 Thank you, Emma Jean. So welcome to the session. So we're delighted to have Javier, Vio, and Juliana joining us for this session. So they'll be talking about open data as a driver of critical critical data literacy in higher education. So this is a session I think is going to be very useful and hopefully to you all as well. So they're going to be exploring the educational potential of open data. So I'm going to stop sharing slides and hand it over to Javier, Juliana, and Vio. Okay, great Martin. Thank you very much. There Javier and other 65 people. I'm becoming rather shy. And let's start this session before starting. I wish you thank the ALT and the OER20 committee for making this possible in a context of terrible stress and disruption for each of us. And to all the participants here as OER20 community, because being here is believing in this effort we are all doing. And I believe my feeling that there is something profoundly ethical in making things rolling on in times of crisis, and not just confinement. So far in this workshop with Javier and Leo Javeman, we bring a perspective over a topic that has been obsessing us in the last say five years, which is open data as open educational resources. That in the left you'll see the hashtags if you want to comment something enjoying the mainstream and the Twitter. And we believe that this could be a driver of data literacy, but also of empowering and critical data literacies. And we'd like to share this perspective and discuss our ideas with you. Maybe you'll become as obsessive as we are on the topic. So you'll probably be asking yourself a number of questions like which data, which openness, and particularly which caring times of certification where data is seen as a sort of monster. So let's play around these questions. I'm giving the floor to Javier Adenas. She surely will be better equipped than me to start this small rolling. So Javier Flores is yours. Hey, thank you. Thank you, Holly. I'm really glad to see you all here. We are not panicking. It's 72 people in the room. So we're not panicking. What we're trying to do today, and I'm sure it's going to be great fun, is to take you through roughly the representation just to contextualize you all in what we understand of open data, how is it valuable, how it can be used as an open educational resource, but also how we can play a little bit. We can do a little data expedition all together to see how we can start using open data in our teaching. Of course, because I think it's first on teaching and then on research. So when we talk about open data, one of the things we need to consider is that open data provides in a way of another equal opportunities to participate in democratic processes. People have the right to be consulted and participate. So in a way of another for people to access information, apart from documents and files, people need to have the right to access data, but also they need to be trained in how to use the data. So open data is just simply data that it's been opened up and made publicly available by governments, by research institutions, by the civil society, and it's data that can be freely used, reused, and redistributed by anyone without any barrier. So the basic conceptualization and the principles of when data needs to be available for everyone needs to enable universal participation. That means it needs to be accessible and understandable for everyone. That means that it's non-proprietary but also it's non-discriminatory and also that is timely and accessible. And this is very, very important right now. If we see how much data Open Science is using right now to deal with the crisis, with the coronavirus crisis, data needs to be timely because if data is not produced, its status is not made available with an open license. People cannot reuse it, cannot redistribute it, cannot study it without having to go through copyright clearance. So it needs to be license-free and also needs to be machine-processable. So that's kind of the key elements of open data. If we can move forward, but also there is a value, a social value of open data. It's not just a technical value. It's how people can really use data for their daily lives. So one of the bits is social civic monitoring. The people that really, really rock on civic monitoring is Sicily, monitor Italy and other organizations. Work with citizens, for example, to control and review how much money the government is spending in different things. So it's kind of, in a bit of participatory budgets, but also understanding how the money is spent. And this is something that we should all be doing right now. So where the money is going. Also, it's a great resource for scientific communities. We've seen the value of open data these days, where it is from China, from all over the world, sharing the data and trying to find a cure for what's going on. Also foster transparent research practices. And when we say, okay, how come that can foster transparent research practices? So when you share your data alongside with the papers or with your reports, it means that people can assess if your methodology is correct or do new findings. And that prevents, but science helps to develop scientific skills. So how people learn to do research. How do you learn to do research? If you have access to open data, you can replicate the models that all I've been using, so you can gather and gain skills and break the silos between teaching and research. And this is something that's really close to my heart. When you bring data to the classroom, the same data that you may use as a researcher, because we'll do research here. So if you bring the data they're using as a researcher and involve your students in the methods, in the way that you do the teaching with, or you do, sorry, you do your research with your data. And to help you to find things, you participate with them in doing research. You help them to develop research, research skills, but also you can bring real social life problems to the, to the classroom. One of the things, for example, if you transfer data or international data, you can bring a problem, a specific problem, and show it to students so they can try to solve issues or solve problems through the data. And who produces the data? The World Bank, the United Nations. So if you want to see education data, you just go to the UNESCO Institute of Statistics and you will gather data from there. You have the national government, so yes, you can gather data from the Scottish government. There should be a Scottish data portal. There you have the UK Open Data Portal. You have a little local government, so London's data, Barcelona's data, which is actually really good. Then you have non-governmental institutions. So for example, in Italy, that's a great, great release of data. Then you have, for example, the NASA or the European Space Agency that are putting the data up so people can just go and use the data just to deal with astronomy issues. And then you have research data platforms. So for example, Senado and Big Share, where people can just, or deposit their data or just go and retrieve their data. So yeah, this is where you can get data from. And that's from me so far. I'm going to pass back to my colleagues. Hi, everyone. And so at this point, I thought it was useful. Well, we really wanted to relate the theme of this workshop to the theme of the conference, the caring openness, and to think about a couple of different senses in which care relates to data. So of course, we tend to think of openness and caring as kind of natural friends in the sense of caring for each other, in the sense of that through providing open data and transparency the opportunity to learn from each other, to become better informed, to have the opportunity to critique the activities of governments or research resources or society organizations, to be informed about what they're doing and be able to form our own views about these things. But of course, in the data context, just as in the open education context, critical approaches have asked us to think carefully about whether being open automatically makes something good or automatically makes openness is automatically caring. We also need to think in the sense of careful, why we need to be careful. And I think in this context of datification that we live in, we need to think really carefully about the relationship between the sort of realities in the world and how they get represented as data and understanding that there is not necessarily a natural transparent link between data sets and realities, live realities of people in the world. And so that's an area in which we need to be quite careful as well as in the wider sense of notification of where we have corporations spying on us as their business model. So that brings us into this question of how can we use open data in higher education. So first of all, one of the great things that students can be doing is collaborating with researchers in real research projects, rather than merely studying the research that's already been done. It can actually work with the real data that's being collected and sort of studying that as it comes in and being part of those kind of new analyses and new discoveries. It's a great opportunity for interdisciplinarities of students working across problems and issues that are complex and cannot always be solved merely with the tools of one discipline. So working with other students that kind of bring different knowledge to the table. It's great for scenario based learning activities. It's great for collaborating with local communities working on real problems. So this is, there's a lot of work now starting. It's really prominent in Canada, I've noticed. And it's also there's a bit of this happening now at my institution where I work at UCL on community-engaged learning. And that's really where students are looking at issues that are faced by organizations or communities out there and how a piece of research that they do might be able to contribute to informing them and supporting them with their issues. And so for civic engagement, obviously there are going to be different degrees of control over data and data set concepts, depending on how much training experience you have with them. And so we really propose though that anyone at any level can start working with data. They have to design the task to be appropriate to the level at which they can engage. So we think that there are initial tasks that are suitable for all levels. There are some time to be more appropriate for undergraduates. Some where post graduates are going to be able to really use their much more advanced skills to do something that looks a lot more like academic research projects. We'll share this slide so that you don't need to try and read all of this now. And another really interesting way of working with open data is in the context of data journalism, where you're both working with all the relevant kind of data literacies and skills, but also transforming that knowledge and communicating it. So you're working on kind of turning that into narratives, into explanations, translating it for people who don't know the data to understand what the data is saying. And so that's a really exciting area. And so in terms of embedding open data in teaching and activities, we think it's really important to identify and describe learning outcomes for the activities, identify portals for source in the data, clearly identify and describe challenges students might face. So try and anticipate some of the issues that will be challenging for them. I think getting them working in teams is really important there so that they can support each other and not be not be just kind of alone in it. Provide training materials for the questions we'll need to use and also supporting students to communicate their findings. So in other words, doing assignments that are available openly, other than the dreaded disposable assignments that we talk about often in open education. And then I think I'm handing over to Huli. So my turn to keep digging on the issue of care with the question you see. Let's use the images to the bad luck that can be only opened with the right key. But there are many kids, which is the right one. And this has to do with what Leo was saying before that we need data literacy that we have been mapping the approaches to conceiving data literacy. And we found out that there is information literacy and numeracy, statistical literacy. And recently we have frameworks trying to characterize the skills needed to handle data and to reproduce, visualize, present, embed data into narratives. And one thing we came across in all these models is that most of them are focused on technical skills, even in the case of visualization as part of, for example, data storytelling. But what about a critical perspective? A critical perspective in the sense that we need to be able to see the crisis, the biases, the pitfalls in data abundance as Leo was saying before. So let's move quickly to something where we are all going to see what we think about this problem as educators. In our role as educators being educational technologists or adult educators or teachers, people working at several levels in education. So we are going to use a mentor. Some of you probably know these interaction tools. So for those working from a PC station, you can open a new tab and go to menti.com and then type the number below 316986. Those that have a mobile can scan the QR code or you can also just type menti and also introduce the sequence, the number sequence. So I'm going to move to Mentimeter. So you can see, you'll see from here from, okay, so let's do this tool screen. So you'll be able to see this on the blackboard chat space and you'll also see this from your top on the Mentimeter interactions. We are going to comment on the interaction. Just for information consent, those not willing to share the information that we are going to open and share with all of you, can just follow this interaction precision and not send replies because we cannot identify you and we cannot remove the data, but we are going to take care about anonymization. So the first question is, do you use data in your teaching activities in the way Javier and Leo were describing? Which type of data, even if it's data taken from reports, written reports and digital role data or data coming from, okay, great. So we are having the first interactions. Great. Thank you Mark for sharing the link and the number. So I encourage people to use open data. So we have people here that is already experienced in this approach, not a teacher, but if you are an instructional designer, you could support or a librarian, you could support others to use open data. For example, research open data is a treasure within your university. So let's see, let's move. We have 17-18 interactions, very interesting. A range of data use images, texts and numeric data as examples. I use result data and collect such as social network analysis, okay, great, qualitative image and all the open data from repositories. These are very interesting experiences. So I see in part you are already experienced, but it would be interesting to follow these stories about open data connecting to the problems and the issues we find. So I'm going to move to the next interaction and I'm going to ask you how comfortable do you feel with the word data in terms of data and teaching and learning and data in the society. You are already engaging with data as we see in the other slide. Oh, great. So we have people that is feeling cool, really cool. Let's see how this picture changes with responses five. We are at five responses, six, seven, nine, 10, 12. We, and this session, we are 71 people. So the 19 responses are kind of representative. Would you say so? We see and now we are moving to a third of the participants and we see that you are comfortable with data and teaching and learning, but not that much with data in the society. We are going to handle these results and to discuss about these results. So okay, I'm going to move to the next slide. Sorry, we have time constraints. So it's really interesting to see. We are going to share the results. No worries if you want to keep answering these interactions. And the next question, could you share why do you feel fatal or cool about data? And mostly the replies were about data in the society, thinking about that. I know it's a difficult question. Okay, great. We have also interactions here at the chat. Okay, those not willing to participate in the mentor can leave their impressions at the Blackboard. Great. Okay, data sounds very scientific, positivistic, but isn't necessary, it leads to understanding, but we need to be careful. That's not really properly described what is in one word, possible biases, who produce data and which is connected with biases and with the claim for carefully, being carefully. Data can be used to give us information about the society. You don't need to be a data scientist to make use of it or understanding potential. It can be a key, but are we ready to use this key? Maybe some of your impressions come from this feeling, good feeling that there is danger, there is potential, but there is also fear. Let's see, I work with repositories and have been a teacher of open access and influence. So this is a very expert impression. Fatal, there are too many criticalities and cannot see enough of them cool. I am doing quite a lot criticizing what is data in the staff I am teaching. So it's really, really great to have this, there's several impressions because when wrapping up, we'll see that these are epistemologies and positionings relating an object. Many, many more impressions. I love them, teaching and learning as it is my train, but in society, not feel those who are propagating in social media have the ethical analysis training. Great, need to question assumptions. I'm happy to use data in teaching, but in real life, people have poor grasp of the stats and visualization. So it looks there are two trends in this, and it's the concerns and the ability of people in the real world beyond the protected area of your classroom to handle with data and data. Indeed, as teachers, you are supporting your students to engage with data, 26 interactions. Now, let's move to an open data explanation. We'll be very short also because what I see is that most of you are already experiencing it on portals of open data or digital data libraries or other sort of repositories. But what I would suggest to you, I think we have at least finished to navigate a little bit one of these three portals or repository and take a look. Take a look and see what is in there for you, if there is something for you in there. Those that are not really experienced go to the European data portal or the UNESCO portal. Those that are librarians probably know Zenodo or people already engaged with research data can go to Zenodo and take a look. You can copy this link. I don't know whether... Okay, I'm going to copy and paste the links to the Blackboard chat. So, okay, great. Martin did it for me. Oh, that's marvelous, Martin. Thank you. So, take a look and take a look. When you are ready, just say something at the chat. Ready, ready, did it and tell us which data portal UNESCO, European data portal or Zenodo you visit and we are going to move to the next interaction then. Great, Jade, UNESCO, okay. Zenodo, Sarah probably knows, Dan, UNESCO. Javier, do you want to reply to Gaby, UNESCO? UNESCO, okay. You're taking a look. Great, European data. Yes, yes, it's not possible to put and Andy, it's not possible to put the links in the in the Mentimeter. This is something really annoying but not possible. This is because we are sharing that way and thank you for sharing. Just go and take a look, experience it. For those, particularly those not having any experience, take a look at the portals and because these portals are being announced as a resource, as vocational, mostly as open educational resources and I'm doing research about open research data and what we are seeing is I'm not going to spoil this this this movie. I'm going to tell you what we are seeing that take a look as educators. Okay, okay, yes, yes, Sarah, yes, absolutely. Sarah is saying that, okay, probably you are all seeing that that connecting the dots of data in medical research is incredibly important and particularly now in with the coronavirus virus crisis because the researchers could be just connecting discoveries on one side to make the next move the next step. Okay, great, Javi, thank you for replying to Javi. So, most of these as Zenodo is created is like self-curated by the researchers themselves. So, the researchers create the communities and upload their resources as an open platform and it's embedded into the open area platform. So, it's mostly used by European researchers. There are also other type of platforms similar to Zenodo for the researchers. It's self-curated. But in the case of the European data portal and the ISCO, there are more curation, top-down curation from this government or these international bodies on the type of data and how the data has to be aggregated. Okay, so, due to the fact that we have to move to the next interaction, we are going to stop this user experience. I hope you enjoy it and you'll be curious. And we are going to move to the next and last interaction. Tell us about your experience as an educator. So, you can say whether the all the collections that you saw in those three portals gave you some ideas for teaching that you felt are interesting but you don't see anything for your practice that these are relevant data like for example medical data but there is nothing for me in there and that was an nightmare. And my experience was an nightmare. We should stop all of this. So, feel free and go. Great. That's very interesting. You felt inspired mostly. Now, you should tell in the chat that you felt inspired because it was an initial experience or you were ready, as I suspect, are engaged with these platforms using them and got inspiration from your own approach to these. So, nothing new. So, under the sun. But if you are new, yes, yes, have you ever been sharing the community, the COVID community? So, if you want to work with students or about data literacies to read COVID data or the way research proceeds and so on, here is a real authentic material. So, let's see. We have 19 interactions out of 76 people engaged in this workshop. Anyone else willing to say something? Yeah, Leo. That would be great. But actually, yes, the nodo is open. So, we could create because there is crowd science and response under the context of responsible research and innovation. We could create students' research communities, yes, publicating, for example, the results of their authentic results of their learning under your guidance as teachers. So, okay, let's wrap up. So, the experience was mostly inspiring in the sense that triggered some ideas, some inspirations. Interesting, but you don't see a little bit less. Also, the fact that this might have to do with the fact that the relevant data is not there or maybe it's put there in a way that we cannot interact and nobody had a terrible experience. This is good. So, I'm going to move now to wrapping up this session. So, thank you very much for your participation because, sorry, okay, let's move to the next. And now, let's reflect a little bit using a sort of frame to think about our experience and our positionings in the sense that, as you can see, there is a third thing has to do with how do we position in relation with data. In a continuum, this is a continuum I conceptually draw on the basis of the alternative epistemologies of data activists by Stefania Milam from Communication Sciences. And in this continuum, some of us are in the reactive side. Those that are really like being concerned about the ethical problems cannot be overcome. The abilities are really low. So, we need to prevent people or at least be really careful and play with that only in very under very controlled context. There are other of us with experiences or not with enthusiasts that see in this a lot of potential. And this is a more proactive positioning and epistemology of data is not better or worse than the other. It's just a human positioning about a socio-technical problem, an issue, an emerging problem we are living by. But the worst thing I didn't see happily in this workshop is a naive approach and it's that approach where we feel that big data will change the world and all the tones that we could now make sense of to understand learning processes will change our life. So, this is a very good thing. So, you can comment and say about your personal positionings being more reactive or naive or in which part of this continuum do you feel can keep counting. And in this quadrant graph we are seeing now, we further represented distinctions. Open data can be in the right side, upper side for public good, but which data collected how? Is that a luxury for example students generated data? Would be very good, but is that a luxury in times where we are in a hurry for the basic contents? If we come to all the data that is collected from private companies in the left side of this quadrant, that could become easily a commodity. To which extent should we be active or start engaging in activist activism against this data's commodity and the opening of some of the data properly anonymized to release this data also as public good. So, these tensions are not solved, my friends. We are not bringing here answers. We are in the middle of these tensions and you will probably be, I suspect most of you are in the right side of the quadrant, but probably you are also thinking for example those working in learning analytics the importance of making sense of that data with for example participatory designs engaging students and the community in using this data. So, we here propose some ideas to keep on working as educators where that's a firewall on data literacy, engaging with open data and working not only the technical skills about data, but also ethics and politics of data. The techniques are necessary to engage with data and then understand what is in for example developing an algorithm and the bias that is in it and promoting the aesthetics and the narrative of data and considering data as an object that can be in the middle of meaning making and there is semiotics in presenting data beautifully. So, I'm going to leave the word to Javier or her to close this session. Javier. Sorry, something. Can you hear us now? It's, it's it. Yeah, okay, I'm back. I don't know what happened. Bit of shredding gear moment. So, a few things to close down. Thank you all for what you're doing. Please make sure then you start thinking on how could you use data because governments are spending lots of money in opening up data through the transparency laws. They are forced to open up data. So, your governments are literally paying millions to get data available for you. So, take advantage of it. That's something that we gain learning from understanding how our national data portals work. Also, think about very carefully if you're using data related to populations about how will you care for the people that you're studying. This is this is one the map that I'm showing you. It's one of the classical maps on how to show poverty. But one of the things that when we start working with humans, we need to have very close at our heart not to stigmatize a population. So, have a look, look on the data ethics frameworks. There's also I showed you the data ethics canvas. Because when you work with data, you're also working with people information and we need to be very, very, very careful with the communities on how not to harm them and how not to expose them. Also, this is a bit of self promotion with Leo. There is this book online. It's called the state of open data. So, you can find data about agriculture, law, education, infrastructure, government, and it's open. It's fully online. Maybe Leo can share the link to the book. Of course, it's open access. So, it's very little chapters on what's going on with open data in different levels around the world. So, have a look at it. I'm sure that you will enjoy it. It's a good very good reading for confinement. So, yeah, if you want to learn more or if you could like to have a workshop with us, just let us know. When we're allowed to travel again or we can do it online, of course, we'll be happy to meet with you and your colleagues and take you through this data journey. So, yeah, that's all from us. I think Leo and Juli, we can we just leave you to think about it and you know where to find us. Of course, we're going to keep the chat open so we can start answering questions. So, maybe if we put the audio in, we can start answering your questions. If anyone wants to grab the microphone, if you just raise your hands, we can give you a present of rights and you can use your microphone or we can pick up questions from the chat as well. I see, yeah, Cruz has put her hand up. So, I'm just going to make your partner, Maria Cruz, and then you should be able to use your microphone then. Thank you. Leo, Javier and Juli for this presentation and to Martina. You cannot hear me. You cannot hear me, I guess. Okay. We can hear you. Okay, no, I thought someone was trying to tell me something. No, as I say, thanks to Art for hosting this event. As I point it out in the chat, just so everybody can be aware of that, I think the key point is the statistical knowledge and training of those who are going to be working interpreting and analyzing the open data because if they lack of the basic knowledge and they don't understand how to interpret data, we can go to situations like what happened now with the media in the UK that statistics are actually misinterpreted or changed or not changed, sorry, or you know you can extract the information that you want from the statistics. That's what I thought that is important also to invest in the statistical training or you call it data literacy and just to understand how to handle the statistics. Thanks. Sorry, I think I can just answer very quickly here. Luckily for all of us, and thanks Maria Cruz, luckily for all of us, there is something called the School of Data. That's an open knowledge foundation funded project. The beauty of School of Data that it's not just tutorials in English. There is a School of Data for Spanish. There is a Portuguese version. There is a German version. So regardless where you are, you just look for your local School of Data that's online. So I just posted the link for the English version, but there is one in Spanish that is based in Mexico. The Portuguese one is based in Brazil, but all the tutorials are translated and placed online. So you can start from the very basics of statistics. 200% recommended, they are great. They are fantastic people. Hi. Martin, do you manage for the people or you? Sorry, what are we saying, Eliana? Yes, Sarah, once you ask a question. Yes, hello. Good morning or afternoon rather. Thank you for sharing School of Data that's really helpful. I was curious about one of the things when I think about the critical appraisal of different, whether it's data sets or randomized trials or even systematic reviews, I'm always on the lookout for updated checklists for critical appraisal. I haven't really seen many that are much more up to date than those that were released by the University of Glasgow years and years ago. I was wondering if anybody else had updated tools that they'd like to use, whether they're teaching in an evidence-based medical practice or just for data literacy? Sarah, there is a lot of research about data literacy and I did a systematic review of the literature recently on data literacy frameworks. These frameworks are mostly focused on the development of technical skills. As for example, critical data literacy is being dealt with from the lens of media education. Connected to personal data literacy and there is some debate, for example, led by Zawin on Lucie Pangradio, but no framework yet to analyze the development of critical data literacies in the sense of not only, for example, data wrangling, analyzing, collaborating, presenting data visualization and data storytelling, but also considering the collectives behind data representations, how the data was collected, methodological issues. These are less frequent concerns and I'm engaged in working on a framework for critical literacy and critical data literacy from the EU project and there are also other people like Lucie Pangradio, so we're working on that and I know Bonnie Seward is also having a focus on that this afternoon. Does that answer your question, Sarah, or do you want to follow up? I'm curious about Caroline, she's saying, but I think it is not so relevant now. Yeah, I think Caroline could have her hand up, but if she wants to ask, if she just puts her hand up, we can take a look at how could you present her, right? Yeah, thank you very much. Just to say one thing before I ask my question, I can't tell you how amazing the presentation was. Thank you so, so much. I was just thinking, you see, that all of the things, I'm just wondering why this being so incredibly useful and incredibly needed, I think, nowadays, what is there that is hindering the doing of this in our classrooms? And I think I do agree with Marie Kruse, when she says about the statistics that is involved in working with data sets. And I agree again with Juliana when she says about the humanities, so I do think that that is a tricky combination. And we always have to think that when we teach this, we are in, well, I teach face to face, not now, because we're online, but I use to teach face to face. And I think to do this properly, it really devotes lots of preparation and some kind of feeling that you master the statistics behind whatever you're going to do. So I just think that all of this is just, you know, it just takes a lot of time and it's just my worries. That's what I was saying, it's not relevant, because I'm not sure it's just, but it's a worry I have. And yeah, I think it's very relevant, but I'm kind of wondering how can we promote the statistics, because it's also needed with the humanities, so that we really do a good work when we do this with our students. Thank you. And thank you again, the best workshop ever. I have, not workshop, but the session I have ever been. Such a great session. Thanks very much. Well, Caroline, this is too much. We are just, I mean, sharing concerns. And this is kind of a conversation, familiar conversation with Javier Anleo that we open to all of you. And I perfectly agree with your perspective in the sense that we need to rethink the curriculum of teachers' education and educators' preparation. And probably we are not prepared, and we need also to rethink ethical concern in engineering, in engineering, in the training of engineers, in people in natural sciences, because the crossovers between disciplines are more and more frequent. So I am really engaged in several projects. One project about learning analytics, working with the team of engineers, another project connected to teachers' training, to use data generated by platforms at school. So from these experiences, my concern is that we are starting to need to reframe educators' training to deal with this conflict. Because in education, you are going to see all the disciplines to be engaged in one setting, like, for example, in school teaching or college. In higher education, it could be a little bit different in the sense that we could be teaching a very specific subject or domain. And the data that we handle is data, as Javier said, data that comes out from our own research. So we are training the students to handle data that we perfectly know. But in any case, in higher education, there is a still need, it's very little spread, the idea of using our research data as open educational resources. This is because we are insisting. Okay, thank you. Also, Mike is saying, as a historian, we need both humanities, framing data narratives, and the fact that data is social construction. And we need, from the other side, training, a little bit more training in what statistics and developing algorithms and using some very frequent softwares in data science mean, like, the most frequent practices in data science that are not so difficult, are just, we are not engaged in them. This is the fact. Javier? Yeah, I understand the fear. Also, we're not from, we're not data scientists. So we don't do hardcore big data analysis. One of the techniques that I recommend for people to start engaging, mostly when you come from the social sciences, or from the humanities is, well, to approaches, one is to start with students doing very kind of research led activities using data journalism techniques, because as we understand most of the journalists are not trained in statistics or statistic analysis as a big part of their careers. So have a look into how to use data journalism techniques, and also have a look on how to deal with digital humanities techniques for dealing with data. It's a way that people like us from the social sciences might feel much more connected. So don't try to go hard into data science, try to start little by little with data journalism and digital humanities. In any case, in this session, we couldn't, of course, introduce all the techniques and this requires more engagement. And for sure, there are many templates, tutorials, and we should, I mean, do an effort to keep on working on this to build a more structured field of research and practice, considering open data and data as collected in research as an open educational resource. So we are going to close the session because we are in time. And thank you very much to all of the participants for all the insights you brought to these workshops and all the ideas that we shared here. Thank you very much. Well, I think a wonderful session and if we could show our appreciation to our presenters. You can use that from the chat. You can show some applause and I can see that coming in. So I know Leo as well has posted a link to the session page where additional content has been posted as well. So Javier and Leo, thank you very much for this workshop and thank you for under such unprecedented times. I think you've done a wonderful job in making it a really rich online experience. So thank you all for participating in this event.