First of all, my apologies for the delay, but I'm very happy to have Venkat with us from the Digital Creation Center who's going to give a presentation about research data management. I hope we'll be able to follow this with a presentation by Thomas Margoni from Create, the Copyright Center at the University of Glasgow. But he had some troubles with his sound, so we're hoping to try to get this fixed while we're having this first webinar. If you have any questions for Venkat, you can just, there is a Q&A button button at the bottom of the screen and you can just enter your question there and at the end of the presentation, we will go through each of the question and try to see if we can find an answer. So if that's okay for all of you, then Venkat, the floor is yours. Okay, yeah, hi everyone. So my name is Venkat, so I work for the DCC, the Digital Curation Center, which is based mainly in Edinburgh and Scotland. Just a quick background about us. We were established around 2004 and we work at a national and international level. We deal with consultancy and training and policymaking and advocacy and digital data management. So the best practices and also on the flip side, as well as the researchers, we deal with the people that actually provide these services and how to build those services or improve them. We also are involved in international consortia, schools and projects, lots of EU funded projects, including open air. And one key thing to remember is that we do all of what I just said, but we're not actually actively doing any data curation, although the name Digital Curation Center might give a different impression. Okay, so I'm gonna take you through some basic tools on how to better manage your data. And to start off, I'm just showing you this slide from a nature paper, which was only from 2016. And in this, they did a survey of 1,500 or so researchers to find out if they thought that there was a reproducibility crisis. And interestingly, more than half of them at respondents actually said that, yes, there is a significant crisis in reproducibility of published research. So this immediately raises a problem and this is what we're trying to address here. The other question that you may see is why make data available? And I think this slide, I think sums up quite nicely for me that it was never acceptable to publish papers without making data available. And so we are talking about publishing data and not actually peer reviewed papers here. So when we talk about data publication, we're talking about in repositories and other means. Something that you'll probably encounter a lot and maybe you already have been taught about is the Fair Principles. This is quite a recent thing, only in the last few years. And it's something that is trying to be pushed quite a lot in EU funded projects as well as globally. And in case you don't know, Fair stands for findable, accessible, interoperable, and reusable. And what I'm going to show you in the subsequent slides will hopefully address many of these points. If you don't understand what all these words actually mean, if English isn't your first language, then hopefully we'll be able to clarify later in the Q&A. And also you can find out more about these Fair Principles from this publication that was just from last year. I should find out that these slides will be made available to you and the links that are shown in these slides will be, you'll be able to find these documents. And a final breakdown of what those, each of those findable, accessible, interoperable, and reusable things mean. Some common misconceptions about the Fair Principles is that data needs to be made open. Now, although I said in just one of the earlier slides there that data should be made open, this is only when you're actually able to, not all data can be. And the Fair Principles do acknowledge that. Maybe there's data of a sense of nature. And of course, in those cases, it might not be possible to actually make it open. They don't actually particularly specify any technologies or implementations. So it is more of a wide scope, wide ranging scope of principles. Fair is not a standard to be followed or a strict criteria. It is a spectrum so that, yes, we're trying to be accommodating as possible. And it doesn't only apply to the life sciences. And I say that because it originated from the life sciences, but now it encompasses the full gamut of research. So just to tie that all together, what we're seeing here is if you have this overall large circle which shows managed data, fair data is essentially a subset of that. And meanwhile you have open data that might not be managed in any particular way, but which can also overlap with fair and managed data. And what we're trying to do here is to increase this overlap between fair data and open data. And you will hear this saying many times in your RDM travels as open as possible and as closed as necessary. So the rest of the talk will actually now focus on the actual nuts and bolts, the actual how you go about practicing good research data management. That's RDM. So when we talk about data here, we typically talk about a life cycle. So from the point that a piece of data is created all the way through to its preservation. Now, this life cycle that I'm showing here is our version of it, but you might actually encounter other versions too that have different terminologies. But we typically break it down into six nodes here. Creation, documentation, usage, storage, sharing of the data and preservation. What I'm going to do now is go through each of these steps and show you some practical things that you need to do. So for creation, data creation tips, we encourage people to use a consent forms and licenses and agreements to restrict the opportunities to share that data, choosing appropriate file formats, adoption of a file naming convention and creation of metadata and documentation as you go. So just going through those consent forms. Now, this seems like something that you may not think is valuable, but it is, especially obviously if you're talking about sensitive data. But even when you're talking about non-sensitive data that doesn't require any permission from anywhere, you should do this. A consent form is required by many repositories. So when you're thinking about the long-term and when you're going to actually eventually deposit your data into a repository, most repositories will actually make sure that you have consent to actually use and deposit that data. So from the outset of your research project, make sure you use a consent form. Choosing appropriate file formats. Now, this again seems like a very trivial thing, but let's actually take it step by step here. When we're talking about file formats, we typically are talking about open file formats for good research data management. Now, there's a difference between open file formats and what we call proprietary file formats. Proprietary file formats are such things as you might find as Word files, Excel sheets, and so forth. These are specialized file formats for specialized, usually commercial software packages. But say in a hundred years time or so, you wanted to make sure that that data that you deposited is still accessible and readable in some way. It might be that the file format that you're using, if it were in a Word format, might not be readable anymore because that software package doesn't exist anymore. Now, of course, in this case of Microsoft products, it's highly unlikely that's the case. But if there are other file formats that might not survive the test of time, then you need to be careful. So for text documents, we typically recommend that people use .txt files, for instance. The other thing to remember about open and proprietary file formats is the use of lossless file formats as well. So lossless, meaning uncompressed, basically. So when you, for instance, are talking about images, if you're looking at JPEG images, which are very widely used, these files are actually compressed. Now, if you're doing some research and doing analysis on some images, for instance, you would want to actually do the analysis on the uncompressed or lossless file format. That's because that contains all the data that was captured. And, of course, when you're doing analysis, then you want to make sure that you have the full set of data. Breaking this down, this table here has been adapted from the UK Data Service, and you'll be able to access this slide when I share it. But you will be able to find many tables like this by doing simple Google searches. This table shows you the different types of data that you might encounter, and the preferred recommended file formats and the acceptable file formats. So, again, using image data, for instance, you can see that the recommended format is TIFF file. So this is an uncompressed file format and open. Whereas acceptable, but ones that you should avoid if you can, are JPEGs and other file formats that are like payments. And so for different file types, whether they're text or audio or video, you have these recommended and acceptable file formats. Organizing your data, again, seems like a very trivial thing, but you do need to keep aware, be aware of how you are doing your file naming conventions and your directory structures. Especially if you're using command line or units operating systems, this can be a very powerful way of allowing better analyses and faster analyses. If you have a standardized way of naming your files and folders, then you can do command line operations, for instance, which allow automation. So, again, just following these simple rules is very key. I'm going to move on to the next two here, documentation and usage. So documentation, again, seems very trivial, but think about what is needed in order to evaluate, understand and reuse the data. Why was it created? How have you documented and what did you do and how? Apologies. Did you develop code to run the analysis? And if so, this should be kept in share too. Important to provide a wider context for trust. And this goes back to this idea of reproducibility and trying to stem any kind of research fraud. So what is metadata? Now, this is a word that you will frequently encounter in research data management, and it is essentially a subset of documentation. It is something that is machine and human readable and is standardized and structured. It helps to cite and disambiguate data, meaning to allow knowing what any particular data or data set is from another one. So some metadata standards that you might encounter are ones that are more generalized in scope, such as Dublin Core, and other ones that are more discipline specific. And these are a few examples right here. But if you are starting a project, one of the first things that we recommend that you should do is try and find out if there is a metadata standard that fits your particular field of research. And these two links here actually take you to some catalogs of known metadata standards that already exist, and which you should try and find some metadata standards that you might be able to adopt. Moving on from that, another thing that we encourage people to do is use controlled vocabularies. Now to try and describe this, using this example here, where this is an actual use case where a group of researchers were asked to describe the subject of an actual experiment, subject meaning who was being experimented on or analyzed. They were asked this to just describe it using pretext, meaning they could just write or type in the answer. And this was the list of answers that were given. So looking through this list, I hope people can see that actually what all the respondents said were actually the same answer. They were talking about humans, except they've actually written it in different ways. H-sapien, Homo sapien, et cetera. Now for us as humans ourselves, we can read that and we can actually see that they are all the same answer. But for a computer, that isn't the case. It cannot actually figure out that they're actually what are the same thing. So by using controlled vocabularies, we can actually get rid of this problem. Meaning, for instance, when you are filling out a web form, for instance, you might get some dropdown menus which have some pre-written options. Those are controlled vocabularies. One step beyond controlled vocabularies are ontologies. So these are controlled vocabularies, but now they are structured. Meaning that you usually will find parent-child relationships. So for instance, in this slide, I'm showing two different trees. Now just looking at one of these trees, you can see that, for instance, term B1 is a child of term A3. So these are different controlled vocabulary components, but now structured into a tree. The double-headed arrow shows that what you can do here, in a computational sense, is that if you have two different trees of ontologies, one for one organism and another for another organism, if you were to try and do some kind of searching between the two trees, these trees could conceivably have thousands upon thousands of different components. Now, if we were trying to do that searching between the two trees manually, that could be very difficult because you are trying to do, you have so many different terms. However, for a computer, this could be done in literally seconds because it can actually process this information so much quicker. That is the power of using ontologies. Now, we again recommend that you look for a ontology, if possible, for your field of research. And on the slide, we have this link to a catalogue that you can hopefully find a suitable ontology. It may be the case that you will not find one, but we recommend that you at least try to look. Going on to storage. Where will you store the data? Again, it seems very trivial, but make sure that you store your active data, meaning when you're doing your analyses in an area where there is backup of that information. Don't only keep your data in laptops or flash drives, et cetera, because if you lose those or they break down, then you will not have any way to recover that data. We recommend using departmental or university servers. Many universities have provision for this. If they do, then please do use those. The other option is to use cloud storage, which is becoming more and more prevalent. There are third-party tools that you can use for collaboration, of course. These are very widely used like Dropbox, and there's another option called OwnCloud. And here at the University of Edinburgh, where the DCC is based, we have our own systems that have been built on OwnCloud, for instance. But using these commercial services like Dropbox or Google Drive, do be careful. These are commercial options. Again, if we're talking about sensitive data or any kind of data breaches, then you need to make sure that ownership of your data is actually addressed. Preservation and backup, not the same thing. And this is something that some people do get confused about. Backups are regularly done and maintained, whereas archiving of data and preservation is usually what you do at the end of the lifecycle, and it's for long-term preservation. Sharing of the data, this generally means licenses. So in the fair principles, the R stands for reusability, and this is typically where we are talking about licenses. So one of the most widely used licenses that you will encounter are called Creative Commons. And when we say most common, I'm talking for research data. So this table here shows what the different Creative Commons licenses are and what level of openness that they provide. So typically when we are talking about open data, then we are asking people to make it public domain or use this CCBY denotion here, which the BY means to attribute. The more closed licenses are the ones that you see further down here, CCBY and CND. I'm not going to go into each of these step by step, but please do go and look up the CC Creative Commons license which you can easily find online. And when you are wanting to apply a license, there are ways to do it if you don't know the most appropriate license that you want to use. And this tool here, for instance, can help you out, which will take you through a wizard and it will ask you questions on your data and it will suggest a suitable license for you to use. Finally, in the preservation stage, what we are talking here about is typically repositories. Now repositories can be both institutional or discipline specific. Now, a place that you can find suitable repositories are in this catalog called re3data.org. Again, you'll be able to access this when you get the slides. So we recommend in many cases that when you are starting off your project, that you actually look for a suitable repository first. This is actually a useful way of doing things because if you can figure out if there's a suitable repository for your data, then that actually addresses many of the questions that we were discussing just previously, including metadata ontologies and licenses. So using this catalog, you can find a repository hopefully. We recommend that you try to find something that is domain or discipline specific because this actually adds value to your data. The more that you keep your data with other data of a similar nature, it increases the usefulness because any kind of cross comparisons between the data make it more valuable in that way. If you are only able to deposit into a more generalized data because maybe your research is so different to anything else out there and there is no discipline specific repository, then your data does become somewhat diluted in amongst other data types. But unfortunately in those cases there's not much you can do about it perhaps unless you were going to build your own repository. Of course that could involve a lot of time and money and effort. Check the match particularly data needs, formats accepted, make sure of open and restricted access. You will also encounter this notion of persistent and globally unique identifiers. So something that you probably have already encountered are digital object identifiers or DOIs. Now that's one particular type of persistent identifier. Excuse me. Many repositories will actually provide unique identifiers for any data that's deposited. Not all of them but many do. And this is actually something that you should look out for. The final thing perhaps to look out for is look out for trustworthy digital repository. This means that that repository has been accredited by an independent body. This might not always be possible but do look out for that. So yeah. Just finally mentioning the persistent identifiers. Excuse me. PIDs come in various forms. I describe DOIs. You'll also encounter orchids which are for individuals as researchers for ISBNs for books and so forth. Typically they're actionable, meaning that if you were to type the ID into a web riser then it will be resolved. And many repositories will assign them on deposit as I previously mentioned. So that's the end of my presentation and I'm going to take questions if there are any. And hopefully Thomas will be able to join us as well if he's not all right. Thank you very much, Venkat. Again, my apologies for putting you on the spot, but I think you did great. So if there are any questions from you for Venkat, feel free to send them either in the chat or in the Q&A. There's one question in the Q&A. I don't know if you see this. Maybe we can read it out loud. I'll read it out. Sorry, I started to cough at the end. So the question is, having a lot of different repositories in archives however seems to have two drawbacks to me. A, due to the fragmentation to different databases, the findability will certainly be hindered to a certain extent. And B, research consortia may put data in multiple repositories, making the workflow sticky. So we would ask or recommend that you stick to one repository. When you finish your research project or when you've finished with any particular data set, that's when you're going to deposit your data, of course. Try not to fragment. You are absolutely right. Try to find one repository that you can actually deposit your data in. If you do end up putting it into more than one repository, then it's not that it's not allowed. It just makes things more complicated. So yes, I agree. Try to stick to one repository. Research consortia may put data in multiple repositories. Unless you know of specific examples where that is true, then I'm not sure that is actually true. So maybe you have a particular experience or examples of this, but I'm not so aware of that. Okay, I've just got another reply there saying, I mean, you have three different universities in an EU project. Any of the Unis has its own repository. Okay, where to fit the project data? Yeah, okay. So it depends on the policy of the university or the institution, I admit. There might be a local, meaning the institution has its own policy that needs to be adhered to. Now, in that case, if you are in a consortium or some kind of pan-European project, then this is something that you need to discuss from the very start. I can establish that you will use a common repository perhaps because that is the solution there. Unless, if that is not possible, then I'm afraid you will have to live with depositing into multiple repositories, but hopefully that's not the case. Well, of course, thank God, aggregating services like OpenAir should make this a little less sticky or seamless as well. But that might not always address the question whether a local policy, which one is more important, basically. And unfortunately, maybe some institutions make that a priority, but it's this idea of ownership of data. Okay, so there's another follow-up question on this. Sorry, there's another question saying, wouldn't it be better to have the EU forcing Horizon 2020 projects to use one central repository? Yeah. The problem here is this idea of forcing. I would somewhat agree, but it's better to try and persuade people rather than forcing people to follow these ideas. And that's, I think, where the EU stands with the way these projects are funded. They don't want to enforce any kind of particular solutions onto the researchers. Yeah. We are talking about standardisation, but leave consortia alone with the choice. Yeah. I agree in many ways. Yeah, we're trying to persuade people. We're trying to make a culture change here. That's essentially the part. We need to try and encourage people to follow these principles and hopefully that will happen. And it is happening, believe me. It is. But we want to allow there to be enough room for consortia to be able to make their own choices. I don't know if there's anybody from EOSCUP in the audience who might be able to elaborate a bit on the role, potential role of EOSC in this process. Mm-hmm. I'm looking if I see any familiar names, but not immediately. Feel free to chat, shout in the chat. So do you want me to go to the questions that were written before? Oh, yes, please. Yeah. Let me just find those. I can, I can hold on. Yeah. Okay. Can you hear me? Oh, look who's there. Great. I just thought to use this short pause to check whether I fixed the audio issues. Oh, perfect Thomas. Then we will just, we will just finalize the questions. Yeah. I can go to these, these questions that Gwen emailed to me. These were set before this webinar started. So I'll just, do you mind Thomas if I just quickly go through these? Please go ahead. Yeah. Okay. So the first question was, what is the human and financial cost of RDM? So that's a very good question. Of course. We typically say that maybe 5% of the budget of any given project should be put towards proper data management. It will just depend of course, depending on the project, because some projects might produce vast amounts of data. Others might not produce anything in fact. And it needs to be this taken in a case by case manner. But on average, we seem to think about 5% of the budget should go towards that. The next question was how to choose the best repository. So I'm not going to say any file format or file naming. I hope I've addressed that already. We'll plan S also affect the publisher requirements for research data. I actually don't know the full answer to this, but I, I'm, you know, I'm not the best person to ask about this. So I'm not going to say any more. How do academic libraries participate in the RDM to provide users information needs. Again, that's a very good question. And it will just depend on your host institution. Perhaps they need to be involved. Certainly that the librarians should be involved in building the infrastructure of your host institutions. RDM infrastructure. And this is something that we at the DCC actually do by going to many different institutions around the world and actually assessing what the needs are at that institution and helping the librarians, the IT professionals and the research officers to actually come together along with the actual researchers themselves to build RDM infrastructure. Since Horizon 2020 focused on innovations, do you have any feedback about how project partners from industry and other privately funded entities perceived open data? Were there any legal concerns, especially on intellectual property rights or some embargoes on opening the data? I think I might leave that to Thomas. He might actually have more better answer to that question than me. Next question. Would be interested in hearing more concerning the use of data from social media and research, especially in terms of sharing and ownership. Okay, that is a great question, especially with what's happened with Facebook and other platforms perhaps. I'm afraid, again, I don't have enough knowledge there of this. Next question. I would like to hear about working with data sets within RDM systems and also about the role experiences of academic librarians in this part of RDM. Okay. Yeah, I don't really have an answer there either because you would really need to speak to your local librarians perhaps. Working with data sets, yes, certainly as a researcher, a librarian should be someone that you can turn to if you have established good training, front calls in your institution. They are the people that researchers should be able to turn to find out how to create data management plans, find out about repositories and other things like that. But, yeah, specific examples. It's beyond the scope of this webinar. What's your recommendation for future applicants to universities to horizon projects from what key thing to do and not to do when writing dissemination and communication activities? I think that the key thing here for particularly horizon 2020 projects, is to create a data management plan. Again, it's beyond the scope of this webinar, but you can find out lots of information about horizon 2020 projects and data management plans, DMPs. If it's something that you've not learned about yet, please do just a simple search for data management plans and horizon 2020 and you'll be able to find out more about that. And also templates that you can use to put together a DMP. And finally, what's the best and most efficient way to store your data? Okay, hopefully I'll address that in my actual slides.  Okay, thank you very much, Venkil. I just want to make use of this little interval to just to point you to a couple of resources that are also created by OpenAir. So, if you go to openair.eu slash guides, you will find quite a lot of guides and fact sheets and resources that are linked to both the topics of Venkat's presentation as the one from Thomas's. So, feel free to take a look there and browse around. But that being said, I would say that Thomas, that we try to start your presentation. Yeah, absolutely. You can hear me, right? I can hear you fine. So, everybody, this is Thomas Margoni from CREATE, which is the Department of the University of Glasgow that deals with copyright issues. And Thomas is going to talk about copyright and legal issues related to research data management. And I think he's going to talk about copyright and legal issues related to research data management. And I think he's also going to address ownership issues, right? Yes, I hope so. At least these are very complex issues. And I really hope that we'll be able to do this in a understandable and not excessively accessibility this way. You can see my slides for now. Okay. Do we have to do that again? Yes, okay. Now we see it. It's fine or do I have to full screen it? I think you have to full screen it because now we see your navigating mode. Okay. Now? Still navigating mode. Yes, this is full screen. Perfect. It's full screen? Okay. Okay. So I think it's better to keep interest high and the time within a manageable amount, let's say. Is that okay? Yes, 20 minutes is fine. I mean, we can go over time. So we started late. No, just, you know, as often happens with legal analysis, it's really exciting and interesting. But over time, I think that we kind of discover that this applies only to lawyers and other people find it extremely, extremely boring. So also for the sake of this lawyer effect, I'll try to be as concise as possible. So yeah, well, thank you very much. Thank you to everyone to go in for inviting me and organizing these activities. Thank you also to Venkat for a very interesting presentation and sorry for the initial problem with my audio. So I will go over some of the issues that actually Venkat mentioned because I found his presentation extremely inspiring. But I do think that he and I do think he has identified a couple of basic concepts that are the reason why we have problems with data from a legal point of view. And one of these concepts, the first one and the aspect that I focus most on my research attention, is precisely that of data ownership, which is a very strange and to some extent a recent concept. Because if we look at the area of law that traditionally regulates, let's say, information property, well, one of the basic assumptions is that data as such is not really protected. What is protected is an original expression. So the usual example is a plot about a doctor who goes mad and creates a monster that looks like him and eventually will underline the philosophical tension between human condition and creating life. Sorry, everyone can do that. This is an idea. This is not protected. Now, if you start using the expressive form that Mary Shelley did or any other author, then there it's the area where the form of so-called property, but also here we have some clarifications to make, is that of copyright. Copyright doesn't protect that. It doesn't protect basic information. It doesn't protect facts. It doesn't protect any of these aspects. Why? Well, because they are the basic bricks of our knowledge. The reproducibility crisis that we were mentioning shouldn't, unfortunately, shouldn't be too surprising once we notice how much within research we have switched to a model where data has an economic value. And obviously if it has an economic value, then the closest tool that the law offers you is some form of property. So there are often these associations. Oh, that's my data. When the truth is that, you know, it's not really your data. It's simply some basic information. And international conventions in the field of copyright and the light rights usually clarify this aspect. Only your original expression, the output of your ingenuity, the original, the choices that you make, only this aspect can attract protection. But the basic data, no. Which doesn't mean that there are no forms that can offer some sort of proprietary protection to data. But then what is protected in this case is not the data themselves. It's not a single datum and it's not any form of data aggregation. But we have specific legal requirements that tell us that, you know, this data, it's protected if it is constructed in a specific databases. So there needs to be some sort of methodological or systematic arrangement, et cetera, et cetera, et cetera. I will not enter too much into these legal details. We have guides where we try to, some of which Gwen showed us just now, some others are having my slides. And I refer you to those guides if you want to have the legal details. But I think that the main message that is important to understand now is, you know, data ownership is the enemy of reproducibility. Because to the extent to which you have to ask for permission to use someone else's data to some extent, then you are creating barriers to this reproducibility. And this is a first main aspect to keep in mind. The fact that data as such is not protected and should not be protected has really to do with basic freedoms, such as freedom of scientific research and freedom of information and end of speech. And this is quite clearly stated in international conventions. In Europe, we're a bit peculiar in the sense that we decided to offer one additional layer of protection to data, the famous or infamous Swai generis database, right? And in that case, you do not need creativity. You need a substantial investment in collecting, verifying, or presenting the data, but not in creating it. Once again, all the information necessary is in the guides, but the main problem here, it's the same. You do not want as a legal system to favor forms of property over information because this limits basic fundamental freedoms. And again, if you impede the free flow of information, if you condition it to specific barriers, if you create transactive cost to that, then these are the spaces where data, sorry, the reproducibility crisis or reproducibility issues arise. And now after this nice statement, I basically am telling you exactly the opposite. So what has happened over time? It's that you have to imagine that copyright laws, most of them have been written 200 years ago. Now, back then the attention was on mostly books, maps, songs, that didn't really attract much of an attention. All these relevance on the role of data, it's certainly the result of the last few decades, few years of data analytics developments from tax and data mining to machine learning, et cetera, et cetera, et cetera. So a legal system that was designed to protect knowledge 200 years ago with minimal updates had to offer some sort of answers to modern questions, questions around data. And whereas the main principle survives, data as such is not protected, it turns out unfortunately that at the end of the day you have to ask so many permissions because sure, the data as such is not protected but because you need to make a copy of almost any support where data is saved, then almost always, not always, but very often you have to make a reproduction of, for example, a database and that is protected and then you need to ask for permission. So there are these kind of mismatch between what is the basic legal principle and what is the applicability of the law to a specific case. Now, you can perhaps connect these, hopefully not to abstract scenario to the debate that we had around tax and data mining and on whether we need a tax and data mining exception or the right to read it's the right to mine. These two, this dichotomy I would say mirrors very well this tension that I just explained to you. Of course, the right to read is or should be the right to mine, while if I read something, I don't need permission but if I do that through a tool called a computer then I need permission, that doesn't make too much sense. But unfortunately, there is so much legal uncertainty in this area that at the end of the day just in the opinion of the majority that approved the directive it made sense to have a specific exception. So the text and data mining exception that allows you to do certain things with data. But this is also an implicit acknowledgement that well, we could say that data as such is not protected but at the end of the day, a lot of the ways or the forms in which data is stored or contained or saved or presented while those forms are protected. So we are kind of surreptitiously denying those basic principles of free flow of data and creating some legal barriers. The other aspect that before we spend a few words on the article three, so the text and data mining exception which has been approved and should enter into force over the next now, I think one year and eight months. I want to mention another aspect that Venkat identified in his presentation, but I will not go into the detail from the point of view of a researcher that it's data. So whatever the legal impediment, this is not, you know, I guess probably rightly so it's not something that should bother the researcher. Whether you can do or don't, you know, or you cannot do is a certain thing. It's the question that really matters to you whether the reason why you cannot do it it's grounded in copyright or personal data or freedom of information or public sector information or contracts or technological protection measures. This becomes a bit more of a secondary issue I will imagine to many researchers. Unfortunately from the legal point of view, we couldn't be, we couldn't be on to limiting our analysis to corporate and personal data more far apart in terms of what are the principles, what are the reasons, what are the aspects that we want to protect. So many of the things that we can say about that ownership look at a certain area of law, but when we talk about personal data, we are looking at a completely different area of law and the tools, the permissions, the kind of activities that you can or cannot do work on a completely different basis. And within open air, we have a task force where a colleague of mine and I are working on these two issues. I am focusing as probably having further this point on the copyright and proprietary and ownership issues, whereas the colleague who probably you know, Prodromos, he focuses on the data protection aspect. I will not say much more about data protection for this reason. I don't know if Prodromos is giving a similar talk within the open access week, but certainly the guides that open air has prepared cover both copyright issues and privacy or data protection issues. So very briefly on the text and data mining exception, first basic clarification, certain member states have implemented the text and data mining on their own, the UK for example, but because this was done within the old European framework, all these text and data mining exceptions have to be limited to non-commercial purposes. Whereas the European text and data mining exception, article three of the copyright and digital single market directive, the directive has been approved, but in last April, and since the publication member states have two years to implement it. So member states are right now in the process of implementing this example. So this is probably something that you don't have right now today at your disposal, but it would become available very soon. And it will be the big advantage. It would apply almost in the exact way across all the, at this point, I don't know what to say, 27 or 28 countries anymore. So that's a big advantage also for the issue connected with big large consortia, which sometimes have to follow different national legislations. In this case, article three would apply in a very, very similar way everywhere in the EU. And article three did some good stuff. So the definition of text and data mining, I skim through it quickly. It's broad enough to cover almost any data analytics. Literature hasn't found a big problem here. The scope, unfortunately, it's quite limited. It's limited to the right of reproduction. So you can make a small copy, for example, of a database if you want to extract data, but you cannot communicate or distribute that copy any further. This should be contrasted, for example, with what is the situation in the United States where a fair user says that these activities are transformative. They create added value. So we don't have, for example, this limitation to their production. You can do whatever you want. You can communicate your results to the public. On the basis of the EU solution, well, if your results are production in part of the original, in theory not. So you see that we have a big problem here. Beneficiaries, again, the EU solution limits the availability of this exception to research organizations for research purposes. Probably many of the attendees of today's webinar belong to one of these organizations. So that's probably less of a concern, but we have to keep in mind that here we are cutting out all the small and medium enterprises. One could say, well, you know, the commercial sector should pay for it. Sure, but we're cutting out also all the startups and small and medium enterprises. And the difference is that, you know, incumbent players do have the money to pay for, let's say, a license, whereas startups don't. So this condition that at first sight might sound reasonable. But the difference of marginalizing further the new entrance in the data analytics field, whereas the big ones don't have a problem with that. And again, this I understand you can get a bit technical, but the good thing is that if you're doing text and data mining for this matter, either under, let's say the UK exception or the future you want, and the terms of use of the website say you cannot do this. For example, the terms of use of the website say, by reading this website, by accessing this website, you accept the terms and conditions, which no one and never reads, but at some point in article <IP_ADDRESS>.44, it says you cannot text and data mine. Well, that provision, it's void. So this is a good thing. However, if the same effect you cannot data mine, it's done through encryption, then well, the discourse becomes a bit more complex, but the general answer is you had a problem. I'm happy to discuss this further. I don't want to go into the details because it can get quite technical. And again, I find it extremely exciting, but perhaps, you know, I'm the only one here. Now, the good thing of or apparently a potential good thing of the exception, it's that it created in its latest version, a new article that allows everyone. So you see in point number three, it's all not only research organization to perform text and data mining. However, in this case, right holders can limit contractually. So the famous close 4.3 point, et cetera, et cetera, et cetera. In this case, under article four, it could be enforceable. So then again, you see there is always this tension between openness and non-openness in this specific area of data and data ownership. Again, if this sounds complicated or tedious, well, that's understandable. You're not alone out there. We tried to create a few guides. And here I reported the main guides that we developed with OpenAir. This focus, as I said, on copyright ownership and reuse. So Venkat earlier on showed some licenses, creative commons. So here we have a bit of an analysis, what you can do, what you can't, how can you combine different licenses, for example, et cetera, et cetera. And I'm sure that those slides will be available. So feel free to, you know, click on these guides, have a look, hopefully they are easy enough to understand. And, you know, if they're not, please let me know. So this is an extreme summary of my presentation. I think that the main message, it's that, surprisingly, and probably also deceptively to some of you, that it's not and should not, most importantly, be owned. And for how much you can feel a strong relationship to your data, because I don't know, maybe you have spent the last five years collecting it. There is a much larger public interest need there that wants, that requires that data to be open. Obviously, we're not in a black and white situation. No one will oblige you to disclose all your data immediately before you have had the opportunity, for example, to verify it or to perform a number of activities. But, you know, these are our details, if we compare them with the problem connected with the fact that we think that data should be owned. And it's like to say that ideas should be owned. That if you think that, you know, the idea of, I don't know, a flying object, it's good, well, then it's yours, that no one else can build airplanes. And I'm afraid that in the case of data, we're only starting right now to understand the far-reaching consequences of these assumptions that we are making. So hopefully, the application of open science principles to the field of data will guide us in the right direction. Thank you very much. Thank you very much, Thomas, for this very interesting presentation. Let's just briefly, I'm not going to have this Q&A for too long, because we're going a bit over time, but I would just like to refer back to the two questions in the list that we put in the chat that Venkat referred to you, because the one question was, are there any legal concerns when it comes to IPR with private partners in an industry? I don't know if you have any feedback on that from Horizon 2020 partners? Well, you know, sorry. It's a huge question. And usually when you sign a GA, there is a specific section dedicated to IPRs and the IPRs that already exist within one entity and whether those should be considered owned or not owned by that entity and what happens if part of those IPRs are reused by a partner. But there really, it depends on what you write in that grant agreement. So unfortunately, I'm not aware of whether there should be and if there are not, it's something that probably should be done, a guideline or a best practice on how to write that clause. Unfortunately here, we are always in the same situation. As a researcher, it's usually not you, but it's your TTO or grant team at the university who writes that clause. And their job, if they want to do this job well, they have to protect their employer. So they normally apply a very restrictive clause. Everything that belongs to the university of Glasgow, belongs to Glasgow and it's ours and you cannot reuse unless it is specifically authorized. And I agree, again, sorry, I keep citing Venkat, but I agree with his statement when he said that we're trying to change the culture here much more than the law. I mean, this question that you just reported, it's not as much as a legal question as it is a cultural question because honestly, it depends on what you want to write in that clause. If we understood that should the universities funded with public money really own the IP on what they have produced? Well, there is quite clear in the literature, there are clear cases showing that it's inefficient to recognize these IP rights. And then if let's say that the IP right is a patent, you hit the jackpot and you make a few millions. But the truth is that the general cost to managing and administering all these IP data largely outweighs the advantages of exploiting IPRs from a university point of view. But then again, this is not actually the, you know, direction where many universities are moving. Obviously it has to do with bigger questions connected to public funding, etc., etc. But yeah, I think I would stop here. Okay, thank you. So the next question was about social media, but maybe I'll propose that we refer that question to Prodrovis. I'm correct that he would be the one. There's a couple of questions in the Q&A window. Yeah, yeah. Yeah, I was going to get to them. I just wanted to make sure that we covered all the questions that people send them beforehand. So the one one's sure about the use of data from social media and research, especially in terms of sharing and ownership. So Thomas, you understand you correctly that that would be more a question for Prodrovis. What data, right? If it is personal data. Yes, I mean, don't get me wrong. It's not that I refuse to even consider personal data issues. I do have a working knowledge of those. But the guides have been written by him. But don't get me wrong.  There are two types. The data that you are obtaining could be personal data. If they are personal data, meaning that they identify or could identify an individual, let's say an email age, then in this case you have to look at data protection issues. And yes, consent is so the main thing of data protection of the GDPR is that it tells you that you need to use to have a legal basis in order to reuse that information. And the consent is certainly a very important to legal basis. However, it comes with strings attached in the sense that consent can be withdrawn. So, you know, you acquire consent, but in the known one year and a half, someone writes you and tells you, please, you know, I would withdrawing my consent to eliminate the data that applies that relates to me from your database. In that case, you have an obligation to do that. It has to be specific. So it has to be specific for a specific for a purpose. So if you acquire consent, because you want to, I don't know, analyze social interactions. You cannot reuse the same data for a different consent for a different purpose such as analyzing. I don't know, biological interactions, if this means anything, making up examples, but the problem of consent is that it has to be specific for a purpose. And it has to be time limited. So, you know, you have to indicate the time and at the end of the time you have to delete it. So what we're doing the guides is actually identifying situations where the, you know, different legal basis operate better for you. Sometimes, depending on the situation, but again, I refer you to the guides, a different legal basis other than consent may apply may offer advantages to the kind of activity that you're doing. So in this case, for example, a legitimate interest or the fulfillment of a contract, they may offer you a different set of opportunities or limitations. So this is something that cannot be answered in general, it has to be analyzed in the light of the very specific situation. And then again, the data ownership or copyright aspects are completely different. One would need to see what the terms of use of that specific website say and if they limit or permit certain data analytics, you have to analyze where they're depending on the jurisdiction where you are. There is a mining exception that applies to you, whether it is limited to specific scopes, whether it is limited to specific entities or whether it is limited to specific type of uses. And then again, you know, you have to be to almost develop a case by case analysis, there is really no other way out. But what we're trying to do with the guides, and sorry that I keep promoting the guides, but I really think that one of the main contributions of open air in this specific field, it's trying to standardize a set of common issues that researchers might encounter and offer answers to these type of situations. They won't be as detailed as your individual situation, that's simply impossible, but hopefully they will get as close as you can get. And you can use those guides when you go talk to your technological transfer office or to your university grant team and show them that the guide says something else than the usual default disappointing answer. So maybe again, this can help change the cultural approach in this type of issues. Okay, thank you. Before we close, I'd like to go back to the Q&A. Ben knows because there are like three remaining questions there. The first one is one by Sebastian Lange who basically makes a statement, not really a question. I'll just read it so that you see it all. Science is also that it arises from itself and it's not dictated by superordinate political organization. Even it would be nice to have a uniform structure. Structure is always a constraint of science, especially when it comes to measurability. As there is not really a question there, I'll take the liberty of going to the next question. That's okay, I'm sorry Sebastian, but we're running out of time. There's a question for Thomas by Petra again related to the ownership of data. So how can you explain to patients that their data cannot be owned by them? I don't know if you want to comment on this Thomas, I think you already partly answered this in your previous comments. Yeah, it really depends on the data, right? So it really depends on whether these are, if they are personal data, meaning that they identify the individual. Well, ownership is not the right word, because ownership means property and if you own something you can sell it, whereas you cannot sell your personal data, you can do certain things, but not others. So it's not ownership, but there certainly be a connection between the, it's like, you know, your personal data and extensions of your personality. But you wouldn't say that you own your personality.  So ownership is not the right term here. There is certainly a power to control, but only to the extent to which certain certain data are identified or identifiable. Because otherwise, there is, you know, this balance that you have to make between public and private and say, well, you know, combining and linking all these gigabytes of data, right, of data can help finding new, you know, discoveries. So that's a good thing. Okay, so let's leave the final question. It's to be expected and I'm surprised that we kept it at bay for 90 minutes, but the question is what will happen to your research data stored in the UK after Brexit. For example, from research projects in which a number of universities from different EU member states participate, which law applies UK or EU law. I don't know. Thank God, Thomas, you're both based in the UK, so I don't know if you want to, if you both want to. I will say that largely depends on, you know, what will the law say.  I would imagine that it doesn't go specifically, you know, in this very scenario but probably indicates something on the future relationship between UK and and the EU. But then again, you know, it's also a matter don't expect that the law can offer you every single answer to your question, certain other things. You know, there are certain things that are not regulated by the law, but maybe by contract so is there a store the data in the UK, what does the agreement say, can you get it back, you know, again, it really depends on the on the on the details. Yeah, I agree. It's too early to say, I think, but, but if it's EU funded research, then I think that it'll be very difficult for the UK to block access. Sorry, yeah, it's just going to finish there. Okay, well, let me finish the entire webinar up to that to that cliff, slight cliffhanger. So thank you. Thank you very much for Thomas and Venkat for agreeing to talk here I think it was both of both of the webinars are very interesting. Again, my apologies for you the audience for the slight technical problems at the beginning and Venkat you for making your rush through your presentation because I communicated the time wrong. All of these presentations and recordings will be put online. They will be put on the open air YouTube channel on the open air webinar pages. We'll also distribute them by a social media and you will also receive one email from us next week with an evaluation form and if they will also link to all of these recordings. You'll definitely get them somewhere in the next somewhere in the next week it might not be today or tomorrow but rest assured they'll be they'll be there. So, then have a nice afternoon I'd say and I would I'm hoping that I will see some of you back in one of our next webinars this week. Thank you very much. Thank you.


I'm Shalva from Just Move Down and I'm here working on the Lost Trailer for Mortal Kombat X. That's sick. It could be... Father! It's actually a re-editing song. I felt more like I was editing a movie. I kind of forgot that it was a video game during the edit. That's how you do it.


The novel JKS DGCN algorithm was developed for the purpose of accurately classifying sleep stages based on multiple biosignals. It uses the standard CNN to extract features from each signal, then uses two adaptive adjacency matrices to connect signals from the same EPIC or nearby EPICs. This allows the model to capture the transition between sleep stages. Additionally, the model employs a jumping knowledge spatial temporal graph convolution module to extract spatial features from the graph convolutions and temporal features from standard convolutions. This enables the model to learn the transition rules between sleep stages. Finally, the model's execution speed is evaluated using the ISRIC-S3 and ISRIC-S1 data sets, where it achieves the best performance compared to other models. This article was authored by Xiaoping Ji, Yan Li, and Peng Wen.


Hello, and welcome to today's session of the 2021 AWS Global Public Sector Partner Awards. I'm delighted to present our special guests for today's program, and they are Kyle Hines, VP Strategic Accounts at Presidio, as well as Chief Chuck Hoskin, Jr., Chief of the Cherokee Nation. Welcome to the program, gentlemen. Thank you. Terrific. Well, delighted to have you here. We're going to discuss the key award of best partner transformation, most impactful nonprofit partner. Of course, now highlighting some of the technologies now being leveraged to help preserve the Cherokee language as well as its culture. Now, Chuck, I'd like to start with you, and if you could describe some of the challenges that the Cherokee Nation is now faced with in terms of preserving the language and its culture, and how you see technology being able to really help preserve it. Well, thank you, Natalie. It's really good to be with you all today. The Cherokee language and culture is what makes us unique as a people. It's the link that links us back to time and memorial through generations. And over those generations, there have been many threats to our language and culture. There's been disease after European contact. There's been dispossession. There's been our forced removal on the Trail of Tears. Other pressures in more modern times have continued to erode our language and culture, including boarding schools, the public school system through most of the 20th century, as Cherokee Nation has gotten back on its feet. That is to say, when the government of the United States has allowed Cherokee Nation to do what we've always done well, which is to govern ourselves, chart our own destiny, and preserve our life ways. We've been able to make preservation efforts. But those generations of eroding our language and culture has come at a steep cost. We're the largest tribe in the country, 392,000 citizens. And by the way, we're mostly in Northeast Oklahoma. We have Cherokees living all over the country, even all over the world. And we only have 2,000 fluent speakers left. So it's a great challenge to save a language that's truly in danger. And if we don't save it, generations from now, we may do a number of things exceedingly well as we do today, business, providing education and housing, creating a great healthcare system.  And so what we're doing today, working with great partners like Presidio, is just indispensable to what's really our most important mission. Yeah, terrific. Well, thank you so much for those insights. I'd like to switch it over to Kyle and hear about the technologies now being utilized to preserve the Cherokee language and culture. Sure, happy to Natalie. And thanks for having us this morning. So yeah, when we started to work with the Cherokee Nation, it was very clear to us that there's obviously a higher power or a higher mission here. And so it's really been an honor to work with the chief in the nation. And what we've been able to do is take what the Cherokee Nation is trying to do in terms of language and cultural preservation, and build solutions in really a very modern way. So between Inaugay, the 3D mobile open world game and the virtual classroom platform, it's entirely a cloud native serverless solution in AWS, using a lot of the most modern tools and technologies in the marketplace. For example, in the mobile game, it's built around Unity. And the virtual classroom platform is built around the Amazon Chime SDK, which allows us to really build something that is very clean and light and focused on what the nation is trying to achieve and really cut out a lot of the baggage and the other sort of plumbing and various other technologies that this type of solution would have taken just a few short years ago. Yeah, terrific. Well, Kyle, staying with you, what do you think were some of the factors behind the development of this solution? Yeah, so I think flexibility was key, was maybe the biggest design goal in building these solutions, because you learn a lot when you originally set out to build something and it starts to impact real users, and in this case, speakers of the Cherokee Nation, you learn a tremendous amount about the language and how it's used and how people communicate with each other. And so the main design goal of the solutions was to allow a sort of flexibility that lets us adapt. And every time we learn something and every time we find something that works or perhaps doesn't work quite as well as was imagined, we have the flexibility to change that and kind of stay nimble and on our toes. Terrific. Well, Chuck, now switching over to you, why do you think that some of these, you know, platforms like the virtual classroom are so effective with Cherokee speakers? Well, a couple of reasons. One pandemic related, you know, during COVID, the worst public health crisis that the world seen in living memory, we have had to adapt quickly to continue on our mission to save this language. We couldn't afford a year off in terms of pairing speakers, by the way, most of our fluent speakers are over the age of 70 with young people who need to learn the language and be the new generation of speakers. So it's been really important that during those difficult times, we could connect virtually and the technology we've been using has worked so effectively. But the other is really irrespective of what's going on in terms of having to isolate and social distance and things of that nature during COVID. And that is just making sure we can make this language accessible, particularly to young people in a manner in which they are becoming accustomed to learning things throughout the rest of the world. And so using platforms that they're familiar with is very important. But it also has to be something that an older generation of these fluent speakers, as I say, most of them are over 70 can use. And that's what really has been so effective about this platform. It's so usable once you introduce it to people, whether it's a young person who can adapt pretty quickly because they're growing up immersed in it, or it's someone who has not been familiar with technology, with just a little bit of showing them how to use it, suddenly this classroom becomes just like you're in person. And that makes all the difference in the world in terms of connecting these young people with their elders. That's the other thing is Cherokees are, by nature, very much part of a big extended family. And so that personal connection that you can maintain through this platform is really important. I think it's going to be the key to how we save this language, because as I say, we have Cherokees all over the country, even all over the world. We're going to harness our numbers, the large population we have, and find those with the interest and aptitude to learn the language. We must use this technology, and so far it's worked well. Yeah, terrific. And now, switching over to Kyle, would love to hear from you how your team developed this technology, how they really thought out what kinds of methods are really going to drive the interaction and the immersion and engagement among these disparate demographics of elderly Cherokees and also the young generation. So how did your team go about developing that? Yeah, it's a very good question, because in a situation like this, there is no shortage of different ways that you could have built a solution like this. There are a lot of different ways that it could have been done. So the tact that we took was a rigorous focus on the user experience and on the experience of the speaker. And that allowed us to detach ourselves to a large degree from what were the exact technology choices that were implemented in terms of AWS services, other open source packages that run on AWS. It's being able to focus completely on what the nation was trying to achieve with the speakers, both through the game and the virtual classroom platform. It led us to take a lot of other design decisions and technology choices sort of into the background and behind a level of abstraction. And so there's always quite a bit of rigorous testing and really making sure you understand how something's going to perform in the wild. But the reality of the situation was the whole reason for doing it was the experience of the speakers both in the game and in the classroom platform. So we stayed very focused on that and made technology decisions sort of second fiddle or lower priority. Perfect. Well, Chuck, how do you think that these kinds of innovations could be applied to other areas of the Cherokee school system? Well, our greatest challenge is preserving language and culture. But we also have as part of our mission to educate this new generation of Cherokees coming up. For years and years, really generations, Cherokees who were able to get a good education, many of them left our tribal lands for new opportunities. And so we lost a great deal because of the economic pressures here in Northeast Oklahoma, particularly on our Cherokee lands. So the task now is to generate opportunity for a new generation coming up. Education is key to that. And so if we want to create a pipeline of young Cherokees who want to get into the healthcare fields, want to get into aerospace, want to get into other professions, we've got to create an education system that is steady art and modern. We have a school that is K through 12th grade, K through the senior year. And so we have an opportunity really to do that. And I think for the first time in our history, in this era, I'm talking like the last few decades, we are able to really craft education in a way that works for us. And using technology and making choices about what that technology is, is important to us. It's a bygone era in which the federal government or the state is sort of imposing on us what choices we make. Now we can reach out with great partners all over the world, like Presidio, and say what solution can work for our classroom when we can identify what the great demands are on the reservation in terms of jobs. And one of the great demands we have is healthcare. So how can we use technology to inspire little Cherokee boys and girls to grow up and be doctors and nurses here in just a few decades when we're building this great health system? Well, we're going to use technology to do it. So the possibilities are really unlimited. And they need to be because we think our potential here in Cherokee Nation is unlimited. Yeah, I mean, that's terrific to hear how technology is really encouraging younger generations to study, learn, and really push themselves further. You know, Kyle, I'd like to switch over to you and hear a little bit about the benefits of launching this kind of platform on AWS. Yeah, there's there are a lot of benefits to building this on AWS. And I think that it spans a couple of categories even. So I mean, from a technological perspective, there was every tool and every service that we needed to build both of the solutions that we built right there in AWS. And when there was a when there was a time where we needed to jump out and use a project outside of AWS running on AWS, such as the Unity engine, AWS makes that very easy. So I would say that the choice was easy, because there are technological realities and the breadth and the depth of the technological portfolio in AWS, combined with the partnership that we get from them. It's really, you know, there's a lot of support when it comes to, Hey, we're working with the Cherokee Nation on something that's extremely important. We need your help. We need you to help us figure this out. It's, you know, it's never been hard to get that partnership. Perfect. And also following up on that little love to hear, you know, how AWS really helped with flexibility and also the cost effect effectiveness of this kind of platform. Yeah, I would take those questions backwards or in reverse order, because the cost effectiveness of the solution is really, it's really something to make note of because when we build something in the way that we built these platforms, they're serverless and event driven, meaning that the Cherokee Nation is not paying for a solution constantly as we would in the, in lives past running things in data centers and such. It really, the services in AWS allow us to say, Hey, let's, let's spin up certain pieces of functionality when they're needed as they're being used. And the, you know, the meter is running during that time and the cost is occurred during the time it's being used and not all of the time. So that really has a dramatic impact on cost effectiveness. And then from a flexibility standpoint, as we learn new things as we evolve the platform, as we grow this out to more and more speakers and to more and more impact to the Cherokee Nation, we have all kinds of different technology choices that we can make and it's all contained within AWS. Yeah, and I'd like to open this now to both of you, you know, starting with Chuck, how do you think this kind of technology could be applied to other cultures or languages that are seeking to preserve themselves?  You know, what steps can be taken so that, you know, humanity can preserve these important languages? Well, you're right. There are so many endangered languages around the world and indigenous languages are unfortunately dying all over the world all the time, even as we speak, they're slipping away. The United Nations has dedicated the next decade to the preservation of indigenous languages. That's gotten many leaders around the world thinking about how we can save languages here in this era. And I would encourage any tribal leader in particular in the United States, but I think it certainly applies around the world to seek out this technology. I mean, Chair Ganesh is in a position now where we can seek out the best in the world in terms of partnerships. And we found that in Presidio. And of course, they're using AWS, which means they're using the best in the world. And so the technology exists and the willingness to work together exists. And I think generations ago that would have been not something we could have connected well on in terms of partnering with companies that were doing cutting edge things. So if you're looking to connect generations in terms of learning and sharing the language, which is just I cannot stress enough how indispensable that is to language preservation. This type of technology will do it. You know, there are some I think that may think and I don't have a technology background that if you're using this cutting edge technology, I mean, this is the best in the world that you're going to speak only to this young generation coming up and maybe it's inaccessible to an older generation. It's just not the case. This is so user friendly that we've been able to connect elders with young people. And if anyone in the world interested in preserving languages could see this in action, could see a young person sitting next to an elder talking about the technology or connecting virtually, it would change their whole perspective on what technology means for language preservation. Because I promise you all over the world, the great challenge is you have this group of older generations of people who know the language, they have it in their hearts, they have it in their minds, and they're slipping away just from the passage of time. Connecting them with the generation coming up is just what we need to do. This technology allows us to do it. Yeah, you know, Chuck, following up on that when I hear about elderly people being able to connect with the younger generations in this way and share their history and their culture, I'm sure that also, you know, it must have a positive mental effect for them, right? So elderly are often isolated. Do you have any insight on that? Any qualitative insight, what you've heard from people using this? Yeah, absolutely. And I think the last year has proven how valuable it is. I mean, we lost over 50 fluent Cherokee speakers. And I mentioned earlier in the program that we only have 2,000 left, 50 to COVID and more to just the passage of time and old age. But we have many that are active and engaged in language preservation. And they have said, to me, how valuable it's been to be able to be at home and yet still feel like they're part of this great mission that we have at the Cherokee Nation. Understand that this mission that we have is on par with what any nation in history has set as a goal to shoot for, whether it's the United States wanting to land a man on the moon, we're trying to save the language. This is that level of importance. And so for an elder to feel like they're connected and still contributing during this past year, difficult times, that makes all the difference of the world. And even as I say, as the pandemic recedes, and we hope it continues to recede, there is still a need for elders to stay connected. And in many cases, they cannot do the poor health, do the lack of transportation. This knocks down those barriers. And so there's a great deal of joy that has been gained from using this technology. And honestly, just talking to elders about young people getting the opportunity to play this video game, even some elders that were voice actors in this game that Presidio helped us develop. I mean, I can't tell you how important that is for somebody to use their language to make a living. And that's part of how you preserve a language. Presidio has showed us a way that we can do just that. So we're not only training new speakers, we're giving this opportunity, many cases to elders, to do something that is very productive with the wonderful gift they have, which is the Cherokee language. Terrific. Well, you know, that is really inspiring because potentially this technology could be utilized by generations to come. The current young people that are using this will one day be the elderly. So, you know, Kyle, how do you see this technology potentially or this platform being evolved? What's the next step to keep it really, you know, up to date for, you know, future generations as it's evolving? Yeah, there's a lot of plans on where to take this, I can tell you honestly. From the perspective of the mobile game, building on a platform of an open-world game means that the imagination is the limit, quite honestly. So there are a lot of new characters and new levels and new adventures that are planned to further immerse the speakers in the platform. And, you know, I think that will help with reach and it will help with the amount of connection that's built to the chief's point about bridging the older generations into the younger generations over that common bond of the language and the culture. You know, that keeps those connections alive. And so we want to expand the mobile game, the Inagi, to be as accessible and as wide-reaching and immersive as it possibly can. And there are a lot of plans in the works for that. And then with the virtual classroom platform, we started with a very focused constituency within the nation of the language immersion school. And there are many other educational services and even healthcare to the chief's earlier point again, where I think there's a lot of potential for that one as well. All right. Well, terrific, gentlemen. Thank you so much for your insights. Really fantastic hearing how this platform is really making a difference in the lives of people in the Cherokee Nation. You know, of course, that were our guests, Kyle Heinz, BP Strategic Accounts at Presidio, as well as Chief Chuck Hoskin Jr., the chief of the Cherokee Nation. And that's all for today's session of the 2021 AWS Global Public Sector Partner Awards. I'm your host for the Cube Natalie Orlick. Thanks so much for watching.


You ready? You ready? First round underway. All right. Ready to go here. Round one.  She has gone five rounds in the UFC. Finished Angela Hill though in round one. So not afraid to get after it early. We'll see how Naviunas approaches things here tonight. That strike blocked by Andrage. All right. First round is underway and you better watch out for the overhand punches when you're fighting an opponent of this caliber. It really has become a patented punch of hers and something that a lot of opponents know is coming but still can't account for. Let's see how long it takes her to find her range here tonight. That once again. Well, she has certainly landed a high number of total strikes here in round two. She got the message from the corner to pick up the pace. And huge kick for the knockout victory. Wow, Joe. All I can say is wow. She might have even exceeded her own expectations here tonight. You know the kicks have been a big part of her arsenal but that was about as perfect a land as we have seen in this division. Full extension. Full power behind it. And you knew if she found an opening there, her opponent was going to be down for the count. All right. Bruce Buffer has the official decision, Bruce. Ladies and gentlemen, referee Dan Woodley, Otto's going to stop in this contest. Two minutes, 28 seconds of round number two. She was about as popular as it gets coming into this one. And her fan base only figures to grow from here after this big effort tonight. Thug grows Naviunas your winner as she enjoys a moment there with her team. She's a champion who is.