation sharing of information to fill key knowledge gaps on the performance of agriculture. So as you know, our entire initiatives citizen science initiative is centered on a good quality. And in terms of the process what we discussed as well is that we have this value driven participatory cooperation process that we're looking at which which includes those coordinated but specific regional dialogues in the various regions that we're collaborating and the worldwide review and analysis of existing approaches for citizen science that support our ecology and agricultural transitions and then collaboration with relevant knowledge partners such as yourself, such as yourself. And so I have a few questions, and I would like to proceed with them so the first thing that I had been thinking about as I was listening to you is that I see the values and I see the various approaches that can be taken to how people are being included. Once the idea is there. But would you be able to advise us on how we get to having an inclusive idea in the first place so that our, our initiative is fully inclusive and value driven from the beginning. So that already the design and the idea is does not duplicate anything that exists already but also is relevant for global context and for various different contexts but like globally. And in the context of our ecology. So that is my first question the second question that I have is I saw that you spoke about that many of the initiatives that exist have historically had a very strong focus on biodiversity and data and biophysical concerns, but our ecology really bridges those two and has a very strong social component, both in the process but also in the data. So I'm wondering whether you have any kind of advice for how we advice and examples for how we can bridge the two that the process is inclusive, but also that the kind of data that is being collected, like response and corresponds to the social component. And then perhaps my last question would be in terms of limitations in context with limited literacy and also limited technology access. Because as as you know we're looking at developing something that can be used in various contexts, and where interaction and cooperation can exist across different regions, but then like the data and the process can like communicate regionally within itself but also across regions. I was wondering about that and in connection to that, just also because you're saying like a lot of data points I assume the data is very big. What do we have to consider in terms of platforms where the data is stored and for how long, etc, etc. Thank you. So probably you will have to remind me the questions I wrote down but okay let's start with the first one the co-creation so the co-creation project process as I said is really something that we're really keen about. And we spend a lot of time in doing you know before even starting worrying about you know which tool or which platform. However, I mean, basically you have to start somewhere. Okay, and usually, at least what I've seen in my experience, actually in my experience having some kind of an academic research background is most of the time the scientists that they come up and reach out with a question. They have an issue they have particular study, and they would like for many different reasons to involve citizens and at this point, what we recommend it is to immediately try to find out if there is, you know, somebody at the level of society some organization or some let's say citizens that they would want to involve into the study just to make sure that the problematic and the whole idea is something that resonates and for citizens is a little bit the same we had several NGOs reaching out to us. Usually they have an issue they have a social issue that for us needs somehow to be translated into a research question. So if you have you know an issue and again I just use very simple examples but you have an issue with the water of your river. You know, somehow you have to go and define I mean, which issue in particular we talk about pollution I will talk in. So what what we do is we really put the two communities together as early as possible, and then we accompany them into the conversation that follows. In your particular case I think it would be exactly the same. What we try to avoid is, is the famous answer looking for a question. So you know just decide on a project decide on on a methodology and then looking for places where it could be applied. The idea is starting from the community, because if they don't have any issue which is hard but it can happen, then you don't need a citizen science project there. So the idea is really that very natural, you know, communities and researchers that are there should come up with what is really an issue and then we see if citizen science can be an answer or not. So that is usually the way the way we work. Another question I mean you mentioned and then social I mean, again, that we have a lot of people that reach out reach out with the purely kind of social questions, you know, gender violence we had these ladies we work with in India. We wanted to map, you know, sexual harassment in Mumbai. And she just came. Basically, this was the whole idea and then actually she did a lot of work. But, and then we find out, we try to work together how you can, you can track it out you can map it and actually this project is called safe city you can go and watch it is, is really amazing. A lot of, a lot of issues are social at the beginning, even water quality is a social issue so I don't really see a lot of differences between the two honestly I mean you can really. I think that that especially the moment you involve citizens an issue becomes a social, you know, unless you are talking about astronomy which is, you know, the case as well for citizen science projects but you know it's more and more issues that really have an impact on on society. There's only limited technology, technological skills and, you know, you, you don't have to necessarily use a smartphone, even though we know very well that nowadays, you know, a lot, a lot of people as a smart have a smartphone even in developing industries. They may not have internet connection all the time. But again, this is not an issue most of these are just get stored the data until you know you get somewhere and you and you upload them. You don't need any way a smartphone. Some of this project first of all use just kids simple kids can be low cost. You know, we collaborate a lot, especially in my past I collaborated with maker spaces and fab lab all over the place so they can build the key on their own. When you are talking about, you know, sampling water of sampling air, but also, I would like to mention UCL UCL University College in London. They developed an app, which is for non literate people people that cannot write or read is all based on images. And again, just the result is the same people collect images geolocated even though, you know, they've never used an app before. This is true that most of the time. I'm thinking about a project that I work with in Tanzania. They wanted to map water points, you know, water sources in the different villages and, and so they got a big donation by by a foundation and they donated the smartphone to the schools and then the kids said in the mapping. So you have, you know, several way you can try to cure the lack of technology or lack of, you know, availability of technological tools. I don't know if I'm answering to your question feel free to interrupt and absolutely perhaps just also on the data storage platforms. It really depends in our case we saw our data in our service at the University of Zurich in Switzerland. We have a lot. I mean we respond to all of the privacy or, you know, the European GDP are plus the Swiss rules and regulations. We have very little personal information. If the project has to do with personal health information. We do require the blessing of the ethical committee of your institution before before we implement the project. And at the moment that we have been storing data in our servers and we never had any particular issue. However, again, depending on on the storage and depending on where you want to store your data. You know, it's really, it's really not a mandate that you can, as long as you are very careful about this legal and ethical aspect that you can store them anywhere. Thank you very much. I am sure that many others have questions, so please just unmute yourself and. Yeah, Fergus here. I'm just wanting to try to get things very practical. If we take one of the elements of the objectives that we have. It is to assess the performance of agroecology approaches or practices across a broad range of context to understand, you know what's motivating to do people to do things in different places and whether or not what the outcomes of doing different taking different courses of action. So if we just take that as a context, then immediately the the the issue of the relationship between the scientists and the citizen in this case the participants will be farmers of one type another or farm workers people, you know who are involved in agriculture or other parts of the food system. We are doing things and but but we are interested as much in their evaluation criteria as in any predetermined scientific understanding that we have of performance measures. We want to have something which is inclusive in terms of the measurement as well as in terms of what should be measured is as contextually variable as the measurements themselves. And of course there will be both ecological, biophysical, if you like, and social dimensions to those performance measures, even if we take something simple, like an integrated pest management practice, you know why it's what impacts it has on people will be a mixture of these things. So, even if we take, you know what what appears to be a relatively simple starting point like that, if we wanted to organize something across, you know, many different contexts where of course there'll be different practices that are relevant. How feasible is that. And how would we ensure that it was networked so that people are learning from the experiences that other people have, even if not all of the knowledge is transferable because what works where and for whom does have that contextual specificity so being able to understand what's context specific and what's generalizable becomes, you know, a key issue. Thanks. Thank you very much. Yeah, I, I mean it's, let's say, if I think about the example that you mentioned so pest management. Okay. And what what I, what I was trying to express if you want in in like talking about this collaboration between the citizens and the scientists that has to start as early as possible. In this case, he would basically translate into this kind of question so you want to monitor, you know, past presence or evolution or whatever. What exactly what aspect of pest management exactly you would, and then you would have the opinion of the scientists and the scientists would they well, I mean to actually learn something that can be then use at the level of, you know, policy or anything. We need to monitor these these and these aspects. Okay, this would be like the typical approach of a scientist and maybe valid across the globe or again can be like location dependent, but usually scientists. Usually they have to have clear somehow what they would need in order to make use of the data provided. And then you look at it from the point of view of the farms. Okay, and of the farmers sorry and the farmers would be sitting at the table and listening to this and saying well first of all discussing you know if this can be done or not. We can also discussing what is in there for them. You know, why should they provide this they should first of all understand what the scientists would do with the data and how the data that they provide would be useful to them and to other on the longer time. And then they can also tell you what they are available to collect or not because the scientists or the policymaker or whoever may want to you know a certain information for a certain amount of time, and the farmers can tell you well you know I cannot or is too long or I'm not available or so is all of this mediation in order to find a situation where what you're doing is clear for everybody. Everybody has a stake. Everybody has a clear you know interest and the clear benefit in the participation and only at the point you start talking about the tools. And which at that point are the list of your problems, because again, a lot of tools exist that which are free and available, and especially, again to pilot projects, really you don't have to do anything, particularly, you know, expensive. I don't know if this answer your question but this is a little bit, you know, the way you go about starting this kind of projects is a conversation that has to start somewhere though. So, again, it can be an NGO that comes to you and they say, you know, we want to monitor we want to have more information about this certain past. And then, you know, probably, there is a reason for that the farmers have a reason, but that the scientists needed to understand it, and need to agree with that. And for us is very important that these collaboration because is somehow one of the few things that guarantee that at the end of the story, the data that you have our quality data and can be used for the scientific if you are, you know, a researcher and a professor, but if you are an NGO or if you are, you know, a villager a group of farmers, you want that data to be used for policymaking, you know, to change things. And so the quality is essential for for everybody involved. Fergus, did I answer your question. Yeah, that's super thanks. Thanks sir. Yes, thank you Lisa and thank you. Thank you Rosy for this very interesting and inspiring presentation. I really love knowing more about a citizen science actually when I first heard the term in our meetings with the TPP on AE I was kind of attracted to the, to the concept because as a farmers association, we are a farmers association in, in Asia. We have always advocated for farmers as also scientists with their own, and with their own knowledge and wisdom that should be respected and considered and incorporated in the mainstream or professional kind of science. And from your presentation, I really, I got to two main points no in conducting in on on citizen science one is we need to be very clear now what is the research question after all, science starts with a question or a hypothesis and, and the second thing is the second thing I learned from your presentation is that if we want to have many citizens to contribute, we should tell them or they should feel the benefit of the of their participation. So that's, that's my second learning that which I get from your presentation. And I'm also glad to know that this can be applied in, in analyzing social issues and in making policy recommendations not because what we want in this million voices initiative is agroecology and agroecology is not your kind of like birdwatching or something like that but it's really a very comprehensive term, comprehensive term for a kind of a system of agriculture that we want to mainstream or we want. Okay, so, so these three things and so it's not a question but I would like to start this brainstorming process and maybe hear from you, because when we heard this million voices initiative. The first thing that we that we thought was, oh, our farmers, you know, we have, we always say that we have 10 million farmers as members. And maybe we could just get a million of them or even just 10,000 of them to, to, to tell us if they are practicing, for example, agroecology, and what kind of agroecology they practice what kind of system, what, what are their challenges. What are their needs, and when we get this data, we will be able to be more know what what kind of issues and problems and challenges to address and therefore our policy recommendations can, can be adapted to, to what the farmers say but I really don't know. So will it be feasible because the, the examples that you showed us is really very concrete like how many birds what kind of animals you see in the cliff but agroecology is encompassing so should we, for example, have like, this is a 10 year, 10 year project for example on agroecology citizen science, then we for for one year we narrow, we narrow agroecology into one aspect and get data from the citizens about it. I would want the co-creation part, the co-creation part with the scientists and the farmers who are inside the TPP AE because that's the composition, very glad that that's the composition also of TPP AE. So in terms of really doing this, should we focus first on one research, narrow the agroecology into specific research questions and do that citizen science per per question, something like that or can we do a more comprehensive one. So what do you think how should we start. Well, I mean that is that is a difficult question and is certainly not for me. I can give you my, you know, immediate reaction which is based on the little that I learned reading a little bit about agroecology in these days and as you said, is a concept that encompasses a lot of aspect, right. So if I put myself in the shoes of a farmer somewhere, and you come to me with, you know, with a survey or with something that asked me, you know, do you do agroecology and and what do you need. I don't know how many. I mean, I didn't know honestly what agroecology was until a few months ago, and probably farmers do, but probably they don't. So I don't know, you know, how effective and how useful something as generic as you know are you doing agroecology and you know what do you need I don't know how effective that could be. Personally, I always like to start small and very concrete so your, your approach that you just said, maybe use one year to focus on agroecology on a particular application on a particular field and gay can be you know, I don't know water can be pests can be whatever just pick a topic. And then on that specific topic I imagine that you can formulate your questions, which are always the same question you know trying to understand the farmers do agroecology or not or what they need but you can formulate the question in a way that I do believe the farmers will understand much better, because it is exactly what they do and even without knowing the concept that they will be able to answer to your question. You know, me, I will say, yeah, I would advise you start, you know, trying to focus a little bit more and then maybe with time you open up. But you know it's just my personal opinion so I mean all of you are way more expert than I am, you know, on what the farmers know or don't know about agroecology. But you know, another thing is concerning the tools. Yeah, if you want to go with the survey, you know, that would be very easy there are there are a lot of ways of doing that. So, again, the technology is never really an issue in this kind of projects. Sorry, I'm being picked up from the office here. Just us to be mindful of time, would we perhaps have one more question and then I would encourage all the participants to continue thinking about it so we'll share obviously we'll share the recording and the presentations. And this is an ongoing process that conversation as we all know. So if we have more questions, please put them in writing share them with us. But if anyone would have one last question, one question perhaps. No, I guess you can go Lisa. Can I just say one thing. I think just putting together that last interaction between Esther and Rosie. I think, you know, having a survey with, you know, kick clear questions in it, but that goes to a very large number of farmers who are Afsa, Afa, and so on, so that you get back some basic feedback on important questions, which allows you to then decide which areas to focus on would be a double win, because you'd be getting immediately a large number of voices, giving you some feedback, which is also usable for policy purposes and all sorts of things because it's giving you an idea of large numbers of farmers who've got particular interest or or issues. Obviously the design of that survey would be would need to be very well done, and it would need to be potentially, you know, different in Africa But nevertheless, if it's well designed that could produce a huge immediate payoff in terms of having something about the scope in a big way and allow you to then focus more detailed studies around some of the key priorities. So that that does sound like quite an exciting way forward that people might want to think about and I would add that to that first on that that survey needs to be co-designed with the people. Absolutely. Thank you for emphasizing that, Ms. Rosie, because for us we would really want a co-creation of any citizen science project on agroecology. We have many research questions. We have research questions at the farmer level, for example, how do you do? How do you do agroecology when this pest attacks you? What you can do? How do you, what are the tools out there? What are the technologies out there that farmers are already using? So we if we could have that knife, we could have that and then what what what policies are there already that that farmers find beneficial so that others who don't have that policy can can then try to tweak it to their own local and political context. So things like this or how much is your government supporting you to do sustainable agriculture, to do climate resilient agriculture so that it can help us do advocacy work for the government budget that we will need to to maintain sustainable agriculture. So things like this are so many, many questions that we want to know from our members so that we could make very intelligent analysis and then policy recommendations. And help them with the tools and the technologies because they want to know how to do when the pest attack, when there is drought and you don't want to, you don't want to apply chemical fertilizer. What to do? Yeah, I'm thinking, since we are eight minutes over, let's let's end it here but I'm sending an email with the recording with the presentations, and then also with a way forward so we continue meeting with the regional leads. And I will definitely make sure to stay in interaction with Rosie and bring her back in whenever it makes sense for us. And then we can discuss those questions and what to tell us that it's very important to your eyes. Thank you very much. Would anyone like to say last what we close it here. Okay, I think we can close. Thank you very much, Rosie. You're welcome. Thank you. Looking forward to collaborating with you. Bye. Thank you so much. Thank you so much. Bye.