 Hello and welcome to this joint open-air in USCUP webinar, Open Science and Research Results, Exploitation, Friends of Force. We'll talk about open data, open science, and research results, exploitation. And we have three excellent presenters with us today. Sai Holsinger, who is a senior strategy and policy officer at EGI Foundation. Dr. Thomas Margoni from CREIT, which is UK Copyright and Creative Economy Center at the University of Glasgow, where he's a co-director, senior lecturer in intellectual property and internet law. And he's also a director of LLMIP in the digital economy. And we'll also have Dr. Pradhromas Tawas with us, who is a policy and legal advisor, specialized in IPR at Athena Research and Innovation Center at the University of Athens. And we'll share slides with all the participants. We're also recording this webinar. And we'll share the recording in case you want to watch again, or share it with your colleagues. And we'll have presentations for about half an hour, maybe 40 minutes. And then we'll have time for questions. So please use Q&A functionality or chat to type your questions. And we'll answer them in the end. And thanks for being with us. And over to you, Sai. Well, thank you very much for the invitation to be able to discuss the EOSC, the European Open Science Cloud, and the EOSC hub project, and our digital innovation hub. So whoops, wrong one. So head to head, my name is Sai Holsinger. I work at the EGI Foundation, which is basically a non-for-profit that coordinates a federation of more than 200 data centers across 40 countries. And we are coordinating the EOSC hub project, which is kind of the first implementation of this umbrella initiative supported by the European Commission, just called the European Open Science Cloud, in order to try to attempt to reduce the fragmentation of the services that are being offered to researchers around Europe and the globe. So trying to pull together the underlying infrastructure providers, so the compute, storage, data management providers, as well as the research communities who are building value-added services on top. So it kind of makes it a little bit more clear for the researchers to access. Within sight of this initiative, we have set up a digital innovation hub, which is another initiative in order to create public and private partnerships. So my presentation is a little bit more geared towards how we're managing, let's say, our public and private partnerships, but kind of how that is connected to extracting project results, and how do we do exploitation strategies out of that. So yeah, the Digital Innovation Hub is an output of an EC-funded project, but we hope that the innovation hub that we set up will live beyond the life of the EOSCUB project and be kind of the central mechanism in which industry engages with the European Open Science Cloud moving forward. So in order to do this, you kind of have to understand, in our context, what do we consider a project result, and how does that manage. For us, it's pretty much like anything that's tangible or intangible that comes out of the project in whatever form that might be. And it doesn't always have to have specific dedicated rights or intellectual property rights included in it. But what they have to do is each be evaluated individually in terms of where does it make sense for certain things to be controlled. So what we consider project results are kind of like anything that needs to be taken forward after the project. And that can be products, services, but then it can also be data, the knowledge, or the information itself. So you have to kind of look at them on an individual basis. So kind of taking a broader step back about how innovation strategy works with this umbrella organization, you have the EOSCUB project, which is just one implementation of the overall EOSCUB strategy. And inside of this project, we're going to have a number of different results. So somebody needs to kind of look at, well, what are the key exploitable results, so the results of the project that we need to analyze to move forward. And normally those are coming from a number of different individual project results. Because let's be honest, there's going to be a number of different project results, not every single one of them are going to be key exploitable results. So you either have to aggregate them or analyze them individually. And this normally comes up with some type of categorization of the different results that you need to take forward and assign them to individuals in who can be almost like the custodian or the leader or the owner to take them forward. So some of these services are going into a central hub. So the hub portfolio, which is like the services that makes the EOSCUB kind of work. And then there will be a number of different services, not just the internal ones that make the federation a federation, but also the external services that can be requested by any researcher or research organization or industry. So the issue with this kind of complexity thing is there isn't really a one size fits all. You kind of have to try to understand what are the individual results and what are the individual paths that each of them have to take. So there's individual paths moving forward. So for us in the Digital Innovation Hub, we kind of have to understand what path these individual results are taking, which organization is going to take them forward, how will they be funded? Will they either be part of some legal structure or be supported by future projects? So they each individual have to be analyzed across a certain timeline. So what I'm going to show is basically the Digital Innovation Hub as just one example of many key exploitable results that are going to be an output of the project. So Digital Innovation Hub, this is a common term. This wasn't invented by us. This is the European Commission Initiative as their one stop shop mechanism for startups and SMEs and industries to kind of join forces with public sector institutions to stimulate innovation. So this is part of the European Commission strategy on digitizing EU industry. So here you can see in their digital single market, they've specifically outlined the EOSC as a key component of this. But they also want to be setting up a pan-European network of these Digital Innovation Hubs. So therefore, with inside of the project, we wanted to reuse some of these mechanisms. So each of the stakeholders tend to have kind of different views on IP issues. So something like the project starts and ends with different life cycles. You can then move stuff into the open source community. Or you can try to do some type of commercial development over the result. And you have to understand that each partner that's coming into the Digital Innovation Hub, they want to get something different out of it. And you have to kind of understand their intentions in terms of maybe they're at the early stage of product development and they just want to kind of do some testing. Or do they really want, they're really close to market and they want to basically move this and then commercialize it. And I'll show you in a second that we have a number of different business pilots that we've been running through the Digital Innovation Hub and each of them have different objectives. So there's a couple of different ways that industry can play a role in the European Open Science Cloud. One is obviously they can be a customer, they can just access the services that are available in the EOSC portal. They can be a provider. So they have existing services that they would like to offer to the wider research community. You can partner together. So do some type of co-development, which is normally the case that they want to, how they want to work with the Digital Innovation Hub. But then there's this like larger procurement framework that's where we're doing large scale procurement where typically large industries are more playing a role in the procurement areas. But it's kind of here where like IPR becomes very important, which is if you're co-developing something, you have to understand, well, who owns what? And then especially if they want to move to a commercialized world, you have to kind of understand this at the beginning and start to identify what are you bringing into it? What do you retain as ownership? What gets co-developed? And then the end product of that, what are everybody's responsibilities or IPR aspects to it? So we currently have a number of different services that we offer. We kind of package those into four different areas. We offer piloting and co-design. So pilots, proof of concepts, integration with our platforms. We provide the technical access to the infrastructure services. So whether it's a grid compute, HTC, high performance compute or cloud compute, and then a number of different data management services, trying to couple them together with accessing the data so they can build out value services on top. Sometimes they just need training and consultancy and how to maybe optimize their algorithms. And then especially for small to medium size enterprises or startups, they really see this value of the visibility of their individual company. And then being included in our marketing material and participating at events. And that kind of then start that networking effect where then they can partner with others moving forward. So here's just, I'm not gonna go into detail with this, but just to kind of show you that we currently have 10 business pilots that are working together with us. So the R&D department at the BBC is doing some validation of some of their video coding and compression. We have companies working with disease diagnosis, even video platforms for sports, security protecting from botnet attacks. I mean, we really are sector agnostic when it comes to this point of view. Then we have to manage all of the knowledge flow and we have different partners that we partner in different sectors. So running joint trainings and joint events or just brokering the services between them. But this requires doing some type of community building because if you wanna broker networks of people and services, then you have to kind of have them all participating with inside of your hub. So this is kind of like towards the end of what really what I wanted to talk about, which is we kind of had this onboarding procedure and whether or not this is captured as innovation management or not. But whenever you're operating in a multi-provider environment, it becomes quite complex to try to navigate how do you capture all of the information that you need? Because if you look at it like a customer comes, they want some type of service from the European Open Science Club. And they come to our digital innovation hub with their requirements or their ideas. So we have to basically identify within site of our partner network who's the most appropriate person who could take the lead on this, understand the services, their potential timelines, technical requirements, et cetera. But then since they're only the lead partner, representing a number of different providers across the ecosystem, you then have to make contact with the individual service providers, collect the different offers for who's interested for them, how much would it cost to kind of support this? Pull that all together. We're managing this right now through an Excel file. It's a Google spreadsheet. We have to have some type of selection process because if you're having multiple providers who can support them, how do you match that request, cost, availability, reliability, just the balance between the two fairness, there's all kinds of stuff that kind of goes into managing this. So let's say we select one, we have to get project offer approval that this cost is available. The partner becomes a member of our Digital Innovation Hub but we need to have some type of an agreement between the individual service provider and the Digital Innovation Hub. So because this is an internal agreement, we call this the operational level agreement in OLA which is based off of a lightweight standard called FITSM. And then the Digital Innovation Hub has that kind of end agreement with the customer. And these agreements can come in all kinds of different forms. So it can be a simple memorandum of understanding. You can have the service level agreement, whatever you call it, it's just where you're specifying what services that you're offering, what are the conditions and what are the promises that you're making. And then here is actually where you probably should start to outline, well, what are the IPR related issues around this? So it's kind of like our internal onboarding procedure or innovation management cycle that we kind of go through. So our kind of current approach is that the Digital Innovation Hub partnership doesn't necessarily mean that we're giving complete access to all of our intellectual property. So you can kind of have this approach. You leave what you come in with. You can just do this kind of collaboration that builds innovation assets like skills and social capital and contact networks, kind of the intangible stuff. But what about the jointly developed IP? This is the issue that's likely to arise. And we don't really have a single answer for this, but all we can say is that it does need to be managed on a case-by-case basis. So there's like no silver bullet that allows you to kind of pinpoint IPR, handling IPR issues with regards to innovation management. So I don't want to take the whole month of time from the webinar itself. I just want to kind of give you a brief introduction. Obviously I'm in charge of the Digital Innovation Hub, so it's me speaking, but we have a large partner network behind us offering some of these services. And that's the team behind us. But if you guys have any questions, I'm happy to take them now or if you want to shoot us an email afterwards, I'll be happy to take those. So happy to hand over to the other speakers and take your questions in the meantime. Thanks a lot, Sai. That was excellent. I think it's a very good introduction to innovation management and Digital Innovation Hub. Thanks a lot. Are there any burning questions to Sai? So raise one, let me check quickly. Maggie's asking, will anyone in the panel address licensing options for co-developed outputs? Yes, Maggie, I guess we'll address that in the next part, sir. So let's hold your question for now. And I don't see any other comments, so then thanks Sai and Thomas, sir. Over to you. Yeah, thank you very much. So I think that in order to give the audience an idea of where we're going, I'm probably taking over now some of the points that have been discussed previously, but in a very specific way, that is to say the contribution that we, I would say, Prodromos and I are developing within OpenAir focuses mostly on the aspects of IP and data and their exploitation. So I think that in the next two short presentations, what you will be hearing, it's mostly a focus analysis on these two additional aspects of what is protected, and that's mostly the focus of my presentation. So do we really have such a thing as data ownership and what does it mean for open science? And then if I can predict what Prodromos will present, maybe I can't, but I think that he will be focusing mostly on how to exploit these kind of results. So we had an overview of the complexities that can lie behind common projects and a few questions were raised on what does this mean for certain intellectual property and intellectual property rights? Now we will see what kind of IP rights can actually be at stake here with a specific focus on data, and then we will see how these rights, if they exist, when they exist, how they can be exploited. So the first question obviously at this point is can data be owned? And for how surprising, I noticed this can sound in the scientific non-legal word, the answer is normally not. And there are very good reasons why this should be the case. And I hope to briefly present some of these reasons to you. So within the traditional copyright theory, the idea is that principles, facts, data, such, they are not protected by copyright law. This is quite evident in a number of international and national agreements where they are either excluded quite explicitly. Principles, factual information cannot be protected. Or they are excluded in the sense that within the copyright realm, we used to say that only original expressions are protected. So if you take certain data and you do something with that, meaning you structured them, for example, in an original database, or by combining data, you come up with your own original expression that may be protected. But otherwise as such, copyright only protect original expressions in the literature and scientific world. So software can be protected, an article can be protected, a map can be protected. But that as such has traditionally been outside the field of copyright. This does not necessarily mean that there are no forms of legal protection whatsoever. There are. And I have listed a few here. Trade secret, contracts, data protection, there are others. But the main point that they want to make here is that these additional forms of protection tend to either focus on a very specific aspect of data or when they cover data more broadly, they are limited in terms of the legal remedies that you can get, that you can, you know, that are at your disposal as a right holder. In the case of copyright, we could call, we could talk about a more fully fledged property right in the sense that if you have copyright, you know, it comes with a kind of generous toolbox of legal remedies, if your corporate is being infringed. In these other cases, there are limits. You know, in the case of trade secrets, obviously only as long as that confidential information is kept confidential. And, you know, with limited remedies, with contracts, there are issues connected with the enforceability. In the case of data protection, only data that is considered a personal data, it's protected and the type of protection looks more towards the protection of a private life and personal autonomy. So there are differences there. And then in the European Union, we have a quite generous form of protection for data that doesn't exist almost anywhere else in the world and is perhaps you have heard of it, this why generous database right protection. A very complex name to identify a right that you know, it's why generous is in the sense that it was created in Europe because at some point it was thought that we need extra incentives for the protection and production of databases. But pay attention, the protection and production of databases, not of data itself. So even here, data is only protected under certain conditions. I don't want to enter too much into the details, but you know, there were specific questions I'd be happy to discuss them later on. I think it's important here and a matter of continuous debate in the scientific field they should that only obtained data but not created data can be protected within this special form of protection. Again, complex legal issue. The main reason here it's to avoid anti-competitive situations. I'll explain this a little bit more in the next slide. Just to give you an idea of where we are, regularly in Europe there have been proposals to create a new right protecting data producers. So not just the databases and not just the obtained data in certain databases but the production of data. We can discuss again about the reasons for this but I think that the main message here it's to show you that data as such it's not protected for how this could sound surprising to you. Otherwise we wouldn't need a data producer, right? For example, right? Again, we are talking about mostly non-personal data. Personal data is a completely different field but what we protect there, it's not really the data as such but the fact that the data identifies you as a person. So there the object of protection is the personal autonomy. And once again, we cannot really talk about ownership as we talk about ownership in the sense of private property. You cannot do whatever you want with that data. There are limits as there are limits in the way in which you can exploit your personality. So after all this huge introduction, trying to explain and convince you that data as such are not protected is this really so. And the truth is that through further research it is somehow true that whereas corporate theory says no, whereas corporate law says no, at the end of the day, because the corporate field is designed in a very complex and sometimes contradictory way, it turns out that well, in certain cases, often even if data as such is not protected, you do need an authorization in order to reuse that data. Otherwise we wouldn't need a text and data mining exception, right? This is the result of the interplay between, well, in article two and article five, blah, blah, blah. But the main reason here is that on the one hand we have a very strong harmonization of certain rights on the one hand. And on the other exceptions and limitations are very narrow. So in order to perform, for example, a huge amount of data analytics because almost every time we need to make a number of temporary and even partial copies, because those copies often need authorization, then it turns out that even if it is not protected as such, almost any instance of data reuse requires some sort of authorization because of those intermediate temporary copies that you're making. So at the end of the day, we are in a rather contradictory field where data as such is not owned, but almost all exploitations that you can think of require authorization. But I think it's quite important to understand why at the level of copyright theory in general, data is not owned and why it is important that the legal system develops in the future. Hopefully there are not really signs of this direction, but it would be important that corporate law developed in a way that would meet the goals of corporate theory. Data as such should not be object of ownership for very obvious reasons, if you ask me. By excluding the protection of factual information as such and of non-original databases, what the law is doing is to avoid anti-competitive situations where you could have a database which is the only source for, to have access to those data, imagine, I don't know, the National Institute of, you decide what that measures, that specific natural phenomenon, if it is the only institute that can do that for whatever reason, because the law says that that's the National Institute or because it's very costly, so there can be only one laboratory doing that where everyone joined in with money for whatever reason. If the results of that single way to produce data were object of a property right, it would mean that that data becomes, you know, the private property of someone and that someone could decide whether you can use it or not. So it would be actually possible to exclude yourself or the public at large from having access to very basic factual knowledge. Now, from my point of view, this should be seen with extreme suspicion by the scientific community first and foremost. This would obviously be at the odds with the open science principles, would severely impact on the possibility to verify those data and those results, to reuse them and to replicate them. So what the law is trying to do here is to say that data as such, especially when they are correspond to factual information, because they are the basic bricks of human knowledge, they should not be owned by anyone specifically, they should be held in the public domain or as close as possible. And let's be clear, main international legal systems, many other countries other than Europe are doing this, including say the United States and it's not that they are at the bottom of the innovation or scientific fields. So what the law is trying to do here is to say, the basic bricks of knowledge should not be, should be available to everyone, everyone should have access to that. At the same time, I'm offering you sometimes small incentives in the form of partial property rights to be able to exploit that knowledge because sometimes it's costly to collect it. So some sort of reward should be present. However, it's very important in my opinion to understand the reason why the law says that data as such should not be owned. It would be like to own some parts of the common knowledge that we need to develop in order to develop future knowledge. So this would be anti-competitive, would severely limit the replicability of the results and would also have negative impacts on the scientific freedoms. So that's the goal that the law is trying to achieve through this prohibition. Well, and then we have a couple of examples that I think are useful to show how, you know, on the one hand, the legal system tries to implement this theory, but at the same time, how the legal system shows a failure in doing that properly. And a great example right now, it's the tax and data mining exception contained in the new copyright directive. And, you know, on the one hand, if you remember the debate, we had this, you know, these two positions, one saying, well, we need a tax and data mining exception because we need to perform these activities. And on the other hand, there was those people saying, well, you know, we don't need one because extracting data, it's not a corporate relevant activity. So the right to read is the right to mine. And it's true, except that you need to legal certainty if you want to do things because otherwise you're creating certain costs, costs connected with the legal uncertainty, with the risk management. And in the end of the day, we came up with the tax and data mining exception. One that is however extremely, extremely limited. So here I have a few examples for you. I don't go through them in detail, but again, I'm happy to discuss them. Should there be any questions? But we see how limited it is in terms of scope, in terms of beneficiaries, in terms of relationship to contracts or to technology. Once again, keep in mind that all this does not happen in many other jurisdictions from the state to Japan to Singapore. There are broader exceptions or standards that allow people to perform most of these activities specifically because data, you know, the basic bricks of knowledge are not object of private property protection, but the creative reuses they may be. So the balance is struck in that way, something that we have been failing so far to do in the year. Obviously this has a lot of impacts for the open science principles and their usability of data. They are quite complex. So, sorry, I'm trying to put this a bit more in focus. OpenAir has created a number of guides to help you navigate this complex legal scenario. I know that the slides will be available after the talk, so you will be able to, you know, click the links and hopefully you find this guide helpful. So we're always interested in receiving your feedback because we want to keep the guides up to date and develop new guides and understand where the needs of the scientific community really are in terms of protection of results with a specific view, in this case, on data protection and data reuse. So this is my presentation. I hope it was concise and clear enough. And if there are any questions, again, I'm happy to take them now. We're at the end of the seminar. Thank you very much. Thanks a lot, Thomas. So maybe let's hear Prodromos and then we'll take all the questions and thanks a lot, Maggie, for your questions and Chrome. So we'll answer them in the end. Prodromos, sir. Yes. Yes, we can hear you. Let me open your slides. How do you see them? Do you see them? Yes, perfect. So I will just move on from what Thomas and Saif have talked about before and try to be a bit more, to give you a bit more, let's say, a micro view of the whole situation and talk about how, what it means to actually combine IPR and Exploitation particularly, so if it goes to the next slide. So the first thing which is quite important to raise in this situation is to explain that intellectual property rights are not merely confined in the realms of copyright. So especially when we talk about open science, we seem to be focusing mostly on copyright, forgetting that in the research results and in publications as well, there are other types of intellectual property rights as well. And I'm mentioning that because when we talk to people doing open science, particularly librarians or information officers, they seem to be particularly focused on copyrights, whereas when we talk to technology transfer offices or when we talk up to other elements of recessed performance organization infrastructures or recessed funding organizations, they are equally even more focused on other types of intellectual property, particularly trademarks, trade secrets and patents. Trade secrets and patents as we will see later are particularly relevant because though they are a different form of subject matter, they have a tremendous impact on how copyright is managed and the other way around how copyright is managed may affect trade secrets and patents. For instance, the existence of trade secrets means that you cannot release the consent, at least as long as it remains as a trade secret or at least as long as something remains under confidentiality agreement. Similarly, before any patent application is being made, you should not perform any disclosure. Therefore, any type of publication, in respect of whether it is open or closed, it is going to have an effect on the patent. But what's important here is to actually see not so much whether you're going to see the two, so whether you're going to have a patent strategy or an open sign strategy, but rather to try to see those two as being complementary and as things that happen in different time. So the primary objective of this very short talk today is to just sensitize you into the fact that these two are not mutually exclusive, but they are rather part of a broader strategy which has to do with the timing section of the relevant intellectual property-wise tools. For the next slide. The first thing to start any kind of strategy about intellectual property-wise exploitation and value production, whether it's going to involve open science and open access or not, has to do with how we identify two different things, orderly versus license, and very frequently we conflate those two aspects of the exploitation life cycle or the divulgation or dissemination life cycle, but they're quite the same. I think your sound is fading, pro. So maybe repeat again the last sentence. Sorry, I still can't hear you. Can you hear me again? Sorry, I lost you. Yeah, now it's better. So in terms of the consortium agreements, if this project, this research is part of the consortium, we need to see how the rights allocation takes place. And this is something quite important. What I would like to highlight is that people use the way... Sorry, I lost you again. When you started saying people, apologies for that. So I think we lost pro. And while he connects again, maybe we can take some of the questions that we already have. So maybe we'll start a question with a question for Sai, because he'll need to leave at 3 o'clock Central European time. And the question was about digital innovation hub. Was there any usage of digital innovation hub for research purposes? So is the question basically just to understand the question for research purposes, I would say that digital innovation as a concept is to bridge the gap between public and private partnerships. And they don't always need to be, and typically at least in our experience, aren't always just to move to commercialization. So a lot of it is just pure research and pure joint collaboration to try to work together, because industry is very interested in, I think one of the key assets of research and academia, which is knowledge. So a lot of the times they just want to run joint collaborations. We're partnering with companies that want to do, they have training available training. So what we would like to partner with companies who are working in, for an example, artificial intelligence and machine learning and things like that. So there's a lot that we can do in just pure research and education in general, rather than always moving into some type of, let's say commercial relationship as an end result of our partnership. So, but I hope I understood the question. Yeah, thanks a lot. There is also a question from Chrome, is there any chance we can file patents freely? If yes, please share some resources. I guess the answer is no, please correct me if I'm wrong. You can't file patents freely. No, but usually universities have funds for that. I don't think it's as a general principle, a good idea. In fact, data shows that university lose a lot of money by pursuing this patenting option. So I don't think you should be doing it, but if you want to do it, because universities are often assessed among other things on how much innovation they produced and they think that, you know, there is this assumption that innovation equal patents, which is empirically not proven. Usually there are funding available. So for the researcher, it would be cost less. Also, most likely the patent belongs to the university and not to the researcher. Even though here one would need to see exactly what is the contract for relationship in terms of employment of the researcher at the university. Thanks a lot Thomas. And then we have five questions in Q&A. So you can see them when you open it. And I guess they all for Thomas. So I guess we'll start with the second question from Maggie. When you say data cannot be copyrighted, does it apply equally to raw data, measured values, and processed and not quality control data, whereas later has undergone substantial processing requiring human expert input? Yeah. I know that saying that data that you research or don't own what you think it's your data usually raises a lot of concern and disbelief. And I also understand the modus for that, right? I mean, it requires a lot of work usually to collect your data. There are a number of things to be considered. This is a new thing, you know, the relevance of data in the scientific environment in the dimension that we know it today wasn't really out there only a few years ago. Combine this with the fact that copyright, you know, it's as law in general reacts with a few decades of delay to social and economic change. Sorry, guys. A profuse apologies. I keep losing my connection. I don't know whether we should try it or I shouldn't. I should let Thomas or Sy to actually go on with questions. Whatever you think it's possible. Yeah. That's what we already started doing pro. So maybe let's let's finish questions and then let's see how your connection works. We can still have your presentation or we just record it separately and we'll share a recording link with everyone afterwards. Apologies for that. So over to you Thomas again. Sorry. Yeah, no, just to finish quickly that therefore, you know, that there is there will always be a mismatch between, you know, these two approaches. But what I think it's important to understand is that the reason why that is and should not be owned. It's because there is a public policy element there connected very intimately with the goals of open science verifiability, replicability. All these issues are at the core of not just open science as science as such. Right. And the more we accept this idea because property means that you can exclude others from reusing that data. And that's the reason why I don't know, you know, 60% of hard sciences results are not reproducible. Right. Because there is this idea that the data is yours and it's not even if you made it, you are probably, you know, paid out of public funding. So you shouldn't be yours in the first place. Actually, there is quite good empirical evidence that shows that academics in general should not own copyright on what they publish. Imagine that. I mean, I think that a lot of people would be quite concerned. But these are the public policy reasons why the law tries, tried maybe more successfully in the past, but certainly from my point of view, you know, these are the goals you should achieve. Now specifically, what does it mean for your question, raw data? The more the data is raw, so the less intervention there is, the lighter the result is what I said, no protection. Right. We're talking about corporate and database rights. If you collect those data and you structure them in what the law defines a database and you can show that there has been substantial investment in obtaining very fine presenting, but not in creating. And that's, you know, opens another cans of warm. Then you could obtain a swipe generous database, right? That doesn't collect, protect the single day to, but somehow extends to the protection of data in the EU. There are other tools, you know, trade secrets, then you can protect your data. Obviously you cannot make it public. So this is in stark contrast with open science. And then other situations, but, you know, let's see now if we go back to prose presentation or we keep going with the questions. I think that, you know, we should check this now. I think it's better to keep going with the questions, sir. Okay. So you really, you know, it's almost a case by case analysis, what you have to identify and please have a look at the, the guides and the article cited that's, you know, we tried to clarify this there. It's quite complex. So, you know, but you should really verify whether, you know, your data is structured in a, in a methodological or systematic database and whether you can show substantial investment. If this is the case, then you can, you can probably obtain a SWI generous database right on that. Well, actually not you, but the, the entity that supports the financial risks, so most likely your university. Thanks Thomas. Then next question from Maggie. Also, even if data is owned by no one or by societal institutions in brackets, is it compatible with legislation to require that regional data producers are acknowledged when the data are used, for example, like CC by license? Well, you know, the law is what I just described, right? So that it's as such is not protected. This is the law. I'm not aware of other laws that say differently, but certainly in certain legal system that may exist. I can tell you for sure that in Europe, only original expressions are protected. Data such as not, but this is the core of the most relevant international agreement. So there is a good chance that outside Europe is also this the case and even more so, as I said, you know, in the US to give you an example, you know, a country that has a very private scientific community. All the data produced by federal agencies is in the public domain fair and square, right? So your question is not really about legislation is about what happens if I apply corporate license such as CC by to data. Well, again, if you look at the guides, we are telling you that because data is not owned, you should not apply a CC by you should apply a CC zero. That's the most correct. That's a contractual wrap to apply to data. Now, if you nonetheless decide to apply CC by, well, it depends on, you know, whether the data is protected or not, because we said it in certain cases, data may be protected by, you know, this white generous database, right? It depends whether there is a database, whether the database is original, or whether, you know, there are these other forms of protection, et cetera, et cetera, et cetera. So if the data is protected as a database, then the by element of CC by applies. If the data, it's not protected by anything such as metadata metadata, 80% of the time is 1999. It's not protected because it's factual information. So to apply a CC by, you're giving a message to the community that is in fact more restrictive than what the law says, because instead of being freely reusable, you require authorization, sorry, my bad, you require the recognition of authorship, right? I understand that there may be situations where you want this, you want to be recognized as, you know, the person or the entity that is making available for free all this data, but at least recognize my effort. It's reasonable. I don't think it's the best way, but there are certainly situations where this is a reasonable approach. So do it if you really think, but don't do it as a default. Keep in mind that because what we said about data, the best thing for open science is that it is absolutely freely reusable. If there is a specific case for which you want attribution, use CC by, but don't do that as a default. And also be aware that if that as such is not protected and you use a CC by the creative commons as any other open license and freedom of person software license say they tell you that you only need the license if the underlying thing is protected. If it is not protected, you don't need the license. Therefore, the license clauses are not enforceable. So people may comply because they think they have to, but the truth is that they don't. Under this point of view, I think that the best example I've seen so far is what Europeana does with their metadata. They apply CC zero and they ask kindly the community to recognize the fact that they come from, from them because that helps them. And then again, you know, don't attribute to the law more, you know, higher way than what it deserves. There are other rules that we live by every day that are not strictly speaking law, but are based on say scientific norms, you know, when you cite people, which for us are really important and we comply by them, but they're not strictly speaking present, you know, in a specific piece of law. So again, you know, the situation is more complex, but also offers many more opportunities than one could think. Again, have a look at the guides because we really tried very hard to cover all these aspects in the guides and let us know if something is unclear or something is missing because it's very important for us to have feedback in order to improve them. Thanks, Thomas. Then we have a question from Yad Vankam. Researchers are usually afraid to share the data before articles are published, which are important for their career. Data isn't. They believe that by sharing the data, someone can use their data before they use it and publish as a part of an article, how to solve this and what are good arguments to use. Again, you know, the reason why we found data, and you know, I'm a researcher myself, you know, as long as you acknowledge law as a scientific research field, but I understand your point, but this is some to some extent, you know, an individualistic point. The reason why we found science is not really to support your career. It's to advance science. So if another scientist, you know, it's better than you at interpreting your data, you know, that's a quite compelling case. But things are not black and white, you know, it's not that you either disclose your data or you don't full stop. There may be situation and usually in the field of, you know, making your data available. There are intermediate solutions such as, you know, you have to completely disclose your data within, I don't know, one year from the end of the project. So some sort of embargo for data or sometimes, you know, you have to deposit your data in a public but closed database only for, you know, reviewers to be able to ensure that, you know, whatever you are publishing, it's supported by good data and the data becomes public only later on. So there are a number, I think that science probably is doing something in this sense. There are ways to do this. There are ways to ensure that you can fully the benefits of the data that you have collected, but at the same time, you don't create, you know, you don't close down your data for the next five or 10 years. Probably this is an area where better guidelines would need to be developed both by us and by funding bodies. But I think that, you know, the solution you're looking for is in this area. So the data will have to be openly available. It doesn't mean that you have to make it openly available, you know, the second next when you are done, but it cannot be unduly kept closed because by keeping it closed, you are, you know, not offering a good service to science in the first place. Thanks. The next question from Yadranka. What was open access to standards? Sorry, what's the question? What was open access to standards? I don't know. Maybe Yadranka, you could clarify what exactly do you want based standards like ISO standards, or maybe while you're clarifying with another question from Maggie. Copyright and ownership not only for a researcher, but for an organization like a university. So that was a question mark. In Sweden, it is said that data produced by academic researchers in framework of their normal employment is owned by the university. Yeah, if they say that they are wrong, and they mean databases, not data. Okay. So what you can obtain the property over it's the data, the soy generous database right, which in certain cases extends to data. Now, why they why university may want to say that, well, you may want to say that for the very same reason I just told you if you take the database directive, it implies then the employer that the employer can can be considered the right holder. And that that's normally the case. We said in the case of this way, generous database, right, the right holder is the entity that bears the financial risk. Okay. So that's normal. But it's more of a terminological issue. They say that they don't really mean that they mean that they need to be generous database right. So that are right that only protects in certain cases, certain types of databases. Which, however, also extends to a certain level to certain data. So universities saying this should improve their policies. I also know that the this this is becoming something quite common and, you know, I every time I you know the issue emerges because there is this idea that well you know we transfer it to the university and the university because it's a university that supports open science and ensures that this data it's made public available for reproducibility and I think this is fantastic it's just you know don't use the wrong wording I mean if your goal is open science why you're talking about ownership when there is no ownership in the first place now this may mostly be caused by you know a lack of familiarity with the specific technical terminology rather than with you know a different view maybe their view it's it's absolutely right often it is but it is problematic to use the unprecise wording especially in the legal field it helps to create further confusion further uncertainty so it's really important to talk about the right terms even if they are quite complex thanks Thomas and then maybe we can go back to the first question that Maggie put will anyone in the panel address licensing options for co-developed outputs it's Rennison you would like to to add about that it's just me yeah yeah I think I'm gonna take this question no but I don't think it is a licensing issue in the sense that as prodromos was started to explain in his in his slides the first step is to clarify ownership so what are we talking about are we talking about you know let's say an article so copyright you know an article written by a few people or a piece of software co-coded by a few people from the same institution or from from different institutions participating to the same consortium a database or unprotected data so that's the first step verify what you know you'll the object in front of you and then categorize it properly from a legal point of view what is it is copyright so I generous database right both corporate and slide generous database right not protected trade secret patent trademark once you have the lead you know the proper definition of the object and of its legal categorization then you can move to the next step which is exploitation so you choose a license that does what you want to do hopefully this is a CC zero CC by your GPL for example right now who can or who has to approve that license well it depends who owns the rights if for whatever reason all the rights are owned by one person then you know it's enough that that person applies the license if there are two co-authors then both usually have to agree unless there is a specific agreement for bigger consortia this is when the consortium agreement is really important that has to clarify at the very beginning what is the that it is protected what it is not protected and how it should be exploited but once you know that these are two different steps a clarify ownership b once ownership is clarified apply a CC by I mean look at Wikipedia it uses CC by and it is co-authored by I don't know a million people right so you can use the same license regardless of how many license source there are as long as you know who has to apply that license and this is something that the larger the project the more important it is that it is addressed at the very beginning to usually the consortium agreement and also Maggie is adding by co-developed she was referring to academia and corporate collaborations in the case of private public private partnerships once again you know you have to clarify it usually here academia it's at the disadvantage in the sense that when a private entity is interested in co-developing something well because IP it's one of the most valuable currencies usually they have dedicated offices with you know corporate lawyers who will present you very well drafted agreements that determine you know of future ownership and future exploitation obviously it could be reasonable that you know those departments working from for that private entity will have you know particular attention for the interest of that entity not for you or not their customer so at this point again it's very important that universities develop strong guidelines to to do this I think this is probably an area where where we would need to develop further guidelines you know how you know how should a consortium agreement IP policy you know for the support of open science look like problem is that is very difficult to develop standardized tools there but I think this is an area that should be further developed thanks a lot I don't see any other questions and we went a bit over time sorry about that and we lost prodromos but it's a network problem so it's it's beyond our power to solve it for now but we'll make sure that we record his talk separately and we'll share recording with you so because we need to do that probably we'll share slides and recording early next week if you don't mind that thanks again for joining us and you'll have contact details of Cy and Thomas and prodromos on the slides if you want to ask them some more questions later on thanks again Thomas and also thanks to Cy who had to leave to catch his flight have a nice evening and have a nice weekend and we promise it will continue this topic of open science and research results exploitation in the coming year so expect some more joint webinars open area newest hub for joint webinars thank you bye