 Okay, great. It seems that we are ready to go ahead with that. So first, I would like to welcome you all to this session on democratizing research and science ways forward. This session aims to reflect on how research and science is currently produced. We want to discuss ways we can actually make research and science more inclusive, open, and democratic. And for this session, we have three very interesting talks. Jess, can you show us the next slide, please? Thank you. So the first talk is from Jess Carr and it's about what makes a researcher. She's going to talk about participatory and inclusive research with people from excluded communities. The second talk is from my friend. It's about the opening research to non-professionals through what we call this community-led citizen science. And the third talk is from Bart Rientes. He should be joining us in a few minutes. And it's about the open science and scholarship in relation to learning analytics. So let's move. I guess we are ready to move to our first talk. Jess, the floor is yours. Thank you. Thank you very much, Leah. So hi, everybody. My name is Jess Carr. I am a postdoctoral researcher in the Institute of Educational Technology at the Open University in the UK. So today I'm going to talk to you about my PhD project, specifically looking at the inclusive research methods that we used when I worked with a group of adults with learning disabilities. So specifically I worked with a charity called My Life, My Choice, who are based in Oxfordshire here in the UK who work with adults with learning disabilities helping with self-advocacy. So the first thing I would like for us all to do is to have a think about what makes a researcher. So I would like you to go to the menti.com and pop in that code. If that doesn't work, I'm just going to pop into the chat a link that you can go that will take you straight to the menti meter. If you've not used menti meter before, it's just an online platform where we can all put answers in and we can see the results come up together. So I'm just going to switch my screen over so that we can have a look. So on there, what I would like you to do when you get to the website is pop in three terms or words that you think describe the essential characteristics of a researcher. So take a couple of minutes to think about it because I know it's quite a big question. But yeah, have a think about what you think makes a researcher. What do you need to be or have to be a researcher? Great. So we're getting some wonderful words coming in. So knowledge, intellectual capital, uprightness, networking, trustworthy, sincereness, independence. They're really interesting words. And if anybody just want to say anything around the words that they put up, either pop something in the chat or feel free to turn your camera on or your microphone and have a chat about it. Creativity is a great one. We'll get into that later on. Inquisitive, trustworthy, integrity. Yeah, so these are all words relating to kind of personality characteristics, which I think is a really great way to look at it. I wonder if anyone has any ideas on education backgrounds or something that's not necessarily about the personalities. Open for collaboration. That's a great one. So a couple of the ones that are coming up most often. So we've got creativity there twice, trustworthy, curiosity. And I think those are really interesting ideas about what makes a researcher. So I'm just going to switch back quickly. Two seconds. Right. So now we've had a look at that. What I want to show you is a picture of the research that I was doing. So I've blanked out the faces of my co-researchers for their anonymity. I can never say that word. What I want to discuss here are the three different co-researchers that we've got. So we've got people who come from different educational backgrounds. We've got people who have different lived experiences. We've got a mixture of people who are both neurodivergent and neurotypical. And we've got people who have differing expertise. All the people in this photograph are conducting research. Now what I would like you to do is go back to your men's meter and I will move the slide along. And I would just like you to vote as to whether you think that all of the people who were in that photograph classify as a researcher. Just from the very basic description that I've given you there. So getting lots of yeses in, which is really great to see. I wonder if anybody would like to expand on why they think that everyone in that photograph is a researcher. If you do, you can just pop something in the chat box or turn your microphone on and say it to the group. If not, no worries. I know it can be a bit stressful having to suddenly talk in a big group. So feel free. But yeah, it's all yeses, which is really nice to see. So the reason I'm asking this question, oh, you cannot activate your mics. Oh, no. Well, yeah, just pop something in the chat. I'm not entirely sure how I would activate other people's mics. I don't think I have that control. So yeah, just pop something in the chat. Maybe, maybe, yeah. That's yeah. It'd be interesting to hear from the maybe. Yeah, perhaps a better question would be why wouldn't they be considered researchers based on the short summary that you provided? That's a really interesting one. And when I've done similar presentations before, a lot of the talk around perhaps why they might not be considered a researcher is based on the academic backgrounds. And so in that picture, you had myself, so I'll go back to the slide actually. In that picture, you had myself who was a PhD student at the time. And there you've got two people who have very limited or no educational qualifications and to some people within the academic community that does disqualify them from being able to classify themselves as a researcher, which is why I wanted to open this up for discussion in the group to start with, especially because the rest of my talk is based on inclusive research where the term co-researchers is a very key part of the definition of inclusive research. So there's a quick little introduction, a little thought exercise for us all. Let's move it along though. So I'm not sure how many people here have experienced inclusive research. It's only been around, it's only been classified as a research approach since about 2008, when Warnsley and Johnson termed the approach as an inclusive research study. When I started doing my PhD, I knew I wanted to work with adults with learning disabilities, but I knew very little about research approaches. I hadn't been in research, I hadn't been in academia since I left my undergrad about four years earlier, but I had been working in the charity sector with adults with learning disabilities and I came across this great paper by a woman called Melanie Nind. She's amazing. And she asked the question, what is the research? Is it research for? Is it research with or is it research on? This is a really important question when considering inclusive research because many inclusive researchers will say that it is research with. The idea behind that is that you're not doing research for someone. That idea puts you kind of in this holy place where you're this amazing person who's coming in and doing research for them. They need your help. Research on is going back to thinking about perhaps slightly more medical trials, clinical trials, where these people that we're engaging are a subject rather than someone who you are working alongside. Now, that's a very key part of inclusive research. You're not doing inclusive research unless you are doing research with the people that you're trying to work with. And this can be reflected in Walsley and Johnson's principles for inclusive research. Now, because they termed it, these are considered very important. There are lots of principles out there as there are with many, many pretty much every research approach. You can go and look in the literature of you and find a gazillion different ways that people say you should do it. But Walsley and Johnson is kind of hailed as the original. So that's why I've chosen it for here. So the first principle that research must address issues that really matter to the learning disabled community and ultimately leads to improved lives for them. Now, when we're doing research with them, we're asking the questions, we're involving them from the very get go. Therefore, they get the control and the power to be able to say whether this does actually matter to them. It's very easy for us as researchers or just people who work in academia to make assumptions about things, especially with marginalised communities. We see that they're not being engaged in something. So we just assume that they would want to be and that they need to be. That's a great assumption to make because the idea behind it is that you want to open up your world to these other communities and you want to share what you've been able to experience with them. However, there's a chance they really don't care. You know, that sounds like a really horrible thing to say, but there is a chance there's not got anything to do with them. Sorry, just quickly in the chat. Does learning disability also include disadvantaged groups not having easy access to learning? Yes. In this specific study, in the one that I'm talking about, I worked with a group of people who were diagnosed as learning disability. So it was a medical condition, but inclusive research in general doesn't necessarily, you don't have to have a diagnosis to be classed as someone with a learning disability. Like you say, it could be of someone who does not have access to easy access to learning. Thank you for bringing that up. I wouldn't have actually thought to mention that. So the next principle that it must access and represent their views and experiences. Again, this comes from the idea that we have these assumptions. We come up with a hypothesis that we believe to be true and we do our research and a lot of research papers, a lot of the outputs that come from research might are very academic in their nature. Inclusive research and in particular the newer papers that are coming out are all easy read. So that involves using photographs and speech that is easily understood rather than academic jargon in order to represent the views and experiences of the people who you've actually done the research with. So often we see that an academic or a group of academics when they're writing a paper will write about the people that they worked with, but actually by doing something like an easy read document you can include the group that you are working with in that writing process. So it's a much better way of accessing and representing their views and experiences. The final principle that people with learning disabilities need to be treated with respect by the research community. I'm sure for a lot of us on this call it sounds like an obvious thing, but unfortunately it isn't always. Research in general can be and in particular the research community can be a very elitist and hierarchical place. We put a lot of emphasis on educational background. So it's very important when you're doing inclusive research that you understand that whilst yes you may have a PhD, a master's, an undergrad and these people may not you might have a better understanding of how a research process works than them. It doesn't mean that they don't deserve your respect and most importantly it doesn't mean that they know more than you in other ways. For me personally I went into this study as someone who does not have a learning disability. So whilst I might have known a bit more about the research process I did not know anything about being a person who lived with a learning disability. I did not know how that affected day to day life. I could guess from my work in charities, but I didn't have that lived experience and it was very important that I then respected that they had more knowledge than me in that situation. I'm just going to have before I go to the next slide have a quick look at the chat. I've seen that someone's asked a question. Interesting of medical classification. Yeah so I think learning just the terminology around learning disability is frequently changing and it's very hard to keep up with to start with. So now often you'll hear terms like neurodiversion over a learning disability. In certain papers, in certain journals, you hear it discussed as intellectual disability. You'll hear it discussed as learning differences, all these different terms. And if you go on to like the NHS in the UK or other health bodies in different countries, they will have their own terminology relating to learning disability. But a lot of people will live with what you might consider a learning disability and not have visited a doctor or have a diagnosis. And actually inclusive research specifically tries to move away from those medical classifications. It's all based around the idea that there is a medical model of disability and that by terming someone as having a disability, you automatically create barriers and differences within their lives. Whereas actually moving to a social model where you understand that it's not necessarily the differences that they might have. It's actually the social approach to a learning disability which disables a person. So that's where that's kind of coming from in terms of the way that they classify learning disability with an inclusive research. Okay, so I'm just going to talk about how you might do this research. Within the inclusive research literature, there is an awful lot of different ways in which to do inclusive research because it's quite a new term and it's quite a new approach. A lot of people are still working it out. And that makes it a really interesting and sometimes difficult approach to use. So I'm going to use a bit of my own experience to talk about this. So firstly, the term co-researchers will come up a lot in inclusive research literature and a large part of that is to create an equitable research environment. So when we use the term participants, we're automatically starting to create a hierarchy. We may not be meaning to do that, but there's a chance that if you've been a participant in let's say a medical study and then you come to a social sciences study and you get called a participant, you're going to see these barriers still where you're being worked on or for not with. And the term co-researcher just allows for that equity between each other. In my research study, I called myself the non-disabled researcher and they were all my co-researchers. Now, again, specifically, I did that because when it came to looking at our identities as researchers in the research study, for my co-researchers, their learning disability did not play a part in it whatsoever. So I quite liked the idea of them just being the co-researchers, but for me being defined as something different because they were the ones who came up with all the ideas, it was their study. And I really wanted them to have the ownership and for me to be defined as the different one. I thought that was quite important for me personally, a lot of other people wouldn't do that. But the term co-researcher as well is really important when you're engaging because you're engaging in an inclusive research study, you're engaging with your group from the beginning. They're looking at co-creating in terms of the research topic, how you do your data collection, how you do your data analysis, how you use and create outputs and dissemination. Every part of that is co-created. So it's very important the term co-researchers because it allows for that understanding from the get go that everybody has equity within the research. So the next idea is co-creation in capacity building. So I've just kind of touched on the co-creation side of things. And whilst it's great to come to this and think, okay, so we're all going to have a part to play in all of this research project. To start with, people might not want to. So in terms of my PhD study, my co-researchers got involved in everything from the get go. However, when it came to writing my thesis, they really didn't want to help with that. Shockingly, I don't think I would have either. And they actually quite enjoyed lauding it over me that I had to write this big thesis and they didn't. But that was a really nice thing to see. And it was great for us all to have the ability to speak up and say, no, actually, I don't want to be involved in that. And that's a really great way of looking at it. And the other part that I wanted to talk about with co-creation is that it's very easy to assume that everybody knows how to do research, that everybody understands these terms, but they might not. And specifically, if you're working with marginalised communities, no matter what marginalised community it is, they may not have had any experience of research at any point in their life. So you really need to think about what you're putting in place, what structures you're putting in place, what training you've got, in order to allow for people to build up their own capacities to create that equitable research environment. It's perfectly acceptable to say, oh, I know a lot about data collection. So I'll do a little training session on that. But then to also say, I know nothing about the healthcare sector in terms of when it went being a person with a learning disability. So can you do a training session in that? And that's a really important thing as well is the knowledge exchange is never just one way in an inclusive research study. It is constantly back and forth, which is really great. The final idea I want to talk about is reflexivity. This was a big learning curve for me as a PhD student in that we as researchers are taught to be in control of our research. You've got reports to make, you've got probation to pass, you've got this, you've got that. And the only way that you're going to be able to do that is by being in control. But actually, inclusive research asks you to step back a bit and lose control and allow for other people to change your ideas, adapt them to suit their needs and what they want from that research study. And that can be really quite difficult. And the only way that you can really do that is through reflecting on yourself, on your practice, and most importantly, on your privilege and your power within the group. I remember in my study, I turned up and I was called, you know, a researcher, I was called this, I was called that, Mrs, you know, referred to like a teacher. And it took an awful lot of reflection on my part to think about why did they refer to me in that way? How did I turn up and what did I, how did I present myself that made me seem like I was the most powerful within the group? When in actual fact, that's not what I wanted from my research study at all. So it's an incredibly important thing to have in your toolbox as a researcher. I think not only if you're doing inclusive research, but just any type of research can be a really important thing to have and have that fall back on. So finally, I just want to talk briefly about my experience. So I've kind of talked a lot around it. And but talk about kind of how I went about doing inclusive research. So from the beginning, I involved the stakeholders of charity themselves, my life, my choice in planning. So planning for ethics committee, planning to get my, to get through my initial probationary year as a PhD student so that we could begin our study together. That was a really nice thing to do for me because it allowed people who actually knew the group that I was going to be working with to have control over what we were planning to do. And they were also then able to take it back to the group and discuss it with them when perhaps I might not have been in a position to do that without ethical clearance. What was also really nice, and I think a really important part of my study, and I know in reflection that it's probably not something that most people can do, was I was able to go and visit and meet my co-researchers prior. So I got to spend about three months. So they had a month, it was all done on a monthly meeting, but I got to spend three months, including a Christmas party, getting to know my co-researchers and getting to break down those barriers. And then throughout the process, we just worked with one another. We made friendships. We also made working relationships. And it was really nice. And it created an environment which was wonderful to work in. Similar to working with people who you know in your career, friends or colleagues that you work with, it felt very similar to that, which was really nice. And I'm very aware that I'm running out of time. So I just want to offer the suggestions that my co-researchers said. So at the very end of our study, we got broken up by the joyous COVID pandemic. So we did phone interviews in which we talked about the project and what suggestions we would have to any researchers that wanted to engage with the learning disabled community, also just marginalised communities. And these were the four suggestions they had. So helping people to engage and give it a go. Don't let your fear or your worries stop you. Just go on. Try it out. Support them. So support the communities in their engagement. And talk to people. Start those conversations with whoever you can. And just keep the dialogue going and keep it open. So that was a very swift journey through inclusive research. But I think we have a couple of minutes if anybody has any questions. Thank you so much, Jess. That was very inspirational. I guess you can raise a question in the chat. And yes, we have a few more minutes till we move to the next talk. But I mean, you could use the chat functionality any time if you come up with a question. So as I just moved to the next talk, Jess, I think I need to share my screen now. So the first, the second presentation is about the opening research to non-professionals through an approach to a coindex community led the citizen science. In an effort to put the context and some background literature in the presentation, I went back searching for participants and participatory research. And I came across this quote from the NHS in England saying that they are committed to involving consumers in research, not the subject of research, but the active participants that take part in the different stages of research. This was back in 1998. Since the question raised was whether this was actually something that is actually happening today? And if so, to what extent? And actually, we can see very different ways people can be involved in research. In particular, in the field of citizen science, we have seen different taxonomies or different levels of participation in research. So they talked about level one as the most basic level of participation where volunteers or citizens or participants are used to collect the data for scientists. The level two is a bit more advanced and it's about volunteers analyzing data for scientists. The levels three and four are much more advanced, I would say. Level three is participatory research. So volunteers or participants are defining a problem and they are defining also the process of data collection. The most advanced or is called extreme level of participation is where participants take part in all the stages of research from problem definition to data collection and analysis. And I guess the justice experiences are alive very well with this level four extreme citizen science. At the Opin University where I am at the moment, we coined this instead of extreme citizen science, we called it citizen inquiry. And this is about the active engagement of the public in scientific activities in a way that they are actually defining their own research agenda, their own questions, how they want to collect data, how they want to interpret this data. While we originally talked about the citizen inquiry or citizen science, which is the most relevant concept I would say, now the last year we started talking more about community led inquiry or community citizen science. And there are several reasons behind this terminological change, I would say. The first has to do with the term citizen and the fact that in the US it means specific things. You need to go through a process to become a citizen in the US. Also by using concepts like community, the emphasis goes on collective action in order to change something and bring a solution to your community. So what do we do at the Opin University to achieve this idea or operationalize this idea of community led inquiry? We develop technologies that can help people and communities design their own studies and also processes where they can work closely with scientists to help them design those studies. Overall, whatever we do with relation to participants, we try to do it in a way that can bring learning benefits to those people who are giving their data to researchers. And I will say more about it in the next few slides. So the technology we designed at the Opin University is called Enquirer and it is an online tool or website. It's free to use. You can actually, I'm not sure you can see the URL just a second it was here, but it's enquire.org.uk. It's free to use and you can use it to design a study, pilot a study, use it to collect your data, visualize this data and check your findings either in the form of a PDF or an interim report. And it's free for anyone that wants to design a study or take part in a study. The platform, the website is also hosting several studies that scientists or communities have already set up. Just a second to check the chat in case there is a question. No, it's absolutely fine. So why did we design Enquirer? Well, the motivation was to help people, especially those with no scientific research background to learn how research is done and how knowledge is produced. And do we try to achieve that by giving them an opportunity to take part and learn from studies that are set by others and set up and manage their own personally relevant study. The ultimate, I would say, objective is to help people start thinking more critically and more scientifically. And we think that this is actually a skill much needed in order to understand and assess the information around us, like fake news or any misinformation. So I think we think that thinking critically and scientifically is a skill that should be developed in the general public. At the moment, we work with several universities and non-academic organizations to set up studies on Enquirer. And mostly, we have a long-lasting collaboration with the BBC. Actually, the Open University BBC partnership sponsored the design of the latest version of Enquirer. And they also run several studies on the platform. And here, I'm just briefly, I just briefly want to say that to design your own study. There is an authoring tool, a functionality that takes you step by step in the process of what to do in order to design a study and is giving you also some scaffolding and support as to what is needed in each step. Also here, I'm just emphasizing the piloting functionality. So at any moment, you can get a private URL shared with people and pilot your study questions. What is the outcome now of this process of designing? You can design two different studies or record the mission on Enquirer. Confidential missions or confidential studies are more like the survey-like studies we often see or we come across in social media or elsewhere. Here, we have the example of the garden watch, which was actually in collaboration with BBC Springwatch. And it was one of the most of the biggest studies we had on Enquirer attracting more than 230,000 people that engage in capturing and sharing who is living in their gardens. The other type of studies we have, which I would say is relatively uncommon at the moment, are social missions. In these studies, all the data is open. So the moment you write down your responses, these become public and then anyone can read them, comment on, or like them. Also, in those studies, you can get data visualizations. And I think I have a slide for that. No, I have it after actually. So the data visualizations, they show you graphically in a graphic manner what data looks like the moment to visit the platform. And it's changing dynamically the more people take part in a study. So this is the example of a social mission. We ask people to capture the level of noise in there where they live or work. And you can see here the data pinned on the map and also the noise graphs for each participant. And all this data is open for you to see at any time on the website. And as long as the study is live, it hasn't been ended. And these are the data visualizations I mentioned before, which are dynamic and they are changing the more people are taking part in a study. For us, it's a very important development because in a way, it minimizes the time gap between a person taking part in a study and accessing findings. Normally, this may take months or years for scientists to produce and share a report. With this development here, you can actually get the sense of preliminary findings, finding the moment the study is running. And now I said earlier that we design studies in ways that can support learning for participants. So we're not just getting that the scientists extracting or getting the data of people, we try to give them something back the moment they take part in a study. So our participants can either create their own studies or can take part in a study designed by someone else. What we did was a survey trying to understand whether people are actually learning something from taking part in Enquire studies. We had 150 participants answering the great majority took part in one study on Enquire. So it was a short survey asking them a number of questions and aiming to find the factors that facilitate or inhibit participation in research that is led by scientists and the intention of volunteers and participants to create their own studies. One of the first questions we asked was why did you take part in a study on Enquire? And it seems that the great majority did so in order to contribute to research and science. And also they mentioned other factors like science is important. They had an interest in the topic or I wanted to learn more about a study. Now, this is one of the interesting questions we asked them whether they have actually learned something new after they took part in a study on Enquire. And we found that nearly half of them they said that they know a little bit more about the topic of the mission and they percent a lot more. And the learning was around increased awareness about the topic and you can read the quotes here what exactly they became more aware of. They developed a desire to learn more about the topic. So this person here developed a desire to find out more through books about I think it was birth identification. Some said that there was a change in my everyday habits. I listen more to ambient sounds in nature. And another person said that I'm now attending to take action to support biodiversity. So it seems that they took part in studies that were related to nature and biodiversity and that's why some of these quotes are related to that. Now another great percentage said that no there was no change in our knowledge. And this was possibly an issue of the timing of the survey. We administrated the survey quite late and after a few months they took part in a study on Enquire. Also they said that they had previous knowledge about the topic. So this meant that they didn't really find something new by taking part in the study. And maybe the most interesting finding was this last comment here saying I wasn't informed about the findings of the study. Which in a way points back to us the scientists as to how and when we communicate findings from our research to participants. I'm just checking the chat. No questions. Okay. And my last two slides I guess. The other question we asked whether they would be willing or would consider in the future to design their own study. And here you can see that the great majority more than 70, 80% said no. So they do not really see themselves as researchers or as creating their own studies. And they explained that with a number of factors including a lack of time, lack of knowledge and skills. They need to have some support. They weren't aware actually that this is possible. And perhaps to me the most interesting was that some people said that creating a project or a study is not for them. So it seems that there is a widespread notion that research can only be done by scientists and not by people that do not have the qualifications to do so. So my conclusions or take away messages from this presentation. And I hope I am on time. There are tools free tools like Enquire that can help any individual or community to design their own study. And these tools are coming with support from scientists at least at the Open University where we manage Enquire. There are benefits, learning benefits from taking part in scientific studies especially when these are well designed and they are designed with learning in mind. How can we give something back to our participants? There is a need to make the public more aware of the importance of creating their own studies. And this has to do with local decision making, owning solutions and resulting in more sustainable solutions when these are owned by communities. And also about more active citizenship. And the last point I want to raise is that universities should open their doors to communities in ways that they can facilitate community research. So they should start working more closely with communities in a more equitable manner. And I think this goes back to just as a discussion around the equitable access and participation. This was my last slide. I would like to thank you for listening and I would be happy to answer any questions. So I'm looking at the chat. How do you find people that can be interested in your topic? Well normally we approach community organizations that relate to the people we would like to work with. And normally those organizations have contacts to community members and they become the bridge between the university and the community because they have access to the people or the participants we would like to engage with. So up to now this is at least in my experience is something that worked really well. And thanks Silvia for asking that. So I don't know if there are any other questions but feel free to drop them in the chat. I will move to the next and last talk from Professor Bart Rientes. It's again from the Open University. Bart I'm not sure you can control the screen. I think you cannot. So I would be happy to move the slides for you unless you want to do something different. Yeah it would be great because I just have a couple of slides. So thank you very much for two really interesting presentations which was really exciting and really built on the next narrative. So next slide what we're particularly looking for today but also on the 21st of March is to think about okay how can we make this open science and scholarship work for data intensive subjects like learning analytics. And this is a shameless plug that we're doing a half day workshop really discussing in great depth what the affordances and limitations of open science and scholarship are and how we as as a participant in society have to deal with this. And the next element is actually run by a range of research across the globe. And originally there was a critique done by Christopher Brooks who indicated okay what is so large a society of learning analytics research doing about open science. And as soon as you raise a critique then of course you have to organize a follow-up reaction. So you're more than welcome to join this workshop so we're just going to give you a taste today of what we're going to discuss and we would be keen to have your insights for the next slide please. So the last remaining 10 minutes that would be interesting to think about some of the questions that of course we won't have the answers for today but think about these questions in your mind. So what could or what should solar organizations like Eden do to encourage open science and scholarship? What may prevent research and I think it was really nice from Jess's word in terms of inclusive research what prevents participants in these conferences from contributing to open science. And in particular for solar but perhaps it may also apply for Eden is what kind of methodological approaches could be made more open and why and why not. And last but not least and it's a kind of tragedy of the comments it's often really difficult to encourage participants or research in general to publish in open science or open scholarship ways. How can we make it more attractive and relevant and what we aim to do at the workshop is to write a policy paper that hopefully will drive development forward. So if you're really interested although the workshop is full you're more than welcome to contribute also online. So next slide please. So just a brief few words on what is open science and what is open scholarship. So Feynman Friske basically defined five broad open science schools and of course there are many different ways how you can conceptualize open science. So oftentimes people think in terms of open science is a kind of infrastructure that needs to be in place or a kind of school of thought the kind of public school where everything needs to be publicly available. There are also researchers and policy makers who think well it's actually about being very clear and specific on how you've actually measured what you have done and how it's impacting the wider society. Then there is a fourth school which is basically just mere principle and I think it resonates also with the current crisis is this democratic school of well you just have to make sure that everyone across the planet has access to knowledge and at last but not least there is the kind of pragmatic school that basically assumes and I think for example the COVID vaccine is a fantastic example by working together we as researchers can help each other and can tackle really big complex problems. So basically what Feynman Friske indicated that there is a lot of emphasis on trying to make research artifacts openly available and allow researchers to check and reproduce published findings as well as also to to help people to get a lot of data and to generalizability and cross-validation. Open scholarship is slightly broader than open science and that basically tries to start from the beginning in terms of making sure that whatever you do in terms of your research you make this openly available. So the example that's just presented at the beginning are a really good approach to make sure that from the beginning you start to think about how you can include all participants but also make that available while you're doing the research. Oftentimes we as researchers basically think the research is finished once it's published but there's an increasing focus on making all the steps of our process available. This sounds great but what are the limitations so that's the next slide here. So there are of course some concerns about open science and there and some of these concerns could be legitimate or maybe not relevant for your context but oftentimes there is a debate about the kinds of research that are easy to facilitate in an open science manner. So if you do some really hardcore data mining of objective behaviors of people or animals or molecules it's relatively easy to to share those findings but what if you're doing some really in-depth qualitative work the examples that HS illustrated or some of the work that the S has represented how do you make sure that that is actually available. So what if everyone has to do open science and your particular doctrine doesn't fit with you know doing open science leads also perhaps to some equality and equity issues amongst participants. So for example even if you make all your data available it might be that for some reason there are some unexpected findings that might occur. So for example in a large open access database about all your analyze which gathers lots of data about students and we recently found that where students are located in the region had a fundamental impact on whether or not this whether students were successful but the location that people are based on basically had nothing to do with them as an individual it just illustrated the social and economic context in which they were working. So you might as researchers find some really complex and strong ethical and equality challenges that you may not foresee when you publish your data in an open science manner. Link to the second point is the ethical implications in terms of doing research. It is difficult to make data anonymous but in particular when you're making data available from multiple different sources. So I saw that mentor posted an interesting question about the social networks of people. Obviously it's really difficult once you start to make social networks for example of your students interactions make a publicly available. How do you anonymize that data? How do you bring that together? Then there are intellectual property and legal challenges which we'll talk about in a minute and there might also be reputational jet damages and last but not least it's time consuming. So the next slide basically goes a little bit deeper into them. So it could well be that if you have all this data available in your institution and you find with all this learning analytics approaches that particular students could have been identified at a very early stage of never being able to complete a degree but nonetheless we basically ask them to join a degree and pay for that degree. So what if you make that data available and then a student sues university for not giving him or her appropriate support? In addition the next this element sorry the end it could well be that a lot of really innovation and that is made available. I was listening this morning to the BBC Radio 4 about genes sequencing and one of the biggest advances made in gene sequencing was that it was basically linked with venture capitalism and being able to monetize some of the amazing innovations that were done. So if you make everything open what is the incentive for researchers to share this? Then there could be a reputational damage so if you publish your dataset and you publish a range of articles and then somebody else finds out that you've made an error in your data what if you did this on purpose or what if you made a mistake how can you you know make sure that whatever you publish I mean we're only human and this may actually paralyze research from sharing because you may not want to show that you've made some mistake and finally it can be quite expensive to make data openly accessible so especially if you have complex data from multiple waves or qualitative data you know it's very very expensive and I think what's this Athena posted in the chat reputational damage could also be done by publishing in in predatory journals it could well be that when you publish your open data or your open science work that others might reuse your materials and then publish it in another field we recently had some people copy pasting creative comments articles and rewriting in a different way and basically not attributing to that so it's a difficult issue next slide please here so I guess what what I would like to use the last five minutes for today is to think about well what what ideas do you have in terms of how can we overcome this kind of well the strategy of the commerce is classical collective action problem so how could we make sure that we really encourage people to engage with open science and scholarship and perhaps we could also discuss in this I leave this completely up to you what prevents practitioners and researchers from contributing to open science so if we better understand what prevents researchers perhaps we can also overcome this another thing that we potentially could discuss is some practices might easily adopt these open science principles but others may not so what what could these fields be and last and not least what could we as an organization do to make open science more attractive and also acknowledging that's not all scholarship can be made available openly and maybe we don't want to make everything open so I'm gonna pause here because I don't have the answers but I'm keen to hear what you think in terms of what we could do to raise the flag for open science but also be mindful of the potential negative impacts that might occur over time so yeah I know that you can't speak up so you have to do it in the chat but I'm keen to hear any thoughts thank you so much Bart that was really brilliant and I think a good concluding talk had built on the previous two so yes I guess if you have any questions you should go in in chat because that's the only way you could communicate today please feel free to add any comments or thoughts or questions see in chat well if we have no questions I guess this is the ending of the session and I would encourage you all of you if you come up with any thoughts or questions you could email any of us and we could be more than happy to answer any questions that they may come up or appear later on. I would like to thank you all for joining the session today and especially thanks to Jess and Bart for joining me to this session on research and democratization. I hope you enjoy the rest of the day and we speak soon bye bye