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Dilek Fraisl on citizen science for policymaking

Dilek Fraisl on citizen science for policymaking

Science for Policy podcast episode

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Broadcast on

8 April 2024


Show notes

When countries set themselves ambitious targets such as the UN sustainable development goals, then realise they don't have the evidence sources they need to monitor progress towards those targets, how do they square the circle? In some cases, it's with so-called 'citizen science', in which non-professional scientists gather and evaluate data — often on a big scale — to fill the gaps.

Dr Dilek Fraisl is an expert in using citizen science to address sustainability challenges. In conversation with Toby Wardman, she discusses both the value of using crowdsourced data, and the challenges that arise when presenting it to policymakers.


The transcript below was generated automatically and may contain inaccuracies.

Toby: Hello, welcome to the Science for Policy podcast. My name's Toby, and today I'm joined by Dr. Dilek Fraisl. Dr. Fraisl is a research scholar at the International Institute for Applied Systems Analysis, or IIASA, in the research group focusing on novel data ecosystems for sustainability. She's also the managing director of the Citizen Science Global Partnership, which is a network of networks that aims to advance citizen science for a sustainable world. And she's a consultant to the United Nations Development Program and no doubt involved in many other global sustainability initiatives. So Dilek, welcome to the podcast.

Dilek: Thank you, Toby. Thanks for having me.

Toby: Now, just in case regular listeners are smelling a rat here, yeah, it does feel to me too like in the last few months, I've interviewed quite a few different people with some connection or other to IIASA. And I admit, you know, it's a big organization and there've been a few different episodes on sustainability. But I am starting to suspect that, in fact, IIASA is some kind of global cabal that's pulling the strings across all of sustainability science. Have you secretly got the solution to climate change already and you're just waiting for the right moment to unleash it on the world?

Dilek: Not yet, but IIASA is a research institute that conducts policy-oriented research. That's why I guess that makes it very interesting for this podcast and for people who work at IIASA.

Toby: All right then, what's your particular slice of the IIASA sustainability empire?

Dilek: Yeah, I work on, as you mentioned while introducing me, and thanks for that nice introduction. I work in the Noble Data Ecosystems for Sustainability Research Group of IIASA, and what we do is to utilize new sources of data, particularly to address sustainability challenges. So we are a data-focused group. We do a lot of work on Earth observations, citizen science particularly, and at the intersection of both. And I'm also focusing a lot on citizen science, as you have mentioned. And citizen science is a data source, but also a way to influence policy. So I can describe if you want, if that's interest to you, what citizen science exactly means. But I guess people here listening to this have an idea of what citizen science is.

Toby: Yeah, well, I mean, my basic understanding is that this is science that's not done by professional scientists in labs or in controlled environments, but somehow or other by citizens. I mean, that's kind of what it says in the tin, I know. Beyond that, I'm a bit vague, so please do sharpen me a bit.

Dilek: Actually, I'm not sure if you can define it that way because citizen science could also be done in the lab, but people contributing to it is a way, is a nice way to explain it. So unfortunately, within the global citizen science community, we still argue about the definition of what we're doing. So we call it citizen science, others call it participatory science. Some call it crowdsourcing, some call citizen generated data you may have heard of or participatory mapping. There are many, many different ways to explain this public engagement in scientific research and knowledge production activities. So I actually define it intentionally, very broadly, by saying citizen science is actually active public participation in knowledge production, in the production of knowledge, be it a scientific research, be it a type of research that affect communities and people's lives, and people want answers to that. So this kind of research is actually citizen science. Maybe I can add that there are so many different terminologies to describe similar activities is because they all come into existence in different contexts, in different scientific disciplines or in different fields, and they reflect, the terminology reflect actually the background that they come into existence. So it's really much reflecting the peculiarity of the field that is actually originating from. I call it citizen science because I come from the academia perspective of citizen science or these public engagement activities. So in our work, we call it citizen science, but we embrace all these diverse practices, methodologies, definitions and terminologies.

Toby: Okay, I think I would like to dwell just for a moment or two longer on this definition question. So we've been saying, broadly speaking, this is science that involves citizens, and so not professional scientists, not people whose day job it is to do science.

Dilek: Exactly. Yeah.

Toby: But of course, it doesn't include, I guess, all the areas of science where citizens are involved as like sources of data in themselves, object of study to use rather brutal phrase. So it doesn't include, for instance, social sciences, even though social science studies and experiments often have lots of people, lots of citizens.

Dilek: Well, that depends. There are different areas within social sciences that citizen science methodologies can also be used. It's not necessarily for as we know more or as we hear more in environmental and ecological sciences. But actually, that is the area where the term has initially came about. So, yes, it is very much present in environmental sciences, but more and more we're seeing a lot of disciplines are actually utilizing this kind of approach in their own areas. But you actually also mentioned something really important when you were saying citizens. I wanted to highlight that as well. So there are a lot of limitations with this terminology. And for instance, just recently, the US based Citizen Science Association changed their name to Association for Participatory Sciences or something similar to that, because they couldn't really associate the communities or in their own context, the word citizen with the word citizen, because citizen might mean you're actually, you have a piece of paper that connects you to a land. But we're talking about, so we want to be inclusive when we're talking about citizen science or public participation activities. We don't wanna necessarily use terms that may exclude others, even though that's not our intention. And some argue that science is also not very inclusive of a term because it may really sound like, I'm not a scientist, so I shouldn't be part of it for people, for public.

Toby: Yeah, okay. And you mentioned talking about your own work that you look at new sources of data. So what new sources of data? And how does citizen science go into that?

Dilek: Yeah, citizen science is actually, I use that as a source of data in terms of the term. So citizen science can be a lot of things. It can be about awareness raising. It can be about achieving behavior change. It can be about informing public about a particular topic, but it can also be about data. Citizen science activity is usually about collecting data on a particular topic. So in our work at IIASA, we basically want to utilize these data, reuse these data for a lot of other purposes to address a lot of challenges that we're facing today from environmental degradation to poverty, for instance. So citizen science for me is a source of data that we can utilize to address the challenges that we're facing today.

Toby: Yeah, it makes sense. Could you give some examples?

Dilek: Yeah, sure. For instance, at IIASA, in our research group, we have projects that are about mostly environmental monitoring. We initially started from land use, land cover monitoring. When I first started at IIASA, the research group was focusing mainly on that aspect. So how the land use and land cover changing throughout Europe initially. And then we developed different tools and technologies and apps, for instance, to collect data on that. And then we started to work on other areas as well. So we are even now working to help measure an SDG indicator at IIASA that is about citizen satisfaction with public services, which is a policy indicator actually, under the UN Sustainable Development Goals.

Toby: Gotcha. So is this kind of work filling gaps that otherwise couldn't be filled? Is this citizen science delivering sources of data that conventional, as it were, capital S science just simply couldn't do?

Dilek: Yeah, you could say so. For instance, ideas of what I focus on is actually linking this kind of citizen science data, whether it's from our own projects or from other citizen science projects that are initiated by colleagues and by partners in Europe and worldwide. So how we can link these data to address these sustainability challenges fully? Because we know that. I don't know if you're aware of the UN Sustainable Development Goals. Probably you are. So these are 17 goals and they have 169 targets. And the countries committed to achieve them by 2030. Once they have been adopted, there were a lot of issues about even methodology. So there are 17 goals that are addressing health, education, gender, inequality, or many, many different topics. But what we didn't know, how we could actually measure many of these things. For some of them, we had ideas, we had some indicators, but a lot of them were new, particularly the environmental Sustainable Development Goals. The UN actually came up with a framework, an indicator framework, that would help us measure progress towards those SDGs, because we know that we cannot achieve what we cannot measure. So if we want to achieve the SDGs by 2030, we need to know today where we are good at and in which areas we need improvement, where we need policy changes, where we need more funding or resources and so on. And now we have 231 indicators that should help us measure our progress towards achieving the SDGs. But there are a lot of data gaps, so current ways of measuring progress towards achieving these SDGs rely on traditional ways of data collection by the National Statistical Office's ministries, by the countries, let's say. These are household service censuses. They are not really out to date and they are very expensive to implement, the country-wide. So in the end, we have a lot of problems about having timely, accurate and comprehensive data to be able to understand where we are today and where we want to get to in 2030. Our work is about basically proposing new sources of data that haven't been used in the past traditionally and integrate these data into the monitoring of the Sustainable Development Goals so that we have a better understanding where we are. Because as I mentioned, even today, actually, we're in 2024 and we have about six years to achieve the SDGs, but about half of the environmental SDG indicators, and there are 92 environmental SDG indicators in the SDG framework, and they lack data today. So we don't know even where we are today for half of them. And we're talking about achieving them in the coming six years. So we really need these kind of new sources of data to come into the picture and to be integrated into the official statistics. But that requires a lot of changes, especially in the mindset of politicians or official statisticians and a lot of other things. And this is not happening from one day to another. It's a slow progress, but it's happening.

Toby: Yeah. I mean, this makes sense, right? If you have evidence gaps and you need to fill those gaps because you've set yourself some targets that you need the evidence for, of course, you have to go and find ways to get that evidence. You got to fill the gaps. But on the other hand, we look at how policymakers have traditionally tended to get or aspire to get anyway, scientific input. The sources they tend to draw on for advice are often quite a long way from this kind of crowdsourced amateur science. So you have institutions that are consciously set up to give access for policymakers to, quote unquote, the best scientists, the best science, you know, with academies and elite universities and so on. And there's political language also about wanting to make sure only the highest quality evidence, the highest quality science, influences policymaking. I mean, I work for an institution like that, right? That's how we talk about ourselves and the science advice mechanism. And it's not just that policymakers want, like, the most accurate evidence. I mean, no doubt they do, but it's also that we tend to think of science, professional science as like an edifice conforming to certain norms and policing itself to make sure those norms are conformed to, you know, things like transparency, independence and so on. I guess this is my meandering long-winded way of asking a very simple question of how receptive are policymakers to your kind of science, this non-institutionalized amateur driven stuff?

Dilek: Well, this is understandable, actually. There's no direct way to answer that. But that makes a lot of sense to, you know, base decisions to the best scientific knowledge. But just because some information or some data produced by the volunteers doesn't make it automatically bad quality. So quality of data is really sort of an issue for the uptake of these kind of activities, be it crowdsourcing or I use the term citizen science more generically to cover all these diverse activities. So it's about data quality. They argue a lot with us or with people, you know, citizen science practitioners and researchers. How can I actually trust the quality of these data? How would I know this is actually the best available science? And we try and answer that question is basically, as I said to you, just because simply is produced by volunteers doesn't make it bad quality. You need to look at the details of that kind of data source. So you need to first understand what's happening. And to be able to do that, sometimes you just need to sit together with people to understand how they have been collecting these data, what the methodologies are, and how they have been actually implementing these methodologies on the ground, these volunteers. And a lot of citizen science projects actually publish their results very openly. They publish their metadata, they publish their results, they publish how they reach certain results and conclusions from their research. So this information is openly available. And all we need to do is just to start the conversation around it. That said, citizen science is not always initiated by scientific institutions or research institutions. It could also be initiated by communities and peoples themselves about the issues that matter them the most. And sometimes they do not have this data openly available out, you know, because they are not producing the data necessarily for scientific reasons. They just produce this data to address an issue that is a matter of concern to them. But that also doesn't make the quality bad. We just need to understand. We just need to start creating a conversation. And usually these are the civil society organizations. Some of them have even their own research teams and groups that actually create these methodologies, look into them scientifically and so on. So I think this is all about having a conversation and discussion about what are the things and what are the issues if there are. For citizen science that usually coming from the academic background, usually these are openly available and people can easily access them. But for other types of initiatives that are initiated by civil society organizations, there are a lot of questions by the policy makers. And in that case, we strongly advise to sit together and understand how the data were collected, what were the methodologies, and also to initiate a conversation about how to make it more useful and better for, you know, official data and statistics or policy making in the future. Because we need to work together to advance this kind of areas, because policy makers desperately need this kind of data and information.

Toby: Yeah, I mean, of course, that's the killer argument, right? You can be as sniffy as you want about only using the purest, highest quality data. But if you need a particular set of data and you don't have it except via citizens, well, you know, suck it up. But I want to pick up on a couple of other things you mentioned, too. You said that just because data is crowdsourced by volunteers, that doesn't mean it's necessarily low quality. You know, volunteers can do good science. No doubt that's true. But on the other hand, data that has been carefully derived by regular, traditional scientists under controlled conditions, you know, papers that have been peer-reviewed and bounced around the scientific world and so on, that does supposedly guarantee, or at least greatly increase the chances, that you do get high quality because the scientific endeavour includes these checks and balances traditionally. So it's not like we have to assume that anything that comes from outside that scientific endeavour is automatically dubious. But we might still say that the traditional scientific approach is what gives us the best shot at avoiding dubious and focusing on useful. So then, in that case, are there ways to reassure people here when it comes to citizen science? For instance, do you have like professional scientists supervising the experiments or evaluating the data?

Dilek: Well, what I propose, I'm also involved a lot with the UN, as you were mentioning in the beginning, and statistical offices, the Statistical Commission, and that kind of community, the official statistics communities. And what I usually propose when these kinds of things happen, yes, if under scientists' control, so we design our own crowdsourcing projects at IIASA, and we know the research questions. We know how we can ensure data quality. There are different ways of doing that in our own work. And this is really dependent on what kind of project you're handling. There are different ways to assure data quality. For instance, what I propose when it comes to the civil society organizations to the UN a lot is to involve academia into these conversations. So there's a recent initiative that was launched by the UN Statistics Division, and it is basically the UN Statistical Commission mandated two years ago, I guess, that there is a need to understand and explore what these citizen data initiatives are. And they used the term citizen data because they got really confused with the terminology, because civil society organizations use citizen generated data more while we're using citizen science, and there are others coming into the picture. So they said, we want to be inclusive and let's call it citizen data. So they launched this collaborative on citizen data with a lot of UN agencies are also contributing and supporting, and a lot of civil society organizations are actually part of it. There were several meetings and some output that was produced that is under consultation right now. When they want to be inclusive, they talk more about civil society organizations. And I'm always proposing or suggesting in that group that scientific community or citizen science, the communities that are doing citizen science can actually be a good bridge between the civil society organizations, as well as the policy makers or national statistical office representatives, because they have national statistical offices have high quality standards versus civil society organizations. They may have really good quality initiatives, but their main concern is not to collect data that could feed into the national statistical offices work. They collected these data to address an issue that they are working on, that they are handling. So if academia comes into the picture, it could really be the bridge between these diverse communities in terms of ensuring scientific practices. And if there's a need to help these civil society organizations to improve their data quality, academia can really come into the picture as a bridge to achieve that.

Toby: That's really interesting. So I'm sure you know when people talk about the science policy interface, they often talk about bringing together these two different communities, two different worlds, right? So in the standard model, it's the scientific community and policy community. And so you get these boundary organizations that insert themselves between the two, the boundary, obviously, to try and align the two worlds and enable them to communicate effectively and be useful to each other, basically. And we sometimes talk about, oh, those poor scientists, they're stuck in their scientific paradigm. They really need help to understand what the policy needs are. But now you're saying, no, in a way, the scientists are the boundary organization here because we have a community of citizens and we have a community of policymakers. And the scientists are the heroes who can try and align those two worlds. They're the ones who understand both the policy needs, what's needed, when and how, and the situation on the ground which the citizens experience. And they're the ones that can connect those two.

Dilek: Yeah. So citizen science is also not perfectly accepted, right? So let me get that right first. It's not like citizen science is really great, and all the national statistical offices want to accept and use these data. That's unfortunately not the case yet, but we're getting there. So we published this study. It came out in 2020, but we actually, I started to present it to these official statistics communities and to the UN already in 2019 or even 2018. So it was about linking the data from diverse citizen science projects to the SDG indicator framework. I mentioned that there are 231 unique indicators in the SDG framework.

And countries and national statistical offices have a lot of issues gathering data to measure them, but they sort of are committed to do that. So I was just going to this UN meeting where national statistical offices of countries come together to discuss this indicator framework, whether we have enough data, how we could actually measure this, whether this indicator is actually a good indicator to measure progress toward achieving this target and so on. So in that meeting, when I talked about citizen science, a lot of the people have never heard the term before. And I'm talking about 2019. And now we're talking about UN statistics division being mandated to explore citizen data approaches. So this is a big step, but it's slow, but it's happening. Even when I talked about citizen science, it was initially like, you know, are we sure about this? What is this exactly? Can we ensure the data quality? Even within the scientific community, there is still resistance to citizen science approaches because it involves volunteers. So a lot of traditional ways of doing science or people who are doing that kind of scientific activities may be reluctant or may have a lot of questions. But when we talk about sustainability and sustainability science, we can't do it without citizens. We can't do it without transdisciplinary approaches, which also brings in the knowledge of people into the picture. Because without it, we can never achieve sustainability.

Toby: So the trust is starting to develop now.

Dilek: Yes, citizen science and policy probably in a much better position than if we consider civil society organizations that are collecting data mostly for advocacy purposes. And that creates a lot of questions about whether we can trust the quality of data within the official statistics community. But what I'm trying to say is really we can come and be bridge between these communities as the academic representatives, academic people being on that aspect of citizen data activities.

Toby: Well, yeah, I wonder about that a bit. I think when we talk about citizen science in the abstract, we mostly have in mind this model we've been discussing so far, which is these benign communities of large numbers of volunteers who are collecting data because they want to contribute to fighting climate change or protect biodiversity or, you know, express their own perspectives and new identity or whatever it is. That's the idea we have in mind. But then when you start talking about NGOs, civil society, single issue groups doing their own research and coming up with results that they then want to use to influence policy, that puts the whole thing in a slightly different light. And I know this is what you're starting to suggest, but just to put it like as directly as I can, we're not anymore imagining enthusiastic private citizens collecting data out of the goodness of their hearts and hoping it's helpful. We're talking about advocacy groups with clear agendas and clear objectives. And this brings up questions of motivation and bias and so on. So I get what you're saying about the scientific community having a role there to try and figure out if the data is good enough quality to inform decision making. But it's not just about that. I mean, you might be able to persuade your civil servant or your statistics officer that this data is good quality. But then we also live in a political world where politicians are going to be extremely squeamish about being seen to rely on data that doesn't come from quote unquote objective sources. Even if everyone involved in the process is convinced that it's decent data. And you can imagine there will be opponents of whatever final policy decisions made who will scrutinise that decision. And if they find that it was informed by data from whatever group that's politically opposed to them, they will absolutely make hay with that.

Dilek: Yeah. So as I mentioned at the start, we need changes in the mindsets. So we need to make those civil servants or official statistics people, representatives to be more open. But in order to be able to do that, we need to demonstrate actually, and not only in theory and not only by publishing scientific papers, we need to show actually these approaches really have a lot of potential and they actually work on the ground. At that point, you will start seeing people showing interest in what you're doing. And because they also desperately need data, they will turn to you if you are able to show that this is actually working. So at the end, we're in a unique position to actually make our science policy oriented because our work is about producing scientific results that could actually be used, uptaken by policy. So in that line, we're not only publishing theories and scientific papers that is done maybe in a more traditional way, but we're also trying to see the results or implement these scientific initiatives on the ground. One example that I would like to give, we recently, not so recently anymore, but since two years or so, we started the work with the Ghana Statistical Service. So I was speaking in this statistical meeting with the official statisticians from different countries, and then I talked about the potential of citizen science, and they were asking me like, what are the areas where there are citizen science data that you're telling us we can use and reuse? And I was giving very concrete examples. This is the project that could help monitoring this SDG indicator. And one of the examples that I have given in that meeting was marine leader. So there is an SDG indicator under SDG 14, which is life below water. So it's about oceans and waters and so on. And there is an SDG indicator underneath that. So this indicator is about plastic debris density, and it's actually about measuring plastics in the oceans, in waters, on the beaches, and how actually plastics ending up in our oceans and waters. So this was a new SDG indicator. Even the methodology of it was approved in 2019. To my knowledge, there were no official statistics that were out there that was actually measuring marine plastics or plastics in general. And all of a sudden, there is this SDG indicator that talks about plastics and how this could be measured. And there are different layers to this methodology. It's quite complicated. But one is on a national level using data from beach cleanups, citizen science initiatives. So the methodology was approved in 2019. And I was just talking about how many citizen science initiatives that are actually organizing these beach cleanups, cleaning the beaches, but at the same time, collecting data on the litter that they find. And then publishing these data in an openly available platform that anybody can access and see at any given time. And even deep dive and see the locations of where these cleanups took place, how many pieces of plastics were collected, and what kind of plastics they were. Because it is important to know this. This will have important policy implications to know exactly what kind of plastics, right? So you could actually access this data, and this is openly available. The methodology was approved in 2019, and we were almost in 2023 or so, 2022. There were still no country that was actually looking into this or exploring whether this could be done, because they are under a lot of pressure to gather data for other, measuring other indicators or their own national issues that they are covering. At that meeting, when I was talking about it, I was approached by the Ghana Statistical Service, because one of the country priorities in Ghana is to address marine plastics. But they don't know how to address it, because they have no official data available in the country about the plastics or the extent of the plastics problem. So we looked at data that are openly available for Ghana, and we brought in partners. For instance, there was this global data platform on plastics that shows beach cleanups from all over the world. It's a global initiative that a lot of civil society organizations are part of and using their methodology to collect litter and data globally, everywhere. And they had data available for Ghana. So we looked at this openly available platform. We got in touch with them, and we brought these partners together, including the civil society organizations that are actually using their methodology of that initiative that are collecting the data. And we created this partnership with civil society organizations that are using this methodology. This initiative that is based in the US, who created that methodology for civil society organizations to use and to collect data. They even have an app so you can go out there to your beach and then start collecting data, taking a photo of what you found and so on. There was data openly available. So we were talking about whether these data can be validated or can be high quality enough for Ghana to consider as official statistics. So we went through a long process. So we were just trying to understand whether these data are actually high quality enough, meet the requirements of the country, the quality requirements and a lot of other things. To be able to achieve that, it was all about bringing the right actors around the table, bringing these partnerships. And then we showed that Ghana has become the first country actually to use citizen science data for reporting on that particular SDG indicator on plastic debris density. It's the first country. They use this in their own country reviews that they submitted to the UN. And they reported these data as country validated data to the official UN SDG global database. And they become the first country. And the results of these data, because it's official statistics, are now also being integrated into the integrated coastal and marine management policy of the country, which is currently under development. So it's all about having the need. And when you have the need, and when you don't have official data to actually back that kind of need up, you can actually come into the picture. So that was the low hanging fruit for us that we could demonstrate how impactful citizen science can be in terms of, you know, also in terms of the quality aspects, in terms of all the other things. And then we work with them. And then all the partners were, they all came from different backgrounds and perspectives and so on. And their number one worry was different, maybe, priority. But they were all open to conversations and that made this work happen. And this was the first time that citizen data, citizen science data was used to measure progress toward that indicator. And then we openly, you know, published our results as a scientific paper in a peer review journal. And we mentioned the limitations as well. So the data weren't perfect, but it was quality, high quality enough to address that specific policy need that the country has agreed on that. And that was very important. That is what matters as you were talking about it at the beginning.

Toby: Yeah. So we're getting there in policy circles. Are we further ahead in scientific circles? Do scholars already recognize this stuff is valuable and useful?

Dilek: I think this is more and more being acknowledged by the scientific community as well. Because I heard from my colleagues, they told me when they published to a high impact journal, let's say 10, 15, 20 years ago, a scientific paper that talks about the results of a citizen science initiative, they weren't getting so far. They were being rejected. And some of the reviewers had these sort of comments about this is not good quality enough, we cannot trust this and so on and so forth. And so there were resistance within the scientific community more back than now. Now citizen science data and results are everywhere. They are being published in high impact journals and so on. The scientific community is also becoming more and more open to these kind of approaches in comparison to what I've been hearing from my colleagues that have been around and doing this longer than I did, or I've been doing.

Toby: It's good to know. So what's the future of this kind of work?

Dilek: I think we're getting there. Also in terms of having these data officially accepted by the UN, by the countries and so on. So the more we are able to demonstrate the impact of these kind of research, the more it's getting recognition. But it's not only about filling data gaps, closing data gaps, but it's also about acting together to really achieve change because this is what we need. When I'm talking about the data gaps and how citizen science can address these data issues, please don't get me wrong. I'm not actually overlooking the potential impact and already existing impact of citizen science in changing behaviors because this is what we need if we want to achieve sustainable development. So a lot of citizen science initiatives are reporting the more they participate in these beach cleanup activities they stopped using or consuming single-use plastics, for instance, or when they participate in an air quality citizen science project, they tend to not use their cars to get to work or they either use more public transport and bikes and so on. So there are also these impacts of being involved in citizen science and this needs to be raised and made clear. And there's also the impact, these kind of initiatives are producing in people's lives. If we're talking about a small community somewhere in a developing country, by actually using these kind of approaches, you can keep the governments accountable and take measurements to address the issues that you're facing in your community. So this is really important and this understanding is also growing and growing. So it's not only about data when it comes to citizen science, it's also about the impact of it on people's lives in achieving sustainable development. There is this raising awareness component and behaviour change component, hopefully, of citizen science. But now there's also these bigger initiatives that are happening. So we see that there are associations from different continents that are appearing about citizen science. So we have the European Citizen Science Association, we have just newly established Citizen Science Africa Association, they have even a youth component of their association in Africa. This is huge and we also established the Citizen Science Global Partnership to bring those associations and diverse citizen science communities working in these areas together to become one single global voice for the citizen science community. So these are really, really nice things that are happening and I'm sure there will be more and more awareness that is being raised looking at the current status of where we are and where we have been like just four, five, six years ago. Funding is also growing, but in Europe more than in other parts of the world. So we really need more and more funding for these initiatives to be more impactful and to really, you know, develop themselves. So we cannot ask civil society organizations that are doing a lot of things on a voluntary basis to write, you know, to write up their results in a way that would actually meet, let's say, scientific standards or they simply do not have that kind of resources. But if we want to raise awareness on, you know, creating high quality methodologies or, you know, work with these civil society organizations to make their data and results even more useful than it is right now or uptaken by policy, we really need to build capacities, but you cannot build capacity without proper funding. So it's really important, I guess, we need to go beyond these small pilot initiatives here and there, and we provide or the funders provide really genuine support to these citizen science initiatives or the field of citizen science in general to really see even bigger impacts of this field.

Toby: Yeah, that's fantastic. So it's a pleasure to talk to someone who's really working not only at the cutting edge of issues like sustainable development, but also at the cutting edge of scientific methodologies for policy. So I do want to say thank you very much indeed, Dr. Dilek Fraisl, for this great conversation.

Dilek: Thank you so much for Toby for having me. This was a real great pleasure to talk to you.

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