 Let me welcome you a third time to our panel called Unleashing the Blue Data Economy in which we're going to discuss the opportunities and political strategies for Europe's future when it comes to the Blue Data Economy. We have three panelists here. We're still awaiting one of them, but he is already in the hall somewhere, but we don't want you to make wait even longer. That's why we're about to start already. So let me do this intro in which I'm going to give you a little context about the panel itself. We are diving, literally diving, into the Blue Data Economy, which is kind of a new frontier to leverage maritime and satellite data for smarter decision making when it comes to the activities in the obviously ocean. Our panel experts will discuss these opportunities and actually we have a pretty holistic approach because we have someone from the science, the ocean science community, Dr. Martin Visbeck. You're going to provide insights from the perspective of ocean science community. We also do have a representative from the software and cloud industry, basically the economy, who is going to provide the industry's perspective on how the Blue Data Economy can drive innovation and economic growth on a large scale. I'm very happy to welcome Mr. Jan Wentier. And we are waiting for a third speaker that I'm going to introduce later on and member of parliament, Dieter Janowczyk, who is a political representative from the ministry of the economy in Germany and also maritime coordinator. But as he is still to arrive, I think we can already start with our intro and initial statement. In this initial statement, I'd like you to tell us what the Blue Economy is in the first place. And then secondly, of course, how the data, let's say, age is changing the Blue Economy and turning it into a Blue Data Economy. And I would like you to start, Mr. Jan Wentier. Yeah, perfect. Thank you very much. Thank you very much also for being here. What is the Blue Economy? Blue Economy, I think, is still a bit new term and maybe not everyone here on this fair is familiar to it. But if we simply put the Blue Economy in numbers, we are talking already about the seventh biggest economy in the world if you put it in a comparison to national states. And I think that shows already the direction that we are approaching there. We have for sure different kinds of clusters in the Blue Economy. There is in general cluster about science, which is very strong, which is still also very active and also a very contributing part to the Blue Economy. We have food agriculture or aquaculture production, salmon farms in Norway and Chile, which also contribute to the Blue Economy. And for sure what we all know is the energy production. Energy production, historical, being at the oil and gas domain, which also has a big, big footprint, but is a very enclosed society also in the ocean domain. And what is happening right now is something like a big industrialization of our oceans due to offshore wind. So I think everyone knows about climate change, about the ambitious goals that we are having of transforming our energy production. And we can do this only with a big portion of offshore wind. And offshore wind in Germany is already has very high ambitious political goals. So at the moment we are talking about 8 gigawatts that we have installed. Until 2045 we are planning 70 gigawatts, so almost 10 times more, and this is just Germany. In Europe we are planning 300 gigawatts in total. The whole APEC Asia Pacific region, 1,000 gigawatts. And due to modern developments in offshore floating, so there is now new technology that brings offshore turbines floating, so you don't have to build a base anymore. Totally new areas are opening. And I think on the one side it's causing an industrialization, and on the other side it's causing a huge need for data and for understanding. Just talked briefly in the beginning, we should try to avoid the mistakes that we made on land, that we conduct them in the ocean again. Because the ocean is still quite unknown I think, but if you stick with us a bit about this one as well, and therefore we simply need data. And it is, everything there is geospatial data. It's not optics, like we have it mostly here. It's acoustics, big part of the data chunks. And it's massive, and it's growing. We have different kind of developments there. We have autonomous systems which are getting more and more. We have satellite networks that bring bandwidth out there, which was still a big problem. And we have this industrialization which causes data pressure. So I think a very interesting topic for even today, but even more for the future. Thank you very much for explaining the blue economy and also how the datafication is digitizing the blue economy in the end. Now I would like to switch the perspective to our next panelist, Mr. Dr. Martin Vispec. You are representative of the ocean science community, so I'd like to have your understanding about the blue economy and also the blue data economy. Yes, thank you very much and good morning everybody. So my world in many ways is the ocean. I'm a marine scientist and oceanographer working at GIOMA, the Helmholtz Center for Ocean Research here in Kiel, Germany. And for us, understanding the ocean is what we do. Now you can only understand the ocean if you discover what's there. And that's already a data-driven science. So we go into the ocean, go into the deep ocean, the shallow ocean, everything in between. When I started my career as an oceanographer, the only way we could do it was to go in a research vessel and take the data from that. And you can imagine how hard that is and how many data you can get at that time. But over the last 20 years, we're increasingly getting more data coming in from space. There are satellite missions, exciting ones, new ones coming online of various types. But I don't have to teach you that the problem with the ocean is it's salty, it's water, so electromagnetic waves don't go inside. So you cannot explore the whole ocean from space just at the top and some properties of the whole system. So we're using acoustics a lot and increasingly robotic platforms. So for us, the big growth in data gathering and data science is robotics. And so that's one area that we're going to. But in the sciences, we are seeing more and more that data gets shared internationally. And there now is an increasing commercial interest as well using our data for a number of different areas in the blue economy. And I'm personally quite excited that we're not only seeing this interest of ocean side to support the blue economy, but in particular around digitalization, around data, because in many ways to develop a sustainable, successful, and efficient ocean economy, you need the information and data around it. And that in its own right is its own field, the blue data economy. And for us, it's very exciting to see that actors coming into the space of ocean science that are seeing the commercial applications, the use also governing the ocean about decisions, marine spatial planning, the use of the ocean, all this is database. But we are at the deep front end. We are gathering data. We're developing robots. We're developing sensors. We're trying to understand the ocean. And we are really benefiting from the digitalization of the ocean. And if you were to come to visit us at Geoma, we also have visualization systems. We have a big 120 degree dome where we can visualize in two or 3D what we're observing. So it's very exciting on the one hand. But on the other hand, the ocean remains a data poor part of our environment. That is just a fact. And I think we're looking at changing that over the next 10 years by we have rapidly figuring out new ways to use small sensors, efficient sensors, private sector, citizens, scientists all coming together to actually bring the database to the level that the industry wants it to be. And we want it to be as well. So I think it's a really exciting time to engaging in the blue economy and in the blue data economy. And the ocean scientists are extremely excited about it. Thank you very much. So basically, with the help of verification, we are enabled to not only improve the collaboration between the different stakeholders operating with the blue economy, within the blue economy, but also to communicate it as you have told before the knowledge we know about it or the status of it. Thank you very much for this perspective. Now, we're switching to our third panelist who is a political representative from the Ministry of the Economy, Mr. Dieter Janecek, member of parliament. And you are also a maritime coordinator. And this makes us also intrigued to know more about your perspective on the blue economy in the first place and then the blue data economy that is evolving right now in the second place. So thank you very much for having me here. One thing about data is getting around to these ferries here. So I didn't manage to do that very well, but maybe next time to come earlier to the panel. So regarding the blue economy, which is important for Germany, which is important globally, data is important. So and Germany as the Ministry of Economy and Climate Action, we are funding GaiaX, which is the huge project in bringing data together in different sectors. And one sector is the maritime sector. And as you said, it's not so easy to get data underwater because it's not flying around there and you can catch it like you do it in the air. So it's a lot about of MaryspaceX as one very important project. We additionally funded this area and bringing sensors, bringing shipping, bringing all the question of offshore wind energy, which is a huge project for Germany investing and the whole European Union investing billions and billions of euros in connecting the grids and pipelines and hydrogen and also the question of saving the ocean because there's a lot in the state of especially the eastern sea, which is not good. So there's so many things to do and it can't be done without data. And so we have to get data quality data, bring this together. And I'm very happy to be here today to learn a little bit about your experience, your business models to get a new economy data economy going on. And that's what we aim to do. And that's where Germany is funding. And that's where the German government is committed to work together with companies and bring a reliable market place for you that data can be achieved and bring together in quality terms and bring new business models for research, but also for the question of the economy and all the market players that we have there. Thank you very much. So it is all about creating awareness to the public, regulating the activities, but also funding important activities, be it research or economic development from the political perspective. And now having said that, thank you very much for this holistic approach on the blue data economy and also the blue economy. I'd like to ask before we dive into this political governance topic, dive into some real-world examples. So is there anyone from you who would like to illustrate how the blue data economy is truly creating value right now already? Maybe I can say a few words about it from an industrial perspective. I mean, when we want to build an offshore wind farm, we need lots of data to first determine where we want to build it. Now, so in Germany, we have the Federal Hydrographic Office who is collecting this kind of information, who builds some kind of maritime spatial planning, which is a very important word for us, because as you maybe know, the ocean is vast, but there's not as much space as we maybe think. So there's already now a high over-usage of our oceans, and especially the German areas. We don't have that much space there. So you need shipping routes. You need military practice areas. You need aquaculture areas. You have many, many competing usages, and therefore you need the first data set. But when we determine where an offshore wind farm has to be built, that's just the first place of it. So you start then a total planning phase of an offshore wind farm, which lasts up to five, six, seven years until today. We are, I think, getting there to be faster in the future. But all of this is data-driven. So the BSH goes out, is collecting the first data sets. The potential investors who want to invest in this are collecting data to get an understanding, because what Martin Fiesbeck pointed out is still quite unknown. Even if we are talking about our German areas, it's not simply you know where you're going, you know your costs immediately, so you have to have a very good understanding for de-risking your investment as well. And all of this is data-driven. So this is just the planning phase. And after the planning phase, the whole part of building the wind farm is starting, which is all data-driven. The whole 20 years, 25 years of operations, which is data-driven. So I think offshore wind is, for now, I think our best example, because it's something that's affecting all of us. And it's something that is growing at a very, very fast rate. Thank you. Do you want to add something when it comes to real-world examples? I think energy consumption and planning offshore parks is a very good example, actually. Let me give you another example that comes from a related arena, but isn't maybe not so directly to blue opportunities from the economic space, but it's extremely important. For example, in the ocean sciences, one of the topics that we're studying is, how will the ocean, as part of the climate system, change over the years to come? And what does it mean for us in the coastal communities? And one of the biggest, there's three big issues. One is warming of the ocean system, in particular extreme events. We've seen this year so many heatways in extreme events, many more than before. And where do they come from? Is it special this year? Will there be more coming into the future? So these are really forward-looking planning activities within which you're gonna embed your economic activity, because if you, let's say, have a marine culture system in a place where extreme heat waves hit you, you will have ocean diseases, your marine culture won't work, right? So you really want to know how often do they see wavecams? Will they be worse? What helps in economic foresighting and planning? But I think the other dimension I want to mention is sea level. And for all of us, sea level rise will probably be the biggest problem in particular for the blue economy, because all of the infrastructure at the end of the day ends somewhere in a port. And if sea level rises by the end of the century by half a meter for sure, and some areas by more than a meter, that's a real problem for harbor infrastructure. It's also a problem for flooding, but there you can build dykes, but for harbor infrastructure is sometimes even worse. So what we're doing in the marine sciences, we're giving you also foresight and understanding the environment within which the blue economy will operate. And I think so, that is a slightly more indirect contribution to the blue economy. But when you talk to reinsurance companies, that's the number one business for them. So there's another area of the blue data economy that is setting out the playing field and understanding the area within which you operate, and also how it will be changing in the next decade or two. Because remember, all infrastructure investments are made on a 30 year horizon. They're not made for the next two years. So they wanna know what is the forecast of the next 30 years? That's a fundamental database upon which the blue economy can successfully strive or have stranded assets. Wow, that's quite interesting. So it's not only about the blue part of the world, let's say the ocean to be planned or the sea. It's also about the edge between land and ocean and how we manage the development of this edge or this line most efficiently and that we don't just fight basically or fire fight what is happening, the problems that are occurring but also have some kind of preventive measures. This is a very interesting approach that I've never thought about. Now I would like to switch to our third panelist. Do you have from the funding perspective any examples real world use cases that are popping up right now and seem to be quite important? Well, one is definitely offshore wind. So the question, how do we get these huge amounts of windmills of converter platforms of cables, pipelines, also in this area into the sea and on this other side to get it well done and get the data that is efficient and energy is produced in several hours and different amounts of energy. So the question is how to get all these things together. Then very important in the future will be an also night now is the question of safety and security. So just right now we had after North Stream last year now we had the question of the interconnector and the Baltic Sea between Finland and Estonia. So there is crime in the seaside and so we have to make our assets sure and safety and how do we get there only by sensors, by data, by autonomous systems because we cannot do that by people in this very not life friendly area of the deep sea. Thank you. Now let's stay in this political regulation field or governance field in which we are aiming basically to coordinate this whole antelope stakeholders and I'd like to know, just to have an understanding when it comes to other continents, other countries, where can we see Germany or Europe in general when it comes to the exploration of the blue data economy? Like are we front runners? Are we in the good middle field or are we just behind? Well, I think we're not behind but we could also be better. But let's say compared to United States, for example, they also have big research institutes and do something but it's not like Europe is behind. So looking at Geomar or others, Alfred Wigner Institute and also some companies in the area. Also the military sector who is interested looking to Norway, which is the fifth biggest maritime economy in the world. Denmark with these windmills and also huge investments. They're planning for a huge energy island, for example, which might be in the area about 28 billion euros if they're gonna do this, I don't know. But so there's so much looking to the northern and the Baltic Sea investment ongoing. I would say there is a real chance that we are front runners in the future and we're starting to be front runners. That's why we invest. And one strength could be that as a Europeans we work together and have a European investment which is not only a national investment because it's something that if you look at 450 or 500 million people bringing the economy and the European Union adding UK which is also heavily investing. So the whole area, one example though, Europe has more. There is someone flying around. Europe has double size of coast than the United States has. It's a smaller continent but the coastal side is much more huge. So we are technology based. We are a research continent. We like to do climate change and not we like to do some land. Unfortunately, we like to do climate change. We like to prevent climate change. So there's a lot of opportunities. Yeah, I would say so, yeah. Thank you. Maybe I can add a perspective. I agree with everything you said. But I would say why is it that in the blue ocean data economy we're somewhat struggling a bit more. For example, in comparison with things that go around the atmosphere about weather extreme events, right? And I think there's a big difference. When you look at Germany and you look at our atmospheric data they're very interoperable because Germany and the rest of the world is part of an organization called the World Meteorological Organization. It's a UN organization where all the weather services come together and what the WMO, the World Meteorological Organization can do is a standard setting body. They can set standards and it's a data distribution and gathering hub. So when it comes to atmospheric and weather data they're always standardized. They're always internationally shared sometimes within minutes. When it comes to the ocean we don't have such an agreement internationally. There's the Intergovernmental Oceanographic Commission it's not a standard setting body. So we have the problem in the ocean that there can be standards, let's say in Europe which are different to the standards in China but they're different to the standards in the United States or in India. That is a real problem for developing a blue ocean data economy because the actors in this space are global corporations. These are not the German hydrographic service they play a role but they usually organize globally. So I think the opportunity is for us on the government side to say, well maybe we not only need weather data to run a country we also need ocean data to run a country. And if you take that approach that behooves us to do exactly what you just said. To say that means our data need to be interoperable so we can immediately share and it's really obvious that you want to share the data with Denmark and the Netherlands because that's up and downstream of your coast but it's also obvious when you develop industrial solutions let's say surveying part of the ocean you want to take that same approach to Cabo Verde as an example. But if the data standards there are different they can't deal with it, that's not exciting. So there's a real opportunity to unleash the blue data economy by thinking about who set standards, what is the interoperability and what is the sharing of data model will be like. That is an area I think that we have done some work in Europe we're not behind but it's not globally done at the same level as in other areas. It's and that I think is an area that we need to tackle a bit more on so we can really become an interoperable community. That's exactly what you guys are working on but we have to do that globally because the market is driven globally. They do the business in a country but the companies itself want to do the business in Indonesia, in the United States, in Germany, in Estonia, in some small islands in the Pacific. So if that makes this be the same approach. That makes perfectly sense and actually it means that right now where we are about to set the direction to develop into basically with the blue data economy there must be a political will to organize this interoperability and then the technical infrastructure will be built that way. If there is no political will then we will set the false foundation for upcoming developments. Maybe you can also add on that from the industrial perspective. Yeah, adding some points there was some very, very important points already raised. I think what's clear in Germany is not an offshore nation. So we just don't have this classical offshore business like Norway has for example like also the US has but we are an engineering nation and I think that's also visible in the sensor data or sensor hardware providers that we have in Germany which are very strong which are very niche oriented still. A big difference is from an economic perspective in Germany we don't have this one big player. In almost all countries like UK we have a very big player in Norway, in France, in the US there's not this one big billion dollar company who is really in there. Here it's more some kind of heterogeneous development in the economic space in this direction. This is something maybe has to change in the future a bit so there needs to be some kind of more movement to be more, let's say, blue economy and blue data economy oriented but we have a very solid base to start with. We have amazing research institutes like the G.O.M.A, like Alfred Wegner Institute, like Herion. So there's a very, very solid base that produces specialists that has gained huge knowledge, huge reputation also in the world so we really realize there's one main scientific entity in the world regarding ocean data. And this has so much opportunity and I think the politics is starting to realize it. We just heard from Diana check that the Maryspace project was funded so it's one project which has the aim from a very industrial perspective to standardize the first approaches how to exchange data, how to create something like a data space where data can be stored, where data can be exchanged and securely exchanged as well and we are coordinating this Maryspace project. It's 10 million euros project so it's quite substantial funded and what we realized there is even if it's a German funded project and only German partners are funded there's a huge interest from the industrial domain way beyond even Europe. So we just got for example Microsoft as an associate partner in this project and Nvidia just announced to be a partner in this project so that shows that not just the classical sensor space but also even the tech domain is starting to develop an interest in our oceans and I think that's a very good sign because it shows that oceans are getting relevant that also finances are coming actually into the ocean as well. So with the basis that we have if there's enough political support I think we can really play a major role in the world many more in Europe to set some kind of standards and what Martin Fuspec already mentioned what's also clear for the whole ocean industry it doesn't stop at borders we don't know real borders in this case. As soon as you're entering also as a company the ocean space you have to go worldwide because it's still some kind of a niche and your market is not extremely big but it's growing. So I think there's very very big potential for us here in Germany to really enforce some kind of standards also from the industry perspective. Thank you very much. Is there anyone who wants to add something directly to that? If I can. I'm just gonna we talked about energy systems wind farm renewables I think it's a very exciting space for sure but let's look at the flip side as well I mean that is also driven because of climate change and the mitigation efforts but you can also look at the other dimension the ocean has taken up about 30% or about 30% of the human produced CO2 that goes first into the atmosphere and 30% of that goes into the ocean so the ocean is a very significant sink but that's something that the sciences my colleagues are working on but now there's a big industry more so in the US than in Europe but it's coming up in Europe they say can we maybe make the ocean to take up 10% more, right? So as a way to remove carbon dioxide from the atmosphere and make the ocean sink grow and buy interventions, right? So what can we do? So there's an investment market in the United States about 20 billion dollars on all sorts of carbon capture issues. The ocean data science part to it for the ocean data economy will be all of a sudden well how much CO2 is in my coastal zone in Germany in the European waters and is any of the technical solution that people are proposing actually changing it. The fact of the matter is we don't have an observing system in place at the level of fidelity where a company who says I'm doing action X let's say growing seaweeds or growing grasses and I'm gonna capture so much gigatons of carbon you wanna prove that, is that actually true, right? We don't have that database or the sensors right now in the system and yet globally it's a 20 billion dollar investment industry on that, that's including land but about 15% is ocean, just ocean. So now where this interesting space that people can claim I captured and gigatons of carbon and nobody can prove it, right? So it's a huge opportunity for the ocean data community to actually have the baseline data of understanding how much carbon is in the ocean how is it changing on a year to year basis and are the interventions that we're doing actually doing what they say they do? It's a very exciting another area for the blue economy that goes from energy systems to carbon and it's gonna be a huge driver in the market in the next 10 years to come. To create that sort of common ground that we can basically behave or operate in. It's verification in many ways, right? And we need this, I think this verification surface is a common ground because otherwise we cannot communicate about achievements or not. You wanted to add something? Yeah, I think you're stressing a very important point about the carbon capture and carbon, the CCS, CCS technology and I can announce that we will open up the market here also for Germany and go into, I know that the research community is already demanding to have more possibilities to go into data that we find business models because as we know climate change is happening and we have to do something and we cannot afford that we exclude these possibilities but as you said, the problem we have at the moment is it's not really data driven so we don't really don't know how effective is carbon capture? So is it just a business model from let's say the oil and gas companies to sell more oil and gas in the future? They can say they do a little bit of carbon capture or is it a real chance in the North and Sea for example to find areas where we can talk about it and try something and find something out and we talk to Norway, they want to go into business but we also know Norway is a quinoa so it's the question of data is very important for us so we want, we will to deregulate the market but on the other side we want to have the real data so that's a business model for the future. So in that respect, data sovereignty becomes increasingly important also within the blue data economy because this is gonna be the baseline of all our activities and I'm really curious how we could succeed in organizing that, like what should be the next steps to initiate that kind of verification system but also neutral data sovereignty, yeah. I mean I would take this one because it's quite related to our work in the context of GAIA-X or in general data spaces. So the idea of GAIA-X as a European initiative is specifically also focusing on the topic of data sovereignty and this is even from the industrial perspective, not just pure from the science and also from political perspective, data sovereignty is getting more and more attention. So we really see it and again I'm coming to the example of Offshore Wind because it's quite prominent there as well. You have one very large scale company which is building an offshore wind farm but they don't do it alone. They do it in cooperation with many, many stakeholders with different kind of survey companies who are acquiring data with different kind of engineering offices around and there's a tremendous amount of data is created and this data needs to be shared, needs to be exchanged, needs to be transferred, needs to be processed, products are generated. And we're coming more and more into this discussion who's the owner of the data at which point of time this whole data value chain and it's not that easy. So there's an originator of the data, there's someone who paid for the data, there's someone who is enriching data maybe with third party data information as well. And this is something in the GAIA-X and especially in the Maryspace project we are looking very much to how to ensure data serenity, how to ensure data ownership over this whole pipeline, also how to do it not just from a legal but also from a technical perspective. Legal perspective is one thing. But good example, as soon as I'm signing a contract, today you're sending a contract, you're sending a hard drive around. Do you have any idea what's happening with a hard drive? Even if the contract is dated it should not go anywhere, it should be deleted after 14 days and all these kind of things. But there's no technological proof that it really happened. And we see this question more and more arising because there is a value in data and that's also realized by the bigger institutions step by step. So this idea of the data economy which we're even discussing is starting to play a role for future business models as well. Just one also example there. As soon as you know where an offshore wind farm is built, you know that there is a need for data in this place. So the first survey companies which was before all-time project-based, so you get a contract and you go survey, they start now to do multi-client surveys. So they go out already knowing there is a need for data in the future at this place. And I can sell it maybe not to one person or one entity, I can sell it to five, maybe even for a cheaper price but I have not just one customer anymore. And then it is getting even more important who is the owner of data at which point in time. Also, when it comes to data security, I mean, we mentioned Nord Stream. Nord Stream was something unexpected, so extremely unexpected. Everyone knows that there is a problem in this field but that really someone is pressing the button and is really blowing up something. I think that's unprecedented. And we know that we don't know enough. So it was clear for us, there is no way to have a full monitoring of these large-scale infrastructures that we are having there. But there is data around. And this data has to be gathered. This data has to be exchanged. It has to be exchanged between different kind of entities. How do I today exchange data between the military and industrial company and the science? Almost not at all. They are just simply data silos. And this is something that is heavily related to data sovereignty and the ownership of data in the whole process. Can I bring another dimension to that, which I think also relates a bit to opportunities on the governing side? I mean, I find it fascinating that a country, let's say Germany, Europe, that we are actually allowing private companies to take environmental data, let's say a maritime survey, in a nondisclosure agreement, you give me the contract, I take the data, you pay for it, you get the data exclusively, they are behind nondisclosure walls forever. I find that fascinating that the country of Germany allows that to happen, because what you're actually taking is observing the German territory, but somehow keeps the information away from the public. So can you imagine a regime which says after a time n, you can negotiate what n is, one year, five years, 10 years, but at some point, the data has to become part of the public record. Even the President of the United States, the secret recordings become part of the public record after 30 years. But environmental data in the German bite is an example done by private sectors for a private customer, stay locked away from German citizens forever. I think that's an unacceptable situation. And it can be easily fixed by having laws around that, say you must disclose environmental data after some time. No private sector company has a problem with that, because for them, after 10 years, the data of no value, but there's still a value for others, right? So it's one little example, and I can show you how that is playing out in Norway. In Norway, when you have Maori culture installations with salmon farms, as part of the license to operate a salmon farm, you have to take environmental data and share them in real time. That's part of the licensing agreement. So they pay for the data and they share the data. Why is that? Because the spreading of lice, you know, one of the diseases is a real problem for all of them. So they all agreed, if you don't share the data, we're all gonna be worse off, right? So they agreed to it, but it's the government who actually said, you cannot operate a Maori culture installation in our coastal waters without an observing system that you pay for, but you share the data. So there's governing models that open up data, that get them out of the grip of the private sector who seems to think that keeping them to themselves is a value. I don't think it's actually true, but you just provide a regulatory environment that supports good behavior. That can be negotiated. So I think there's a real opportunity for those who send these licenses to think about that and also an opportunity for us on the data side to figure out ways in which we can keep data sovereign or exclusively lose for some time and then eventually opening up. But if you don't plan that from day one, if you don't make the data in a sense behind, let's say a data archive that is not open for everybody, but after in years will be open. But if you ask the company 10 years later to submit the data, they might not have it or not find it, right? So you have to do it from day one. But there's really interesting opportunities and comes back to the point that I said before, if the coastal states agree to that running the coast depends on data, they will act differently. They haven't thought about it like that. On land, they do it. Land data get controlled and regulated much differently because they know it's so important for running the country. But in the maritime space, they haven't thought about it like that. And I think it's a huge opportunity because it's an enabler for the blue data economy. If that sharing is regulated well around the planet, it enables business models. It's not a showstopper, it's the opposite. Absolutely. On the other hand, I must submit if you regulate data accessibility a lot, it also creates business models, but maybe the kind of business models that we don't want necessarily. So it depends on how we define the value chain, basically. And that is a political decision. And for this reason, I'd like to have an understanding of your perspective on that. So how do you think we should organize data accessibility and data privacy? Because in the end, it's like a balance act. We need to master it. I think there's a high complexity in this topic, especially in the underwater domain, because you very fast get into the realm of defense in some way. So and this is something when someone on the land side would all the time investigate your pipeline and your infrastructure would get very fast suspicious what is happening there. In the ocean, it's a different story. It's way harder to figure out that someone is actually looking for infrastructure in a way. And in the end, it's data that you acquire in the ocean when we talk about multi-beam acoustics, high-resolution data. It's a very high probability that you see something in there all the time, even if you're not even looking for whatever you're looking for. So in this kind of complexity, at the moment, there is no real way to work against it. It will come. We will approach this with these topics of Gaia and data sovereignty approaches. But still, it's facing big, big kind of challenges. I mean, there's a possibility some of them is also done in a terrestrial domain to aggregate data on a specific level. That is something that you can do. But at the moment, there is no law that enforces you to do it. You can simply collect as much extremely high-resolution data as you want without some kind of political or legal enforcement to aggregate at the moment. But for sure, data aggregation, higher-level aggregation, that is at least a technological approach to solve this. But there needs to be some kind of political will also to start in some way regulating this domain. So all the time interesting when industry calls itself for regulation, we see that now in the AI domain. But here, it's some kind of a similar discussion in a bit of a way, because we see the problems that arise with this high-resolution data and the problems that could potentially arise. And Nord Stream was just the culmination of all of this. Would you like to share your perspective on that? I would say, in principle, we should share as much data as we can and open it up and do an open-data approach as we do. For example, the tourism sector, we have a knowledge graph which is open data regarding the blue economy and the question of the Model C and the Baltic Sea. For example, there is security measures. So we have to respect that. And the German maritime industry, for example, has a huge share of also defense industry. And they have their interests. We have our interests. So I think we have to find out which data is for sure and which data is maybe critical. Maybe there has to be a better debate to separate that. But I would formally agree and say, let's give so much data as we can to research, to companies, to ecologists to do something good. Because I think the most people, maybe there's always some people who like to do something bad and then we can regulate them. And as we are now in the deep sea, we always are able to regulate things. I think it's just important that we have this discussion about this kind of issue that's happening. Because if we don't discuss it now, what you just also mentioned, and don't find some starting points at least, not the perfect solution because maybe this doesn't even exist. But we at least have to find starting points to think about this also in the future because the data growth and the opportunities that we are having already today. And we are coming there. We see it here as well. The drone technology and the beginning drones were amazingly expensive. So no one could afford using this kind of drone technology. Now, everyone can put almost a laser scan sensor under a drone and can collect as many high-solution data as he or she can. And we see the same development in the ocean. Now we also see the big change into autonomous systems because we need to be less expensive out there. Usually, if you charge a ship, you have 5,200,000 euros per day just costs for the ship. So gathering data is extremely expensive. So industry starts to go into autonomous systems. They send torpedoes length two meters out and they start surveying. And this will get more and more and more. And it will get cheaper. And the amount of data will get more. And we have to think now about the problems that are coming with this kind of direction. So the task is to use this kind of momentum that is being created through the data-driven world to set the right directory. And the question is, which use cases could be applicable for that? Where do you think there is the biggest touch points between the stakeholders but also the countries to initiate that kind of collaboration, international collaboration? If you want, I can give you a frame. It's maybe a use case. I mean, I'm known in the community of advertising very much around what we call digital twins of the ocean. So we're using the engineering analog where you build a car, you do it on the computer, you design it on the computer in the digital world before you actually build a prototype and then test it out. So this approach has been more and more now also converted to the environment where you're basically saying, let's build a digital version of the ocean on the computer. You call an ocean model or data-driven model and use that digital twin of the ocean to think about marine spatial planning. How many, let's say, wind capture systems can I install? Is that a problem for fisheries? Is it a problem for safety at sea? Or for national security issues? So you're building up the ocean in the digital realm and you ask this, what if questions? What if I triple the wind insulation in this area? What will be the impact, let's say, on birds? Or on ships pathing through? And so these what if questions are the central thing for designing an environment. And I think we're in the space where we used to think of the ocean as a natural environment. And now we're switching over to maybe 2,000 years ago when we were hunters and gatherers in forests in Savanas and then we started farming. So now we're making the land do what we want. We call it farming. We're now making the ocean, the coastal ocean, to do what we want. Capture energy, get fish there, do tourism, keep reserves alive. So it's a planning effort. It's an engineering effort. We're engineering our coast to do what they want. Protection is part of engineering. So and you can do this on the computer. And the reason why that's exciting is because that is the data economy. A digital twin is a data-driven product. Either observations or models or some fusion of that. And then sharing that information. And what we're seeing is as these digital twins are developed and used, there's a huge demand for data of all types. And there's a huge demand for interoperability. And there's a huge issue about whose data am I allowed to use for what. But I think these frames will really switch around the economy. It's sort of the GIS in the dynamic version. And I think it's happening already. It's very dynamic. It's a very exciting environment. Big companies like Esri or SOA, they are here, engaged in that. And I think that will change the needle because all of a sudden now you are switching from thinking of the ocean as this blue, pristine, untouched environment to an area that you're going to engineer. I mean, you might not like it, but the fact is we're using the ocean. And if we use the ocean, we should do it properly. And to engineering to be sustainable, to engineering to be balancing protection and use in good ways to understand pollution and mitigate it. So it's an engineering approach. And the engineering tools like digital twins are the obvious tool to do. And digital twins will be a game changer for the blue data economy because they will lead endless amounts of data and sensors and zooming in capabilities. I personally think that is really where the energy will be in the next decade. The engineering part, the designing, the planning that really makes it hands on. That's what you mentioned in the beginning as well, right? Is there anything that you would like to add to that specific point? I mean, the digital twin I think is a very, very good example. There also won't be this one big digital twin that we for also from an industrial perspective for every small-scale system. If it's an offshore wind farm, it's an agriculture system. If it's some kind of a coastal engineering thing, there will be very, very dedicated digital twins, which needs a solid underlying data infrastructure. And then also some sovereignty plays their role, which kind of aggregation level of data do you need, for example, for which kind of digital twin, for which kind of application. It's not that you all the time need the highest precision one centimeter resolution for everything. So there's a big, big interlinkage between this whole topics. It really dives into some kind of idea of the blue data ecosystem in this kind of direction. So I think digital twin is a very good example of all this. Thank you. So having said that, if there's nothing to be added for the digital twin idea or this specific engineering approach that will probably make the drive within the next few years when it comes to actually creating this kind of collaboration, both digitally but also in the respect of governance, I'd like to ask you out about sustainability. Because what happened when you mentioned all the use cases, how we are using blue economy already right now, so the ocean deceive, actually what stroked my mind was the question, so how can we ensure that we use it sustain in terms of sustainability when it comes to blue data economies? How do we design an economy that is also in the directory of sustainable economic activities? What do we need to, which kind of foundation do we have to set now for that? Well, regarding Germany, we are on the path to decarbonize our energy system. And so offshore wind energy will be a huge part of that. About 30% of the overall energy that's going to be used in the future will be produced in the sea, so that's a lot. And if you look at the European plants, talk about 400 gigawatts of offshore wind in the next 20, 25 years. So that's a huge project, which means decarbonizing energy and also bringing, hopefully, stable industry price. I wouldn't say cheaper, but stable industry price is for the future for our companies so that we get a little bit more resilience regarding to the global problems we have with Russia and other areas in the worldwide craziness. We just, unfortunately, experience in many parts. So the second thing is definitely saving the oceans. So how do we get new technologies, let's say bioengineering maybe also, but let's begin with the sea grass and stuff like that and maybe carbon capture. Decarbonizing the shipping industry is also a huge task where we have just started to regulate as a European Union. So that's huge tasks. On the other side, huge chances for industry and for businesses because the regulation is crystal clear. So CO2 will be more expensive and renewable pathways will be the ones that have to be done. So and in this area, we need research. We need companies. We need clear regulation, hopefully, not only European and the future more also globally. I know that's takes some time, but there's some hope now and let's, for example, the shipping industry is not globally regulated for the first time regarding CO2. So tests started. So on this path, we move forward and using data for good, for sustainability in these market frameworks. So that would be my piece on that. So that is a dimension of how the blue data economy can use to support sustainable development and sustainable behavior and sustainability. But I think you also asked the questions about sustainability of the data economy itself. I don't quite know which side of sustainability you were asking us. So on that end, I think there's a lot of work to do. And again, I'm coming back to my example that I mentioned earlier on weather, on meteorology, the FERC, that the World Meteorological Organization, which is the weather service of all the countries, they also agree to store all the data because they feel looking back at 100 years of weather is going to be very valuable. For now, it's economically hugely valuable because that's where the AI models get trained upon. So it's even in their own data economy. So the question that we have to solve in the blue data economy is how is archiving going to happen? That's another problem. The other flip side of interoperability, if you have interoperability data, they can easily capture stored and archived because that's the sustainability on that end because the data are not only valuable at the instance that you're taking them for a particular purpose, let's say what's the temperature today, but they also become valuable when you look at trends over the last 20 years or cycles and all of that. But if the data don't get checked, stored, a quality controlled metadata stored and stored and there's no sustainability pathway for the blue data themselves, you're losing that part of the business or you're opening up to randomness. So there's an issue around sustainability and archiving and revisiting and reusing the data that I gathered today in the next decades that also should be addressed and looked at. In some communities that's done very well and others communities done terribly. And I hope that in the ocean we're gonna land on the very well side. In some parts of ocean data it's already done quite well, but in other parts it's not. And as new actors come in, it's much easier to do if the government is the only actor in space, they have regulations and so on. But if the private sector comes in, citizens come in, you have to work a bit on that. How are you gonna actually make this data enterprise sustainable? And the blue data economy is looking for that because they want to have access not only to the data from today, but also to the data from the last 30, 40 years. So that's another exciting opportunity here to think about the sustainability of the blue data economy itself. Right, right. That is definitely two dimension, if not even more dimensional. Right, we have the blue data economy in itself, but also its impact on the environment. And I'm asking this because obviously we have lots of problems when it comes to ocean and our environmental impact in other industries already. And oftentimes I wonder if we should maybe talk about the sufficiency debate and not only the efficiency debate when it comes to creating those systems because literally oftentimes our agenda is to how to make things more efficient, optimize them, but not to ask whether we need them for instance, right, when it comes to energy consumption. That might be a critical approach to the offshore parks for instance. So that's why I'm just interested how you would react on that. Yeah, I think it's a very complex topping. How to use, and we will use, we are industrializing our oceans, how to sustainably use them. And what is also very, very clear, we need as much information as we can get to be as sustainable as we can be. Sciences of extreme importance for this science has to show us with projections, with models, how the effects of our work will be on our environment because we simply don't know. We have a bit in understanding that offshore wind, in the worst case it can be rebuilt, it can be taken apart if there's a big effect, but still, we are affecting on a very, very large scale our environment in there. It can even have positive effects. So fish farms can be created around, so they can be even some kind of development of ecosystems in the offshore wind farms. So it's a multi-dimensional problem, but we need the blue data economy to predict as good as possible what kind of, maybe new problems we will create by now doing for example, the energy transition. And the energy transition is only a small part. I think we altered our systems, our ocean systems already quite a lot. And there will be more to come. We were talking about the heat waves that we are recognizing that will definitely have a global, or is already having a global impact on all different kind of systems. And it's a very, very complex topic, how to solve our energy transition, how to fight against climate change. And on the other side, how to sustainably implement the ocean in this whole approach of fighting climate change. And blue data economy itself, it has an impact. So as soon as you are gathering data from the ocean, you're doing noise emission. You are sending an acoustic signal into the ocean, which is noise pollution itself. Now this is also something where debate now slowly starts, how much noise are you allowed to emit with actually acoustic sensors as well. So it is really a multi-dimensional problem which has highest complexity. And that's why we need a very, very strong scientific component in this whole environment to understand what industrial impact we will create on our oceans. And for that, basically the digital twin worth, its ability to simulate what we're doing is the one way to go. I agree on that. And in the end, it's again a political decision. What is the impact that we want? And what is the gain that we get from it, right? That's like... But on the sufficiency side, I think what has to remember, there's a big difference between land and ocean. That is the ocean is completely data poor and land is data rich, right? So in the land, sometimes you wonder, okay, this has been measured four times, do I measure it five more times? And can I not save that effort, energy and everything that goes along with it? In the ocean, that's not so much the case. It's still under sampled heavily, but some of the sampling that we do does have an environmental impact. And that's one of the areas why I'm a little bit allergic to, let's say, private seismic investigations where the data don't get shared. So then the public comes back many years and does the same seismics with noise pollution and everything and CO2 pollution around. It's a, well, why didn't we share the data that we already have? And so then we don't have to do another cruise. It's not just the money that are cost to run the cruise, but it's a CO2 emission that goes from the ship out. It's a noise pollution. So there's other reasons what I think we should only do it once and share the data, rather than do it many a times in having the environmental impact around that. That's one of the other arguments around it that goes a little bit against what the surveying industry likes to promote, right? Exclusive nondisclosure contracts. Thank you, anyone? Yeah, I would agree because collecting data at the ocean is much more costly than collecting data just here in the room. So there will be a move to be more efficient in collecting data and having use cases. And there was always a saying that digitization is bringing productivity, but that's not true. It's only bringing productivity if the organization of the data and the quality of the data is good. So that's why we have now a debate of, which is a real debate, that collecting data and data and bringing artificial intelligence and everything is bringing so much data centers and new ones that we also have energy problem with that. But I wouldn't say that's the problem for the blue economy. It's more here for the land economy, yeah. So I'm happy to hear that there is a holistic approach when it comes to that because in some industries I have the impression that they just talk about sustainability and see the benefits of datafication and digitizing everything, how it's gonna be amazing and impact sustainability in that respect, but here there's a holistic approach that I can hear out. So that is definitely part of the discourse as far as I understood it, right? Yeah, I think the interesting thing in the ocean domain is still it's very scientific driven. So ocean itself is young. Understanding the ocean is still a young domain. I think on the terrestrial side, we are already way further. It was way more research conducted. It was way easier, yeah. And this is still, we have a huge scientific impact still on development of sensor technology, on these modeling approaches. So industry is starting to overtake and going more into these fields and to modeling and all those kind of things, but still the majority of findings which is in other domains, not the case anymore is coming still from science, yeah. And I think this is still something where maybe also a bit more holistic approach comes into play. I think there has to be a balance between what industry and what science brings in. But when you have a look at AI development, most of this stuff is even coming from companies right now, from the Googles of the world. I think still we are at a different stage right now in the ocean. And that was actually part of the closing session, I would say. I would be interested in the technological advancement that we are talking about right now that are really impacting the blue data economy. Maybe you could use them to make kind of a foresight where we are right now, which play AI, generative AI plays in all that fields. And what do you think the next big milestones will be? Well, I'm as a politician can only be a market observer, but I think definitely autonomous systems is a huge thing that's ongoing because we cannot do things with people. We need technology without people inside these dangerous areas of the ocean side. And so autonomous shipping, autonomous underwater systems, how do we get munition out of the sea? This all needs autonomous systems. And artificial intelligence will definitely be a part of that. The question is on which level are we talking about? So maybe there's a lot of movies ongoing about artificial intelligence. So sometimes now we see directions maybe we couldn't imagine some years ago, but I'm not quite sure which will be on the use case the next year is what we will see there that might be quite interesting. And so, and then the question will be, how do we organize and bring quality data together so that the Gaia X approach, the various space X approach to share data that the economy for itself, the market shares, they can do the business models. It's not the question of the state to do that, but to bring the right environment to write regulation for that. I'm going to make three forecasts. Number one, I mean, I agree everything that you said, Diana Cheikh, but let me just add to, I think a big revolution will come by more sensors being developed that are easier to use and more platforms becoming online. I think we're going into an environment in the blue economy. Imagine that every container vessel would have a science bay on board and we're taking measurement all the time. That is not a dream. I think in five, six years, we're going to start to have that big companies like Meriska thinking about that. That will be a game changer for observing the ocean. Robots, we talked about the drone equivalent on the ocean is happening. So I think that's the one that we're going to have more actors in the data space gathering blue data which will be very exciting. It's a big game changer. On the what to measure side, I see two game changers. One will be EDNA, looking at the DNA of the ocean and bits of water around the world and reconstructing from DNA gathered on certain parts of the ocean. What the ecosystem in certain parts is looking like and how it's changing. That's a very interesting way to look at sort of integrated measures of biodiversity and these sensors are becoming more abundant, more available. We have to learn with AI how to scale them down and up to an ecosystem. But I think it's one of the ways we can make progress. And the third one is the old friend. I think it's going to be also the new friend is acoustics. We're going to see a revolution acoustics by using passive acoustics just listening to the ocean sound and using AI technology to invert from listening to the sound, what is there? That is going to be, if that development is moving at the speed that it is moving, it's going to be fascinating. What soundscapes of the ocean will open us up to see all of a sudden the ocean in very interesting ways that we haven't imagined right now. This will be interesting with defense. They are using that too. But these are the kind of game changer that I'm seeing that could really open up new ways in which we can look at the ocean and feed data into the blue data economy. Thank you. I think from an industrial perspective, what is changing? We are going from prototypes, the first wind farms and the agricultural systems or whatever, they were prototypes to processes because we need to scale. So, and this scaling for sure has a financial effect as well. On the one side, it brings finances and on the other side, it needs finances. And there's some good examples for this. So, we see the autonomous systems. That's very clear. It's also very much business driven as well because we want to be faster in data acquisition. So, today a ship 100,000 euros per day. That's not something that you would have to have out there for several weeks. So, you just want to go out, one ship, 10 autonomous systems, two days done. So, that's the developments there, autonomous systems. Then what I also mentioned in the beginning already is satellite or in general network coverage which is still something in the very far offshore domain. You don't have bandwidth. You don't have internet there. And if you don't have internet, that makes things quite complicated honestly. So, it means you have to process data on board of the ship. You have to drive into the harbor. You have to send a hard drive to the next entity who is working with it. So, as soon as we get these Starlings and this bandwidth and all the other projects there, commercially available bandwidth there, it's also, it's a game changer for many parts of the industry there as well. And what's also of interest is for sure the development in the AI field. How much generative AI is now of interest we will see. But what we are working for example with NVIDIA together is physics AI. So, how can you model physical systems not based on the full understanding of the complexity of the system, but maybe based on modeling it in a more black box approach. But we did for example some first tests for multi-beam data correction. So, what you do today, you need to correct your 10 million data points that you gather with sound velocity profiles because the sound travels differently in the different kind of water levels. So, you do every six hours a measurement of sound velocity. We did now with the physics based AI a model of for every of this 10 million data points to have an individual sound velocity profile. So, this is a total new scale that you can create with the power of AI that's coming into place. We still have to understand how we can use this. I think that we are still in the beginning of this. But the whole topic of data quality control, data processing, data pre-processing, which takes still quite some time with underwater data. I think that we will very fast see big game changes in using AI for cleaning data. If it's good or bad, different story. But there will be less human interaction I think with the data and the sensors systems necessary to get good data out in the future. Wow, thank you very much for this super interesting foresight outlook to the next developments. I think we had a quite interesting discussion on how the blue economy is going to be digitized and data-based. We discussed political challenges but also challenges in introducing sustainability, the concept of sustainability in the blue data economy. We also talked about the technical infrastructure, the interoperability that it needs to be created and how it can be actually handled on, or despite the conflicts that may arise because there is some proximity to the defense sector and actually some conflict of interest when it comes to developing such an open-minded approach to data sharing about the blue economy. But I'm actually quite positive. So enthusiastic about the upcoming years because I think we are living in these times where we can really explore the ocean and ensure that it will become an integrative part of how human beings design their environment and live their lives and design their economy. Thank you very much, dear panelists. I would like to thank again, member of parliament Dieter Janowczyk, our representative of the Ocean Science Community, Mr. Dr. Martin Wiesbeck and also our industrial representative, Mr. Jan Wendt. I'm sure you'll be open for any questions if there is any from the audience. Other than that, I would close the panel right now and we are going to proceed here in actually two minutes with our next session, a thematic block in which also at 12, Mr. Jan Wendt is going to present our geodata, maritime geodata topics and presentations. So again, thank you very much and I'd be happy to host you for our upcoming presentations here. Thank you, Mr. Wendt. Thank you very much, Mr. Wendt.