Thank you for my interaction Hello everyone. Thank you for for coming So we're gonna want to talk about business and we're gonna talk about location intelligence First I wanted to ask the audience. Can you raise your hands if your background is more business than technical? Okay, given is the business track, but we wanted to check a little bit of the audience Cool, so let's talk a little bit about ourselves. My name is Miguel Angel. I'm the city of Geoblink. My background is technical I'm a software engineer and most of my career. I've been a backend engineer working with different technologies and then moved on to the technical management track Yeah, so Hello everyone, I'm Rafa Polido. I'm leading product at Geoblink My background is also technical. So I did computer science a long time ago, but I got into product management early on So what I do is I What I know is about building products or the right products for the market that also Makes business value for the company So what do we do at Geoblink? Geoblink is a location intelligence startup our product helps retailers Real estate companies and also FMCG companies to make any decision that has to do with location Much better and much faster So how do we do that? Well, we start from from the problem So we try to understand what are the problems within those industries and then we look at location intelligence so data and also technology to provide a specific business answers that solve those problems and All of these is presented into our product, which is a cloud-based SAS product and that is easy to use and it's very powerful So any company can take advantage of location intelligence Thank you. So in this talk we wanted to provide a little bit of background about location intelligence This is a term that is still kind of recent some people have been talking about it for a few years But it's still definitely not mainstream So in this talk we're gonna talk a little bit about the history of location intelligence where it actually means How companies can use it and then the second half of the of the talk We're gonna go more into practice details about challenges that we have found building this This platform location intelligence platform in the tech side and on the product side So in order to Talk about where location intelligence comes from about the history and about what it actually means We're gonna cover three concepts that came before location intelligence and actually shaped it So these three concepts are They are mainstream and all of you are gonna know about it And the first one is business intelligence. Okay, so I wanted to talk a little bit about the origins of business Intelligence because it's relevant to this talk and It all comes from the 19th century This guy called Richard Miller growed something called encyclopedia of business anecdotes And in there it talks about this bunker that always had an edge Against the competitors because he always managed to get information faster and he talks about he having a train of business intelligence and he always knew first Compared to anyone else when the wars ended or what the king wanted and he used that in his own benefit Later in the 20th century the fathers of business intelligence these two fine gentlemen Hans-Peter working at IBM and a Howard Dresner in the 80s They provided the first definition of business intelligence that is more or less how we know today And it is concepts and methods to improve business decision-making by using fact-based Support systems and I want to stress three parts of this sentence the business decision-making Fact-based what would be equivalent to actually using data and Objective data and systems or computers. So In these days business intelligence is very common and pretty much every company out there uses some way of business intelligence Big corporations have huge platforms with tons of teams. It's even a small companies They have some way of business intelligence and is it's all about using the data to understand The different variables that impact your your business and what to do about that. So With our business intelligence tools you can retrieve and share data and we saw the data you can identify business opportunities gain insights You can generate reports about key metrics to actually understand the the things that are important for your business It helps improve the decision-making process based in that on Objective data and then you can generate analysis that are real-time to know right here right now Which is actually important and what should I worry about my business? So this is funny and good, but these VI platforms They don't actually take into account location location data or Special data is very different from the other data that your business generates It's not just numbers. It's actually tied to coordinates and also in order to visualize it You cannot use standard VI tools You need to use maps and you need to use special visualization. So Let's move on to the next topic. I wanted to cover and this is GIS or geographic information systems. These are systems that are basically related to maps So I'm also going to talk a little bit about the story because it's actually interesting the first person that Is considered to have used GIS in some way is this guy called Jonas know and not the Jonas know you are thinking about but another Jonas know and At the time 19th century there was a big Spread of disease of cholera in London in the Soho neighborhood in London And everyone thought that it was being transmitted by the air and they didn't know how to contain it So what this guy did is he used a map and he put the different points or where people dying were living and identified a pattern It turns out that people dying were living around a specific well He figured out that it was actually being transmitted by water and by cleaning the well they they cleared the disease and Moving on in the 60s. There is this Canadian called Royer Thomas Lund he had to do an inventory of land in Canada and You know if you have to do inventory of land in a country like Canada that is pretty large You don't want to do that manually. So He actually came up with this whole GIS Concepts he was the first person that actually figured out how to use computers to input data linked to coordinates what we call geolocated data Also stored this position data and then query and put it in a map using graphics something very advanced for the time During the last few decades or years. There's been a huge yes explosion Now we have cheaper and faster computers Computation in with GIS are pretty expensive because you have to use polygons. You have to do intersections You have to do a lot of mathematics. So it's computationally experienced. So it helps and now computers are cheaper and faster You also have new technologies both hardware and software that been developed to manipulate the special data And most importantly now there's a lot of data is very rich and it's everywhere. We have data coming from satellites We have Everyone here and all around the world using GPS. You have the internet with smartphones and internet of things And also you have this concept of public data smart cities where governments and institutions are making all sort of Data related to the resources of a city public so that private companies individuals or other public organizations Can use that data to actually Make cities more efficient and we're talking about the word resources in Transportation education all sort of things and all this data is just there for people to use it and so This all has brought amazing products that Everyone is using this day any person can buy a smartphone and look something about Google Maps or order a taxi But is it's kind of crazy to think that only a decade ago These systems could only be Implemented and used by specialists and now anyone can can actually do it but when you actually understand the concepts of routing and maps and Zooming in those are things that are embedded in our day-to-day slice But only a decade ago. It was actually pretty complex and it was in common for people to understand these concepts The last concept I wanted to talk about is artificial intelligence And I'm not going to cover the history of AI in depth because Everyone in the room is probably familiar where it comes from But I wanted to cover two specific concepts of AI the first one is machine learning although machine learning per se is not actually Artificial intelligence, but is one of the building blocks to build some of the key functionalities And I'm talking about forecasting and and catheter organization The other concept I wanted to talk about is deep learning or neural networks and that is something that you need to to be able to use in order to implement something called computer vision and This is actually very relevant to this talk computer vision is the technology used in self-driving cars Basically to understand what's in an image computer vision is just about a computer being able to identify What is represented in an image so when when a Tesla is saying, okay? This is the road and that is a person That is a traffic light. He's using deep learning inside the car in order to identify all these different objects It's also being used. I've seen another talk today about a computer vision identifying satellite images And that way you can see the buildings in a city Not only that also what kind of building if it is a warehouse if it's offices and what this means is that now we can automatically understand the data in images and Put a coordinates in that data because you know who's taking the position of who's taking the picture So you will know the position of what appears in the picture. So now we can massively Gather information that is geolocated With that introduction Let's talk about location intelligence So so location intelligence analytics is all about location And it's kind of closing the gap between GIS systems that are all about cartography and location and maps then business intelligence which is all about grouping the data and getting the insights and There's also the artificial intelligence. So you can actually make sense of the data and get very powerful insights So reality is the location intelligence is so powerful providing business solutions to specific problems when there is a location involved So when I talk about better solutions, I talk about Data data enrichment. So one thing that is quite powerful is when you take your business data So imagine you're a retailer and you have your network and you know your customers because you have a loyalty program and you know the performance and and all the details about the revenue of each one of the stores and then you cross this With information about the location all these different data points Advanced analytics competitors attractors Consumer spending all the population flows when you mix all those things you have many different data points that are very powerful But how do you go from data to actual insights? Well the the enrichment and the data is just the foundation. It's just the the base now You apply artificial intelligence techniques to actually get actual insights from the data And then you present these in a very map centric And so you can do the analysis very much focused on location as a business So I just said that we went from data to insights. How do you go from insights to business value? Well location intelligence is working in three different directions. So let's let's deep dive Descriptive analytics, so it's answering the question. What has happened? So this is very powerful when summarizing a situation So if you have data around a location the people visit an area or the disposable income for the specific area You can and you cross that with your business information with your stores. You can actually It can actually help very much to identify problems and potential solutions So if your stores are split across the country and your target Demographic is not close to some of your stores. Maybe there is a problem there and you need to optimize your network So I'm gonna give you a couple of examples One is just by visualizing the distribution of your customers Imagine that you are an e-commerce and you know where to deliver your products because you have many clients And you can look at that and even enrich those data sets with for example information about the disposable income Another example is the spending data If you're able to identify areas with high spending in the category that is very relevant to you That's telling you a lot of information. But again, this is just describing the situation It's not telling you anything else and what's there We take us to the to the next area, which is predictive analytics Here is slightly different because it's answering the question. What could happen in the future? So it's actually anticipating future scenarios for your business So, you know, what's what's happened in the past and now what could happen in the future? So obviously there is much higher business value because it's not about telling what happened It's also doing some simulations in the future and seeing how those things will go for your business So a couple of examples, I think a very popular one is the sales forecast So once I know everything about your business and we know everything about the location And I have the history of your sales performance Then we can anticipate in the future if you open a store, how much money you're gonna make And that's actually quite accurate. So it's it's kind of telling you future scenarios so you can make better decisions and And this is a closing simulation is kind of the other way around Imagine that you went through an acquisition or a merge and then, you know, you want to reduce the retail footprint that you have So, you know, you don't know exactly how so you can do prediction and see if I close this store How many customers I'm gonna keep how many of them are gonna move to the to the online channel, for example And finally another area that location intelligence is helping quite a lot businesses is the prescriptive analytics So that's what should we do as a business? It's very interesting because that's providing advice on on a specific outcome So if I know your your end goals as a business what you want to do and I know all the information I can proactively suggest do that change that open their clothes there It's just more proactively telling what's the solution for your business Obviously, this is the highest business value Because it's like you don't even need to think almost I'll tell you a couple of use cases so redesign of the network if I know the business goals I would you're trying to achieve as a business using a location intelligence platform We can redesign your strategy to achieve those goals or For example on the marketing side of things if the location intelligence platform knows that you're targeting a very concrete Target for for a new product for example Then it's going to be able to identify those and look at the flows and how what are the changes on their behaviors to actually? Target marketing campaigns much better But let me give you a couple of more specific examples. What do you see in this image? This looks like high street, right like a city center. There's many people there, but do we know how many people there? Difficult right do we know their profiles? Are they visiting are they passing by maybe they live there? So for businesses, they have these questions, you know, there are many questions from geo marketing to offline retail To even like residential real estate. There's so many questions that are very difficult to answer So how do they do this? Well, they actually it might be some Funny, but they actually send people to count and to look at people and figure out the profile if it's rich or not And you know, that's if it doesn't really scale and it's not really You know helpful for for the business So here is how location intelligence actually looks at the same problem So this this is coming from our database. Those are signals GPS signals from the mobile devices There is no map behind or anything. Those are just the these signals So if I link these to the The business value that location intelligence can provide and then you have on the descriptive side I can tell you for a specific period of time for a specific day How many people were in a specific location? Where did they come from? Where did they go after that? If I go more into the predictive and then because I see the patterns I can predict how many people will visit an area and I can even take into account things like events and all other different Location scenarios that can impact that prediction and Finally in terms of the prescriptive side if I know your business goal and I have all the information about your business I know where the customers are, you know, I just can't tell you exactly what what to do And it is very very interesting because You will see that in north Spain is kind of some popular road around there That's coming Santiago and you can see that from data Great another example if you have kids, maybe you might be familiar with this product and this is from a large corporation This product is targeting kids What are the questions that those businesses the consumer goods business have when bringing this product into the market? Well, first is where are the kids right where are the kids and where are the parents and where are the families? Where are the schools where are the parks because if I have to bring my product to specific point of sales And I want to optimize and maximize my performance and need to know those things, but they don't it is very difficult for them to know This is how location intelligence actually solve the same problem. So what you're seeing here, and this is coming from from our app This is the the map of Madrid all those dots are point of sales and the different colors are the Potential for that product depending on on the point of sale. So we can analyze around each one of the stores If there is a school if there is you know, like a park maybe and assign a score So we can describe, you know your network and and tell you Using a scoring. What are the points with the most potential? If we look at the prediction then Looking at your historical data. I can even tell you how much is going to be needed for each one of those and on the prescriptive side What I can do is I can Identify where your customers are and define the strategy and then just design how your sales force have to work and just Without you telling me anything apart from the objectives Great so Those were like a couple of use cases, but there are plenty of them across many industries That's just not retail and consumer goods is real estate is banking and there is government There is pretty much everywhere And the point here is that more than 80% of the business data Has a location component and that impacts the business decision. So if you think for a moment, you have stores you have Clients located somewhere you have deliveries and you have vehicles you have competitors So location intelligence is very relevant for most of the business and data has to do a lot with location But not everything is that Cool and that is it. So let's talk about the challenges behind Thank you. Yeah, so here we wanted to make it a little bit more practical and talk about some challenges The first one I'm going to start is obviously the data that is where everything starts and it's probably the harder So if you try to bring this into an analytical level where we're saying here is what trying to represent the real world The real world is very complex and has many different aspects so probably the first thing that you should do is you should divide the real world into data layers and That would represent the different layers in in the real world, right? And the thing is these layers they would bring data in different formats from different sources That represent different things that need to be treated in different way So how do you actually process that from an engineering point of view? Well, that's that's a challenge and that's probably means that you're gonna have to use a Bunch of different databases that allow you to manipulate the data in in different ways Now we don't have all these Databases in production, but we have used either all of them or have been prototyping with them Or we don't have it in production. So that's that makes it this an interesting problem And so now you have the sources of the data and you know where to put it But we're talking about a lot of different sources and I do blink we work with more than 60 So if you try to do that And in a following manual processes is gonna be to use and it's gonna be very time-consuming So how do you automate that? Well, you should use and something called data pipelines and these are tools and We use Luigi from Spotify and Airflow from Airbnb that allow you to automate all this process of capturing data Manipulating it putting it somewhere Okay, so now we have the data in our databases. Well Actually, it might be data that you cannot really use because you have to check the quality You have to understand if it really represents what the source is telling you and you have to Actually spend time and taking the quality maybe cleaning the data and or maybe you cannot actually use it at all because the quality is not Good enough and remember that this part is Really key because if you start with back data then the final numbers of the final statistics that you're gonna present to the user Then just not gonna be real. That's just not gonna be truth And you want to show numbers that are as close to the truth as possible And so this is something that is like the foundation of the whole Product and also you have to take into account the the bias of the source and the dais regarding data source means how this data is only applicable to some a part of the population and it can introduce a lot of noise and Distortionate the numbers that you are showing and pretty much every source has some bias You just have to be mindful of it and know your way around it Or if there is no way around it, you just cannot use that source and the final aspect that I wanted to comment on is Data regulation and protection things like GDPR. This is also very important And very very relevant and something to have in mind for every single source that you use It actually doesn't impact location intelligence that much because you are not storing personal data Every all the data that you're using is anonymized so you cannot identify individuals or anything like that But you have to check that all the sources that you are using They actually comply with GDPR and you have to always and Take into account that whatever you are producing cannot be used for that purpose, etc Another challenge I wanted to talk about is And when you get data in different format from different sources and Maybe it's not the granularity that you need. So this is a map of Paris of Data related to different areas of Paris and the center of Paris is treated as a single Neighborhood so this is not good because probably your user wants to have something like this Which is very granular and it goes all the way down sometimes maybe even a street level So how do you go from one to the other? It's a problem about statistics and mathematics But sometimes is is quite difficult Okay, so now we have the data and we have the right granularity Okay, so now you have to put that in the real world the real world that has streets and houses and schools and crossroads So that request a different set of problems and a different set of technology that we solved using Graph databases in our case Neo4j where the the nodes of the database represent street crossings and the edges represent the street and You can now mix this with all the the other data that you have This is also useful in order to show a routing or build models to understand how people are moving around the city and things like that Right, so now we have the data and now we know how to represent it now You have to build a front end and building the front end for location intelligence Is also interesting and challenging because you have to put a lot of data there and your user. She's not Using a mainframe in the cloud. She is using a laptop with four gigs of memory And this has to fit in the in the browser So actually one of the first things that we do when we talk about inserting a new product feature in our tool is thinking how much Information we're gonna have to send to the front end and if that's already about the threshold that is considered Natural because otherwise you can easily freeze the laptop of your user and for that we use View js in the front end and we use extensively node js to do a lot of precal questions in the back end and quickly send Stuff in parallel to the front end and then the final tech challenge. I wanted to talk about is well big data and Technology is like spark or hadoop. They've been around for a while They've been used for many different purposes in many different industries But they don't actually go very well along with location data and graphs because they're fundamentally based on map reduce and divide and conquer and You cannot really apply that to a graph this being some Advanced with your spark and some tools, but this is still a challenge that needs to be solved We're working on it or the companies are working on it and I'm sure there will be a lot of development in the in the upcoming years Great. So in terms of building a location intelligence product, there are of course many challenges The first one is is the user right is the user experience So I mentioned a few things like predictions and then you have graphs and then trends and then a map So how do you put all those things together in a way that your user is going to get it? It's going to get the value and not there's all So it's not that there's one user you have many different kind of users So you have the GIS experts those are the pro users They want to know all the in and outs of the platform and the location on the data and everything else all the options And then you have the business users that maybe they don't have any analytic experience. They just want to get the answer So all that complexity needs to be you know taken away From the user experience and just create create the right experience for the users So they can get the value Also in terms of user experience, I'm pretty sure that if you were to build a mobility app I mean Maybe you miss the interface maybe but as soon as you put a map like some kind of Information below and a massive button so you can order, you know, if you copy this you are okay more or less But there are many location intelligent solutions out there So it is very challenging to create these kind of solutions these kind of products So sometimes you have to lead the way Make sure that you are very customer centric You sit down with them and you validate how they want to get the value and In terms of the value and that's another point related to your experience Because there are so many different use cases that you can solve with data and you have the data You might have the temptation of you know fixing all those problems So, you know if you try to solve all the different problems at once you you're gonna end up with a product like this So this product it does pretty much everything, right? It's a phone is a fax is a printer is a copy It's got internet and Facebook. It's got everything, but nobody knows how to use it, right? So there's that that one person in the office who knows how to use it and produce the busiest in the office So you don't want to do that with your location intelligence product And then the the other problem is starting from the data. I see this problem quite a lot from Especially large corporations because they have these data. They think that they can bring value into the market So develop products and and everything else based on the data and that's just not the solution You should start from the pain point. What's the problem? Because most of the time The solution is not only one source of data is plenty of them And it's tons of technology on top to actually bring the business value to the business When talking about the data, then we need to talk about Partners and data location partners and Miguel Angel mentioned this before but this is very important You need to bring the partners on board where the quality is good. It is consistent So it's not just a provider is a partner. So it's very important that you're careful with your data partners There's so many memes in this presentation So one thing that is also related to the features is because you have all these different data points You might want to show them all right all these, you know ratios and penetrations and all those different things But then you use it's gonna be like this. Just you know, where is the value for me? Like they're gonna get lost And finally in terms of challenges, I think this is an important one There's a cultural change involved with this. So it's a it's a mindset So many companies they've been operating in a very specific way for the past 25 years When you you're telling them that they don't need to count people anymore that there's a better solution You know, that's change and people don't like change So that that's a that's a cultural challenge that that you need to face And also the users the users when they think a tool like this a location intelligence tool like this And they're thinking should I look for another job because you know this tool is gonna do pretty much everything So, you know, but that's not true. So you really need to be aware of the cultural change Okay, so kind of a few takeaways First When looking at business data, there is a lot of location involved with business data Actually, there is a lot of location in many of the business Location intelligence is now from the technology and data standpoints. That's mature enough now to provide direct business value That's that's already there And more importantly, it's becoming mainstream and it's it's growing and it's gonna solve more and more problems for the business Thank you very much We have seven minutes for questions Sorry, you probably you were planning to repeat the question. Yeah, no So the question is if here in Spain and the local market, although we operate in Spain France and the UK but the question was if in Spain a business are aware of the geospatial systems and kind of the location intelligence what the location just can bring Into the those business Well, there are different of different levels of expertise So you have very large corporations with the specific teams that they have their own tools and they kind of know about them And then you have the small medium business that they are not really aware But from our experience, there is a lot of experience and gut feeling involved in many processes Where data can play an important role. Yeah, I can take that one I was talking in general about the VIE systems and also I was talking about not just being able to host The coordinates also so meaning about specific analysis of data based on location I mean, I was I was talking in recent years Obviously location your location intelligence been around probably for like three four years So, I mean, I'm I don't know exactly what systems you have used. Maybe they were actually Right, but isn't Tableau is not really location intelligence. It's just a way to visualize Data, it doesn't allow you to actually analyze and extract insights from from what's going on In any case, just taking with a grain of salt I mean, we're talking about how initial business intelligence involved into location intelligence Those those kind of the message that we were to transmit More questions. Yeah, I'll take that one Yeah, there are many different data sources. So you go from public data sources that are available, you know from the government But there are many data sources that they come from providers So sometimes, you know, you can buy the data from a provider Sometimes you have to partner if it's a banking company, for example But it's never one source of data. In fact, some of the data we generate ourselves We have our own indicators To solve the problem if the focus is on the problem And you know when you're looking at the data you you gotta work with different partners and different data providers So it's not easy. It's a tricky part. Yeah, so When I was splitting into things so you have the business data that you can get from the business, right? The historical sales for the past two years, for example And then when it comes to location data, it really depends on the source But you know, you try to have like at least a buffer of one year one year and a half of data So it actually the prediction makes sense otherwise But again, it depends on when the data was available and how much data you are collecting But yes, it's very important very relevant question Yeah, from the technical side you just need to Figure out how to manipulate a stream of events at different points in time With that you can build a model What's going about just like simple machine learning And the problem there is that with location intelligence you have a lot of different Variables what's looking on of the hundreds? So the the tricky thing here is how to find senior model with all those variables and extract the ones that actually are impactful Well, thank you guys very much. Thank you


In other news now, promoting infrastructure development in the educational sector has been identified as a major factor to increase schools' enrollment and also reduce out-of-school children. The Builders' School Initiative in Africa, a non-governmental organization and the Church of Jesus Christ of the latter-day Saints in Isago, Inago State, made the call during the handover of four classroom blocks, a board by the NGOs to promote schools' enrollment to the state government. The founder of the Builders' School Initiative in Africa, Victor Ukorebi, said that children should be encouraged to be in school and not on the streets. United Nations International Children's Emergency Fund UNICEF reports that one in every five of the worst out-of-school children is in Nigeria, and I bet we have some in this state. Similarly, that mind-blowing statistics account for over 14 million children that are not in school. About 60% of the nearly 14 million out-of-school children are girls. More than 5 million are under the age of 15. This is heart-breaking, and this is alarming. Our children belong with the classrooms and not on the streets. They deserve education and not parking or idling away. Speaking on behalf of the state government during their events, the deputy governor, Cecilia Ezeilo, commended the organization for promoting schools' enrollment in the state and said that the government is ready to partner with any association to promote a conducive learning environment for students in Inago. The classrooms, they are forced to study under trees in all weather. They are exposed to the elements, distracted and unable to learn. The busier provided safe and secure classrooms to enable our children to focus on their learning and their future. The Inago State Government is indeed elated and proud to partner with busier on its noble and philanthropic enterprises. I built school initiative in Africa and all my heart is joy, you can see it. Right from the day they stepped toe in this village and promised that they would build this school under three months. Before two months, the school is already there, so I am so grateful to God, so grateful to the government of Inago State. Thank you for watching.


Alright, so YouTube team keeping clean what's going on the same Raven here with another video now Ravens got a game coming up against the Colts in a couple days couple days they play the Colts and on offense for the most part even though Ronnie Stanley's out for the year Tyree Phillips is out for an undisclosed amount of time Mark Ingram. He'll probably still be out But other than that the offense is pretty healthy Now if you look on the defensive side of the ball, it's it's a big yikes. It's a very big yikes Because Jimmy Smith He's hurting. I don't think he practiced on Friday So his status is up in the air because y'all remember Friday is the practice day where a lot of stuff gets determined Usually for Ravens if you don't practice on Friday usually don't play but in Jimmy Smith's case. I would He'll be a game time decision Now we have a lot of other guys like Marlon Humphrey. He's out this week Ain't no game time decision with him The game has already been decided and it's not his time to play because the reasons that y'all already know of now there's a lot of guys who were considered high-risk guys that were around Marlon Humphrey and Matthew Jude on was one of them now It does take five days you have if you're deemed high-risk Then you have to be team keep it clean for at least five days Matthew Jude on he got activated to the roster yesterday And a lot of people were confused especially myself. I'm like, hold up. It hasn't been five days How do they activate him already? But they said that when he was deemed high-risk His five days started before everybody else's so that's why he got to come back. So I'm like, okay cool Whatever, but if you look at the Raven secondary, um, Marcus Peters, he's healthy. So that's good And then it's just Khalil Dorsey, you know Anthony Levine. He's been dealing with the um I don't even know exactly what his injury is but it's around his midsection He's been dealing with that. The Sean Elliott is on a high-risk list It's it's just looking rough. It's looking rough right now But again, as long as they clean for another day, they'll be able to play on Sunday But it this has led the Ravens to work out a couple of guys Some more notable names and some other not so notable names, but let's look at the list. So two days ago They had a trial list had Evan Adams, Zach Bailey, Nate Brooks, James Crawford and RJ Prince So that was that's two cornerbacks Nate Brooks and James Crawford two guards Evan Adams and Zach Bailey and an offensive lineman RJ Prince and another those those names aren't so notable and that's no disrespect to them But some of the more notable names that they've had for workouts are Tramon Williams Now we know Tramon Williams used to play for the Green Bay Packers Slot guy safety guy guy could play some different positions and whatnot and in his packers day. He wasn't bad But that was in his packers day now. He's 37 and I'm just like See my thing is that I ain't gonna problem with them working them out I ain't gonna problem with them working anybody out because you you want to know what type of shape these guys in especially if they're not retired but It does say a lot about the the status of this roster Says a lot and then Today it came out that they're gonna be working out Antoine Bataille as safety So he played for the Colts. That's the team that I know him the most for I believe he plays some with the 49ers as well But that I mean it couldn't hurt get some veteran presence back there at the safety position Of a guy with plenty of experience But this it just says a lot about where this roster is right now Because the second day they just they they banged up Now again with Antoine Bataille with Tramon Williams, they would have to go and just with everybody else too There are no imminent signings. It's not like okay. It's not like the old days It's not if y'all know things have changed Drastically you can't just bring somebody in for a workout or a tryout and then sign on the very next that new They got to go through I think five days either three to five days where their tests come back team keep it clean and Then you can sign them and I think you actually got to do that You got you got they have to go through testing before they even have the trial on the workout So it's just this whole thing going on has made the situation a lot more difficult than it used to be But hey, you got to you got to be safe and you got to be clean But this lets us know that the Ravens are They they get some backup options in mind Because they just they got to know who's out there as just in case just in case Because right now the team is obviously just On defense. There's a lot of holes right now. And again, it's just one game so far but You've been hearing all these different stories around the league and again, they didn't just work out secondary guys They did work out some secondary guys But that wasn't it. They worked that offensive lineman as well So with this whole thing going on right now, you've been hearing about you heard about a lot of different teams over the past couple of days With guys who were getting sick You heard about about it a lot like I feel like over the past couple days This is the most active that it's been In the nfl just league wide And it's unfortunate man. It's unfortunate because this obviously is much bigger Than football. It's much bigger than football So hopefully this thing can be a thing of the past before we know it So but we'll see how it goes But with the ravens these guys are They're heading into indianapolis a place where they they've never won before So that's very it's very interesting But you know records are meant to be broken They are meant to be broken now While records are meant to be broken sometimes people can sound like broken records Now, of course the ravens they lost to the Steelers last week It was a great game shout out to the Steelers they won Uh, but with the ravens when they lost Lost was obviously mostly on lamar jackson You can put some blame here and there and everywhere else too, but it was mostly on lamar jackson But so was that comeback lamar led the team back a couple times, but they just came up short But it's all good. They're 5 and 2 not like that's the end of the season Like a lot of people are acting like I've seen so many people say oh man This this team is not a superbowl team now man. Oh man lamar jackson He's not the ravens quarterback of the future. Oh man.  We're terrible We're not going anywhere. They're 5 and 2 like they're 5 and like no, they're 5 and 2 I don't understand people sometimes and I know being a fan you're fanatic And sometimes you can be a little crazy, but They're 5 and 2 But anyway, like I said records are meant to be broken But sometimes people sound like a broken record after that loss against the Steelers Mike tomlin Steelers head coach. He said that We we respect lamar jackson, but we do not fear him We do not fear him And guess who said the same exact thing Justin houston From the coats lying back outside lying back a patch or whatever he said We respect lamar jackson But we do not fear him And guess who put that up on their their official twitter Indianapolis coats I said, okay coats. I see y'all. I see y'all. That's all good But I just I don't think that's a smart move I really don't I don't think it was a smart move and I think there's a tiny bit of petty in there too because Justin houston, you know, you know the ravens wanted Justin houston. Y'all remember I remember But he ended up going with the coats And I think that has a little bit to do with it too Because you don't just come out of nowhere and say that Yeah, let me just oh, let me just repeat what the next man said. No, that's weak man. That's swag. That's garbage. Don't do that But now lamar and boys better go off They got every reason in the world to go off and not just because of bulletin board material But just because they need to really Gain some steam and gain some respect too Because a lot of people are overlooking them And that's cool. Hey, I ain't got no problem with the overlook the ravens.  Nothing. Give them that underdog role again Give that back to make them earn it Well, I mean they got no choice but to earn it But make them don't give them all this national respect the meteors. Don't give them that Let's go back to how things used to be But anyway team keep it clean. I appreciate y'all. I love y'all. Thank you so much for everything I really appreciate y'all a lot I'll see y'all tomorrow We out


Welcome everybody to the webinar on a road market for energy, technology and the budget data. Co-organized by the International Energy Agency and Mission Innovation. I represent the International Energy Agency that work in the Energy Data Center on data. First of all, I would like to thank Mission Innovation. We have a very nice collaboration, consolidated joint activities in the space of innovation to really, as an objective, build a joint community for innovation. And clearly this also includes data work. We, in fact, have a collaboration on joint data collection for LB&B data with Mission Innovation, in particular in this phase. I'll give a quick overview from the International Energy Agency, but really we are very much emphasizing the importance of innovation to reach energy transitions and climate objectives. If you take our flagship report, 9 to 0 roadmap by 2050, there is a strong message on the importance of increasing clean energy innovation. Both for the deployment of the technologies that are available and also to increase the use of the technologies that are not yet on the market. So the AASD estimates that about half the technologies needed to reach a net zero are still in the development phase. So clean energy innovation needs to accelerate rapidly and governments need to put up the LB&B research development and demonstration and deployment at the core of energy policy and climate policy. But at the IAEA, the foundation of the policy work, we clearly have a solid work on data and the focus today will be on data. The IAEA itself has its own work on innovation tracking. For example, we are going to publish soon a tracking clean energy progress report to assess the status of critical energy technologies and give recommendations on how to really get on track for the long-term climate goals. But in the domain of innovation data, one of the key areas we mentioned to our members is really tracking investment on public research development and demonstration budgets for different energy technologies. Clearly, even public and private are both essential, but we focus in our member requirements on the public budget. In fact, we do have a DIA consolidated data collection on energy technologies at B&B. We have a unit, the data base, I would say in the world. We plan cities to go back to 1974 and the next three years will be on May 3. And we thank actually the work of many experts in the world. Many are here today will speak and participate in the webinar because it's thanks to the national work that we can feed our international database. But through this work, we have realized how maybe the different experts in different countries don't find the workforce easier straightforward. So we embarked on this project, trying to see what can we do to help maybe depending on this understanding of country level, what strategies are needed to be put in place to address the different type of challenges. What are the difficulties in the institutional arrangement in the legal framework, in the technicalities of the data collection, in the classifications. So different countries have different approaches, but we kind of wanted to consult as many of you as possible and synthesize the information in the form of a guidance to define a national roadmap. It's a document that is available on the web now. It was released last fall and we are here to promote its use today and to explain how it can be used both as a tool to assess the status of a country reporting compared to a sort of ideal framework across all its different blocks that we mentioned. But also as a guidance document, we prove different ideas of establish a new data collection. So we're addressing both beginners and more advanced data providers. Of course IEA will be very happy to work with any of you to improve the data together and using this tool. And so we're looking for this webinar and the continued collaboration with IEA. I would like to thank in particular Susie Le France, the IEA, and Ingrid of course for that. That's our reorganization of this event. And I wish you a fruitful experience. Thank you. Thank you, Roberta. I'd like to give the floor to Ingrid from the European Commission in relation to criteria that will give some introductory words as well. Thank you very much, Susie and Roberta. Hi everyone. It is actually an absolute pleasure to be joining the IEA's webinar today on tracking public energy R&D investment. So my name is Ingrid Amoroskaita-Bull. I am the Mission Innovation Insights Module Manager and I come from the Joint Research Centre of European Commission. Exchanging experience and practices when it comes to public R&D data collection is undoubtedly very important and we can clearly see that from our own line of work. So in the Insights Module we focus on tracking clean energy innovation using data on really various indicators such as private and public investments, patents, venture capital investments, standards, levelized cost of electricity, total installed capacity really among other indicators. All of these come from the GRC and my partner organizations IEA and also IRENA. So I want to share with you a very recent example from our last workshop that took place on 22nd of March in Rio de Janeiro as part of the MI Senior Officials Annual Gathering. We presented the Insights Module plans for the upcoming year focusing on particular, on our main deliverable, which is Energy Innovation Metrics Hub. This hub will be like a platform in a form of a dashboard collating information on the already mentioned indicators but also others that will be categorized per MI mission topic area. So such as Hydrogen, Green Powered Future Mission, Shipping and so on. And to collect feedback on such proposal of ours, we have therefore produced a short online survey to mission innovation members that was prior to the event where we asked them which indicators would they find the most useful for their governments to have included into the hub that we will be collaboratively producing. The top five answers in the precise order were public R&D investments, cost of technology, demonstration projects, technology performance and private R&D investments. So today we're discussing the number one public R&D investments which is something that MI countries surely feel a great need to track. One of the key discussion points during our event was that solid data on public investments helps to make informed policy decisions on energy research. It helps to assess the performance of research programs already in place and to design also new funding schemes. I think as most of you know perhaps already, MI countries report on their public R&D investments to the IEA in order to make the process easier and better aligned and also to avoid a duplicative effort. Even those MI countries that are non-IEA members report via a simplified version of the IEA questionnaire. And we're tracking this from the MI's perspective as all of my members have actually in the second phase of MI agreed to sustain or increase their public R&D investments. And during the event we have also heard from Brazil about their experience on reporting to the IEA. They were the first non-IEA now association country to actually do it and one of the key points that was raised was also the knowledge sharing with other MI countries in this process and really learning from the experience of others that perhaps had already more established data collection processes already in place. And indeed collecting, validating and disseminating these data is a really complex process just as the roadmap report very thoroughly explains. Also like what Roberta just mentioned, countries are on very different journeys like Austria collects energy R&D data since 1974. Meantime we have Lithuania that started in 2020 and now has run with three data collection cycles. This is why it is absolutely essential to have these opportunities to really best learn from the experience of other countries through the reports like these and well events like these. So on that note I really look forward to hearing the presentations from national experts on their countries' experiences and perspectives. And I believe I hand back to Roberta. No, thank you. Because the main person here is Susi who is nice to me. I hand back to Susi. And she will actually explain what we are talking about with this new document, the guide to design a roadmap for energy technology, our D&E budgets. And so actually I'm leaving the floor to Susi for this presentation that will be the opening of the framework and then we'll have a presentation from country experts. Thank you, Roberta. My presentation. So yeah, thank you Roberta. Thank you, Rita. So yeah, my name is Susi. I'm an energy data officer in the innovation and indicators on innovation and indicators in the energy data centre at the IAEA. So here I'll be going through what we do at the IAEA in terms of energy R&D data and how we try to support countries in their thoughts through the publication of a roadmap that Roberta mentioned. So this road back to tracking public investments in energy research. So the first question that we might want to ask ourselves before we look at all of this is why are we here today basically? Why we make all this effort to collect this data and why we collect public energy R&D data. There are several reasons, but maybe the first one is to support the design, the evaluation and the adjustments of public policies related to energy, innovation, or both of them at the same time. This data allows to identify gaps and opportunities in current investments and define new priorities. If, for example, hydrogen is a national priority in a country, assessing if the amount of existing R&D investments is enough and let's show them maybe more efforts are needed in the data. It's also possible to use those data to evaluate existing programs. For example, the defined objectives that have been decided at the beginning have been fulfilled during the program. It's also allowed to benchmark countries against each other providing though there is the same methodology and definitions used, which is what we're here for as well to make sure that all the data is comparable between countries. It's also got useful for the private sector and their own energy innovation strategies because the public sector has the possibility to maybe invest in more nationless market-ready technologies that the private sector can't. So for all this reason, as Roberto mentioned before, EIS has been collecting this data from its members' national administration for a number of years. So that we publish twice a year and we'll get the new version in about a month on our website. So what do we have in this database? We have research, development and demonstration budgets funded by national governments and state and enterprises. So all of this can be disaggregated between R&D and demonstration and between governments and SOEs. It goes from 1974 to present day, so when the next relief will have even 2023 data from some of the countries. It covers all the 31 IEA member countries as well as Brazil and the European Union. And we are always looking to support any other country that wants to collect this data to expand the coverage of the database. So it covers in terms of technology, it covers quite a high number of technologies in seven main groups of technologies which are energy efficiency, fossil fuels, lane fleet CCS, renewable, nuclear, hydrogen and fuel cells, other power and storage and cross-continental research as well. And then those group of technologies can be integrated into like three levels further. So for example, under renewable, you'll find like solar energy which in turn would have this big between solar PV and solar thermal as well. So you can see here preliminary results from the next release in May. As I mentioned, most of the work is not done by us at the IEA. It's done by the countries, so we're very grateful for all the effort that you're putting in collecting this data. It doesn't go without its challenge. Of course, that can be such as like when how to start collecting this data when it's not currently the case, how to identify possible improvements in the existing data connections, how to expand the coverage, improve the technology desegregation. One quite big challenge as well is how to maintain data quality year on year taking into account that there might be like resources or high staff turnover. And of course as well ensuring that the metadata is at the level of the data. So based on all this challenge that we heard from the country through like workshop that we had on discussion we had with them we identified that there was maybe a lack of like a missing tool in terms of support because we give support in terms of methodology but we didn't have the mental hope to have the countries actually collect this data and this is where like the roadmap came from. So based on this necessity for knowledge and experience sharing we started this roadmap project. It was a product of discussions between countries so you can see the countries that participated in the project and we're very grateful to them as well at the other countries that might not participate in the rebuttal we have helped us identify these issues over the years. So the result is basically a description of the variety of country approaches to set up and maintain a public energy energy data collection system and this is intending that both like a guide for both the countries not currently collecting this data but also countries with more advanced systems that collect strengthening some of the areas. So the report is structured as you can see. So we try to make it in different parts to make it clearer and help countries focus on certain areas at one time. So it's with like six main components that we identified are like internal separations into different parts. So how first maybe like the first thing to look at is like the purpose why are we collecting this data? What do we want to achieve in the end? What are we going to need? So this is where you will the country should probably like different clear objectives to end up with a world structured and defined data collection system. Then there is like the institution arrangements. This is where like the country might want to look at like how to set up the framework. So who's going to be coordinating the data collection? What resources are going to be used? Then you want to look at what the innovation landscape looks like. So among the partners and the data sources who's funding performing energy RGND then create the network model working levels of who are going to be the contact point in each of the institution and so on. Then you get to the phase where like you will do the actual data collection, processing and validation. Here you might want to start by assessing if there is data already available that you can be building upon. Then you want to define the data collection and the classification as well as the validation of the data. Then maybe in parallel of this the data management and technology which is where you are going to look more at the IT tools. What are you going to use in terms of software really for the technical parts? And if you want to use maybe like ARs or more advanced way of classifying the data for example. Then of course in the end you are going to do a dissemination of the data because all the work that's been done before if the data is not properly disseminated it's not going to show the re-value of the work that's been done. So here you might want to look at how they are going to share the data itself, the data sets and also how they are going to communicate the finding through analysis to the important stakeholders. And of course the continuous components of this data collection where you want to establish something that is going to work year on year and that's going to be improved. For example, do mark partners like step of the institution arrangement. You will want to do that every year because you don't want to miss new vectors from what appears over the years. So this was the main part of the reports. But we also like publish country annexes, as you see with the report. So for each country based on the discussions that we had with them, this interview that we conducted, we defined some sort of like a mini report with the same structure applied to the country where we described more in details how they do like their data collection. So it also goes through like the institution arrangement, the collection classification and validation process, the data management and technology and data dissemination. So this is why even if you want to have more concrete examples you can look at. So the first component that I mentioned, so the purpose in each of this section we look at what the country needs to consider in the data collection system and the key question that might want to ask themselves. So for example looking at the purpose of the data collection it's really important to define like the specifics of the desired output from the beginning so to end up with a well-constructed system and a final process that is appropriate to the users. Here for example countries can ask themselves like what are the national needs for this data collection process, what are the requirements of the different innovations they called what governments we want to look at, what experts we want to look at so you define your process well and also what are the data you want to collect. So a good example of countries that really set up like clear objective was Brazil when in 2018 there was a lot of ambitious projects to improve the process of collecting and compiling energy and energy data but the main objective was to guide public policies and fill gaps so this was a way to end up with the process that is quite good now. So then when looking at the institutional arrangements one of the key elements will be first like who will coordinate the data collection and what sources are available then you will want to ask yourself like who funds and perform the public energy energy in the country to see all the actors that you need to be in contact with and also who will be the key contacts for the collection and validation and all the relevant institutions and so you can maintain and ensure like a good working relationship but also a good like high level relationship. Austria is an example where like the ministry like responsible to the IA to submit the data decided to outsource externalize the data collection through a tender so it's now being done since I think about like 15 years by the Austrian energy agency so this is an example where the ministry decided to delegate the work to experts on the subjects. Then we're looking at the process so data collection and processing and validation some example of questions so what I say like those are just the example of like key question but it's also it's of course there's like a lot more questions and you can find more details in the roadmap of course but some example of key question is like is there for example an existing data collection process that already exists and that could be built upon not started from scratch where would be the best means to collect the data so this could be to service to the funding organization or performing organization or this could be through a database that might include like project data or that institution would use to the data. It's also good to look at how can the data be checked for consistency in a currency at the end of the data collection so an example is this is Switzerland so the Federal Office of Energy the one responsible for this data collection and they set up a quite like exhausted like a process with a very good coverage in the end where they use both existing project databases but they also complement them with surveys to research organizations and they also use secondary data sources to complete the data. Then in terms of data management and technology so here it's very technical and it's often parallel with the previous phase like here you will look at what actual IT tools you're going to be using to collect the data should you be using AI for example to classify the data in a better way or what type of software you're going to use to store the data so here it's very in terms of a technical term because maybe you're currently using Excel files to gather the data from the institution and then like sending everything in an aggregated way for example for yourself as well but maybe you want to look at if the data is going to be more efficient of course depending on the country and their situation in Estonia for example the data calculation is fully done through a database and then like categorize the projects based on keywords that are related to energy. Then finally when you get to the data dissemination quite important part as well this is where you will look at how can the data be effectively communicated so this could be to ministry and audience actors for example who will need maybe detailed information in one technology in particular where you will want to look at very disaggregated data or if you want to talk to the public maybe you're going to want to share more aggregated data more high level to give an idea of the priorities in the country so you want to know the target audiences to adapt or you share the data. A good question to ask yourself as well is how can the data be published in open access so to make everything you connect fully available to the public which is usually good for transparency in which case you will need to be even more careful about the metadata that you published with the data to to make sure everything is transparent. Canada is an example of countries that publish each year sorry an energy book done by the natural sources Canada where they give the overview of the status of energy in Canada and it includes all of the indicators include the energy audience so you can include this roadmap is really for a tool for countries to internally asset the data collection system it should be the basis for discussions with the country and how they can improve and set up the data collection and would be very happy to collaborate with any interested country to assess the status of the data collection system if it started or not and you can use this roadmap as a basis so please don't hesitate to express your interest and you'll find like the the idea like you can contact us at rdndf.org yes so if you have any questions during during the if you have any questions during all of this presentation so this goes during like the presentation if you have any question about the presentation I did or even the presentation nature is from the country please don't hesitate to the Q&A section now would like before I give the floor to to Ireland I would like to share with you a mentee question though I don't know if you're all familiar with it but this is a way also to give some interactivity to this so so if you go to this website so mentee.com and you follow the and you write the code that is written in the next to the to the address of the website I will share the link as well in the chat so you will be able to see this we would like to ask you as the countries collecting data what are the main challenges that you that you think that you encounter when collecting public energy and the data so we probably is it the coordination with the founding institution if it's categorization of the project and data and technologies is it the lack of staff resources the lack of high level engagement or the lack of clarity in the definition themselves or any other so if you think those are not the main challenge please don't hesitate to share in the chat as well so I will share the link in the chats in the meantime I would like to give the floor to to Irene Ward who is program executive in research development and demonstration at the Sustainable Energy Authority of Ireland so the floor is yours Irene Thank you Susie and we're delighted to be here today to share our experiences in data collection in Ireland so I'll just share my screen and hopefully everyone can see that okay so Sustainable Energy Authority Ireland are the responsible data collection in Ireland and that's in public in public or D&D investment and so I'll be presenting today on a number of items so I'll introduce the D&D funding program I look at the National Energy Research Funders Forum then give an overview of energy research in terms of data collection data management and dissemination and then finally our National Energy Research Database so SEAI is Ireland's National Sustainable Energy Authority and we are an agency under the Department of the Environment, Climate and Communications of the Government of Ireland we're at the heart of delivering Ireland's energy revolution and we're driving the replacement of fossil fuel usage in all sectors across our society and we work with government, homeowners, businesses and communities to create a cleaner energy future and research is the heart of our strategy and so the we lead and coordinate energy research supports in Ireland so we do that by running a National Energy or D&D funding program we're involved in strategic research partnerships we also coordinate research on a national and international basis we run an annual energy research and policy conference we are the contracting party for the IEA TCPs and so we have Irish representatives on nine TCPs currently and we're also the Horizon Europe contact point for energy and so in 2022 we set up a National Energy Research funders forum and this was to establish a National Strategic Energy Research Coordination Group to accelerate transformational and impactful energy research in Ireland so the main aims were coordination and collaboration between energy funding providers in Ireland to identify emerging energy policy needs and any gaps to exchange information and also to aid in the data collection process for IEA and others and also to look at European and international energy research activities so the forum members that inform our data collection are listed here so we're funded by the Department of Environment, Climate and Communications but we also work with our Department for Agriculture, Food and the Marine we work with the Department of Transport the Marine Institute Geological Survey Ireland Environmental Protection Agency Enterprise Ireland Met, Erin, Irish Research Council, Science Foundation Ireland and also Housing Local Government and Heritage and Further Education and Higher Education which incorporates research, innovation and science and so our data collection we implemented a new process in 2019 and we backdated that to data we held from 2014 so we circulate an Excel questionnaire which is different to the IEA questionnaire and that goes to our primary relevant funders who are public bodies we collect project level data including the title, abstract, start and end dates, the funding amount the funding agency or if there's any co-funding lead organisation and lead researcher and the maximum amount of funding is that which was awarded at the award stage but it may, expenditure may be different during the project and so for multi-annual projects we take the total budget and divide it by the number of years that the project is running in and then the the data providers classify the IEA to a two digit level and then SCAI classify that to a more detailed level we do all of the validation checking for duplication in year and between years or with co-funders and then we use that to compile our IEA or DD questionnaire on an annual basis and we then have a national energy research database this is a centralized location for energy research in Ireland it provides information on current and past energy research projects and it also helps to encourage collaboration and communication between researchers so there's a you can search by category you can search by keyword and it's linked from our data and sites website on our SCAI page and so the main users of our database are those involved in energy research policy makers industry other interested stakeholders and they can find out more about ongoing research projects and search for possible future research partners or collaborators but also for data providers and it's an open database with project level data and again it's there's a link to it and today it's very interesting to come along here other people's presentations and we will hopefully maybe improve our systems or perhaps learn about other models and maybe recording private energy or TD investment in Ireland as we record public data at present and that's the end of my presentation so thank you. Thank you very much Harry it's very useful indeed to see your point of view as well it's actually quite interesting as well to see that we hear that more and more people asking also about private sector data at the country level and we know that it's quite different way usually to do this it's more like survey based and less individual for each organization and it's a whole other project as well but we see more and more interest in this as well on to the public and public sector data and we're also looking to improve to improve on this on our side as well. Thank you very much so we can see the results so I don't know if anyone has any question or don't hesitate I don't see any for now but later we can of course share the questions in terms of our question as I asked you before that it's mainly coordinating with the RGND funding institution that it's the main challenge that you seem to see most then a little bit of the other categorization of project data and technology where we might be able to have probably a little bit more of the idea so thank you all for this input it's quite useful for us as well then I would like to ask you another question so what sort of support do you think the IAEA would you need to help in your data collection so would it be like more bilateral meeting on methodology, more experience sharing with other countries directly with your colleagues in other countries maybe better documentation and actual online trainings and any other ideas of course so please share in the chat so as I said before you can go to menti.com to reply in the code there and I will also share the link so now I would like to and we'll look at the results after our next presentation so now I would like to to invite our colleagues from the Netherlands to present us this Gasper Varends Stef Nieuwele and Michel van der Bond who are advisors at the Dutch Enterprise Agency so yes the floor is yours thanks a lot Susie for giving us the word and well for hosting this really nice presentation we'd like to share indeed the experiences with other countries and we'll see what you all do well today three people are presenting it's Stef, Stef you see her in the middle and next to me Stef is on master with pie charts of course we have Gasper Gasper is logged in via a different login account Gasper is always sharp at this magnifying glass seeking for differences in our data and we Michel van der Bond that likes to point to data well we are from the Reichstings for Unternehmen in the Netherlands and that's briefly translated to the Dutch Enterprise Agency and today we would like to share you some info about what the Dutch energy innovation portfolio team is and who participates how we monitor and which ground principles we maintain and how we innovate research and develop for your data you can think for example about machine learning but also our technology list well it's maybe nice to show you a little bit how it goes we have lots of gatherings, we are colleagues and yeah we like to have some fun next to of course talking about our data who is in our team, Sabine Lenkijk Stef Knibbler Jan Lansman, Cosper Barenz Jan Boman and myself now in essence what we do we monitor energy innovation instruments and we do that for the Dutch Enterprise Agency so here are some examples from reports we make and for example we have an instrument report that's very important for policy making from the other end we make lots of senki diagrams what you see on the right side and for example you can see here how the money is going to the different institutions we find that also very interesting to see okay where do we have the plug in place so where is more governance necessary, where is more need and where is more linkages in certain types of innovations other examples are for example a web based project database, it looks a little bit similar to what Irene showed in this database you can basically search what kind of projects we are funding where they are happening during in the country and also what is the status so for example is there energy research in solar panels or is it more in nuclear fusion that is also super cool to watch there and of course we make lots of fax sheets we would now like to give the word to Cosper thank you Michael yeah Michael showed some examples of what we do with our data and in order to do this we have certain basic principles for our monitoring we capture once from the source and we make use of the source data the source data is in our case most of the time the project plan we further enrich this source data with other sources like the budget item external data such as company information from the Chamber of Commons or SBI codes and metadata such as the EI classification what kind of product it is and what kind of process it is and we get it it is all in one application and Fiat is what we don't make use of several Excel's but we make use of one application which allows us to with one click we make multiple reports yeah there are four perspectives on our data in our data model where we get gather information on the on the left top is the programming instruments these are the governmental climate policy policy programs and innovation teams on the left bottom is innovation this is the state of the art technology the products and social innovations and as I said earlier we label then also the innovations on the category so that we can also report on that on the right top the finance this is the budget item of the project and on the right bottom entrepreneurship which type of entrepreneurship there is like companies organization and research institutes and by getting this four perspectives in our data model we are able to report on the very specific items and now Stefan will tell a little bit more about how we innovate with with our data yeah so we are starting now to use machine learning in the what we did the last 10 years because we've been labeling our projects with project plans on the ACA for many years and another team who was categorized in machine learning asked us to use this data that we have been gathering over the years as a training data set so they are trying to make a model that learns from the project summary and based on the project summaries allocates into the right category the ACA 3 when this is finished we will check this on quality so we will check manually if it is able to categorize projects in the right way and probably in a year or two we might have more machine learning based categorization but in the meantime we still do it ourselves and that's also very important because that improves also future machine learning tools that are based on our classification right now yeah next slide and we are also trying to label on technology specific cases like screenshots right here is from the EAA and it's a poster depicting different technology in very many levels in different categories this is useful for inspiration but not for classification as it is very widespread in certain categories and other categories are not specified and also some names appear on a fifth level which that also appear on the second level so well it is very handy for getting inspiration in what types of technology there are it's not a good classification and we are struggling with this because we get questions from ministry in what types of electrolyzers for example we have that the EAA manual is not helping with these type of questions and we do realize it's very difficult to make a good classification system because there is always a grey area is this technology or is this technology but this is something we are looking into also looking into other countries what they are doing but we cannot find anything so I am very curious how the EAA is thinking about this part of classification so this is things we are looking into right now and yeah that was for now the presentation and if there are any questions let us know I am not right now I think we will see thank you very much thank you very much friends from the lens I guess first step and Mikael it's very much appreciated so yeah we don't have any questions for now it is very interesting to this part on the more technical side it's hard to make it a lot more efficient through machine learning which is not very widespread for now I think for most of the countries it's still very based on excel and manual categorization so it's good to see what direction countries could attend to so we had a question which maybe was more of a general question if you could give us your input on now which was about if we think so it's for us it's for you as well I think it would be useful to have more standards in tracking data so not on the definition side but more on the standards in tracking data so like the data collection processes are similar in countries so everyone is wondering if you had inputs for that so this question is for you in the Netherlands what are your requirements for these inputs it tends to be very difficult to get one type of standard for your data that's exactly where you're struggling with so what we try to find is like an application that can link all those different pillars that Kasper was presenting about to make with one click on the button multiple repetitions but even in organization it's already very difficult to get that done because you have different standards well name it click for you you have SAS Enterprise also we try to use all that kind of different applications but there is never an only pretend application that can do it at all and that makes it really difficult so also for the new countries if you want to make a data structure it's really important to start with that like what kind of data structure do you want to use and how do you connect all those different pillars with each other that is super difficult but that helps you so much in the long term if you have a solid data structure then you can also report much more easy and you don't have to do manual work like categorizing yourself and that's where the pilot of Steph can really help if you have AI that can link EAR categories that would save us a lot of time and also it will help you in categorizing it better yeah I would be very interested to hear from you Michael about how you get on with that yeah it would really help with the classification side and reporting side thank you very much yeah I can now show like what result we got from our second question which was to lose my mouth I'm very sorry towards the question who was about what support the IEA could give in terms of to support the data collection so the results showed that it was mainly experience sharing from other country from the screen what do you think was useful to give experience sharing with other countries so like we could of the selected countries in contact with each other so it could be with smaller discussions but also with more workshop I think would be useful I see I can see the online trainings as well which is something that we could give with more explanation on the definition the methodology the technologies and so on so it's very useful information for us this maybe one last question for you would be for the country so would you be interested in collaborating with the IEA so on the roadmap using the roadmap as a basis so you could say no it's not necessary of course yes to assess options for improvement but yes to establish all the new data collection system or like other of course please share anything else any other interest you have in the chat so the link is the same for the mentee so I could twice the same it's the same for both so you can reuse it it's a lot so now I now would like to leave the floor to our next and last country which is Czech Republic so we'll have Simon Merler from the Ministry of Industry and Trade that will be sharing their sharing their experience we'll share the presentation hello everyone I should mention yes so before we start sharing the presentation any minute English colleagues hello everyone so first I would like to thank the International Energy Agency for inviting me to comment on Czech approach towards public funding of research and innovation in energy and then I would like to underline that the Czech Republic remains committed to the common target reaching climate neutrality by identity and therefore support and increased emphasis on clean research and innovation projects so next slide very quickly firstly national energy research and innovation priorities institutional arrangements so as I said very quickly next slide yes next slide main strategy documents covering all relevant sectors are predominant additional research development policy and of course state state energy policy so you can see the list of our priorities so I won't tell them right now but can read very quickly and of course short and mid priorities are translated to our national program data which is for that in coming years we did that too and of priority are a lot of separate priorities if every level are directly for the Czech Republic next slide here you can see our national priorities which as I mentioned the most important state energy policy priority research central development next slide here you can see the list of relevant programs of technology agency which is our public agency as I mentioned the most important program is data which is specifically focused on applied research and development in energy but there are others that are involved and of course we are also involved in international cooperation Horizon 2020 but also Western fund research infrastructures and others next slide please the most important part of my presentation is focused on collecting research and innovation so publicly funded research and innovation projects can be searched in the Republic through our public databases and portals there are mainly two portals the most important for our work is Starforce portal based on individual code and project name basic information these projects can be found mainly as you know the amount of eligible cost and the amount of public support not every year but little amount basic information about an institution carrying the research project solution time program of complex project public tender number etc and each specific project has an individual that serves as a unique identifier in the Czech Republic research and innovation projects data are collected by the technology agency of the Czech Republic next slide these public databases are managed by the technology agency as I said and also by research development and innovation council the Ministry of Industry with the help of the technology agency of the Czech Republic which has direct access to background data such as eligible cost and public support search specific energy projects in the Czech Republic within the Starforce database they provide us with individual years as we know the sum of money of funding but we don't know specific years so they provide us with these data so these projects are supplemented by already search projects as the search was carried out in 2015 by occupying one of the US questionnaires and now every year we fill in the form of questionnaire for the international energy agency so that's the methodology of our work of collecting data next slide so the nation so as you can see our money going to research and innovation energy so as you know the Czech Republic as a member of the International Energy Agency is obliged to report selected statistical data in form of questionnaire and as I said before we try to fill in the form every year also I would like to mention that these collected data not only for purposes of international energy agency but also for our national strategic documents and this slide please as you can see development of funds for research and development in energy sector shows a great increase in public funds spent on energy projects since 1990s it's obvious that research and innovation of research and development in energy sector gaining political and social social importance for us for this reason some programs have been set up by the technology agency of the Czech Republic to support not only technological projects in general but also specifically research and development in energy sector the trend of growth in funds allocated to research and development in energy sector was markedly between 2014 and 2015 but it was only one year and since this year our occasion have been gradually increasing again reaching its peak in 2019 the largest share of funds in the category of nuclear efficient fusion can be noted that the Czech Republic historically focused on the research in nuclear energy and finally I would like to say collecting data there can be some possible distortions which due to human factors so because we do it manually we have no computers as our colleagues but we do it by ourselves so the first distortion would have been to classify the projects in the wrong category this should not affect the total amount of eligible costs in public support but could change the ratio between the different categories in the column another possible distortion is the assignment of project to energy projects especially in basic research for example because projects are always more of borderline nature or it is always being considered whether to include the projects in the list of the projects or not so that's the second distortion and the last distortion may be the fact that the support for research is currently concentrated on less specifically targeted programs so finding currently solved or recently completed projects can be relatively easier compared to projects that have been solved in the relatively distant past this may partially fall into question the ambiguous increase in spending on research the energy sector over time so that's all and I would like to conclude by stating the Czech Republic much updates and benefits from the diverse energy cooperation with international energy agencies so thank you for your attention thank you very much very much appreciated I think Czech Republic is quite a good example of a country that uses a very like database based data collection instead of surveys which is also a way to you do have a lot of work still on your side but it is a way to also simplify the data collection and make it more efficient and there's always something to consider depending on the country we compare with our technology agency which has always been collecting every year more detailed information so they share with us and there is no problem but as I mentioned we collect these data manually by project by project so it could be very complicated but it's the most practical way to collect for us because we need every project we just and we are interested in every project so we have a good overview what is happening and also but quantity is huge so as I said there can be some distortions but hopefully not so big yes yeah but thank you very much so I don't know but there is no question in particular for Sharon on the chat I think so then I would like to share the results as well as the last question that was hard for you which were would you be interested in collaborating with the IA on the roadmap specifically and we can see that yes thank you for not saying no we appreciate it but for all those countries that want to collaborate with us because we don't have the names please feel free to contact us so I will rewrite our RGND but also address them so we will be very happy to discuss this and have one on one meetings so we can go through and see where we can like the data collection can be improved so I don't know if there's any questions no so if we don't have any questions please feel free to please feel free oh we do have a question so yeah from Ali okay so this goes for all the countries even your data collection which type of data sources do you mostly use based on your experience and that is probably efficient sources so maybe you can start by answering give an answer to that so the data sources that we normally use are those provided by our partners in the National Energy Researcher Funders Forum so we use an Excel spreadsheet to collect data from our partners and we also input investments made by sustainable energy authority Ireland so then we collect within a centralized Excel spreadsheet but I was interested to hear what was happening in the Netherlands as well and in Casper in the Czech Republic so yeah I think we'll look at ways of maybe improving our data collection in the future thank you Irene maybe a colleague from the Netherlands and I hear you like to answer that is in the sense similar to what Arbeen said so indeed we for example the Dutch Research Council we are transferring data with it and most of the time you do that indeed with Excel that's a program that everybody knows it's easy but you have to have lots of what talks about what kind of standards do you want what kind of data you have to explain like this and this we need but most of the time indeed with Dutch agencies with the ministries etc also with sometimes customers that we do little surveys not that much because we try to make the most out of the original projects plans and that we get in PDF format and then we transport it to our system thank you Michele maybe Shimon I don't know I don't know if Shimon can hear us so the question was what's the main link data so yeah we can hear you yes as Michael said I fully agree with what has been said thank you very much so I see there's no additional questions so if you have like any questions in general maybe they can go to the countries or to us I forgot an email address you can contact us anytime you want for any support any question that you have either for us or for the country that presents today from the States and in the middle I'd like to give the closing words to Roberta thank you Suzy and thank you to all the speakers because they made it so useful for such a large community today discussion I think it's a great example of the importance of sharing experiences among the different in this case country experts where the situations are so different but where maybe we can also learn from each other and adopt a little bit more common approaches if useful and I think this is one of the main conclusions that sharing experiences is all the participants consensus that is a very important opportunity for support from organizations such as VIA and the mission innovation more international platforms and so we can commit to continue this type of collaborative approach and so I would like to thank the speakers and some of the others that were raised also raised particular attention including among the speakers I think the point of the application and that just as an example is there anything that in practice also could be shared more practically among the among camps so we can also look at this dimension in the future because we are also this opportunity is very important for us to understand what more we can do a different way we can do internationally to support your so precious work that you know is not always done with a full resources as needed so I also appreciate the request for online training that came through the Mantis thank you Susie for organizing the Mantis and it's a way of also involving some of the participants in the feedback and so we look maybe at this especially in this area this is not typically covered as an area in our sort of standard statistical training one point of that is actually not in today but it doesn't cover the FNB part so we need to consider how to take this request in account and you also take note of the nice volunteers so that anonymously still but we were looking at understanding who you are are appreciative of the opportunity to work with us on the following this tool for whatever either for establishing a data collection or for improving some areas based on the experience of others so we cannot together we are very very happy of hearing that this is the main objective also of this discussion today so I'm encouraging this five plus four people to get back to Susie with a really more clear request we'd be happy to get back to you also with a bilateral discussion so and and with that I think this was very fruitful discussion we hope that we made a little step forward in terms of helping the national work and ensure that this is a nice contribution in view of a really continued collaboration of this type we really take seriously the commitment on the opportunities of exchanges and collaboration also among experts in different countries this is really very useful so I would like to thank Susie the speakers and the participants for staying until the end so thank you very much and we continue working together thank you from the IEA thank you goodbye thank you very much thank you goodbye