Context within which Public Open Data projects are being supported by the European Commission, and other project examples
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Addressing modeIntegrated development environmentSlide ruleNumberAreaIntegrated development environmentOcean currentSet (mathematics)XML
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TwitterProjective planeOnline helpMusical ensembleCoordinate systemPresentation of a groupMereologyDataflowError messageLie groupClosed setService (economics)Data miningComputer animationDiagram
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DataflowEstimationStandard deviationScale (map)Computer networkSupercomputerService (economics)PredictionCondition numberLocal ringWeb portalProjective planeSet (mathematics)Open setService (economics)Order (biology)PredictabilityDataflowMeasurementAreaStandard deviationLevel (video gaming)Scaling (geometry)Different (Kate Ryan album)EstimatorMappingSoftware developerSupercomputerReal-time operating systemWorkstation <Musikinstrument>Group actionReduction of orderCoroutineWeb pageLocal ringSpiralComputer animation
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Green's functionMobile appMobile WebStatisticsDataflowTwin primeSkewnessSchmelze <Betrieb>Computer networkMeasurementPredictionSimulationChainData modelBuildingData managementFood energySimulationMeasurementTwin primeCondition numberRule of inferenceArrow of timeAlgebraic closureSoftwareLocal ringAreaWordTrailSinc functionTime zoneNetwork topologyInformationTwitterDigitizingProjective planeWorkstation <Musikinstrument>Different (Kate Ryan album)Mobile appCASE <Informatik>Normal (geometry)Context awarenessUniform resource locatorTerm (mathematics)Mobile WebMassConcentricFood energyMedical imagingSource codeData managementNatural numberReal-time operating systemOpen sourceVideoconferencingSign (mathematics)WebsiteShape (magazine)BuildingEndliche ModelltheorieMultimediaReal numberMathematicsCollisionIdentifiabilityInstance (computer science)Materialization (paranormal)System callArithmetic meanComputerTotal S.A.DataflowVideo gameMetreInternet service providerLevel (video gaming)MappingInterpolationVector potentialVisualization (computer graphics)System administratorWeb 2.0EstimatorParticle systemStrategy gameFrequencyMultiplication signSet (mathematics)Computer animation
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TelecommunicationGroup actionComputer networkInformationProjective planeEndliche ModelltheorieSupercomputerOpen setGame controllerArithmetic mean2 (number)Computer animation
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YouTubeDigital signalAddressing modeProjective planeSelf-organizationPresentation of a groupAnalytic continuationEmailMultiplication signGame theoryComputer animation
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Transcript: English(auto-generated)
00:10
And so my name is Mark Vellum-Muscat, and I'm a project manager in the field of telecommunications at the newly formed European Health and Digital Executive Agency,
00:21
which is also known as Hadea. Now I say newly because until recently, the digital sector of Hadea formed part of a different agency that was known as INEA, and which has since ceased to exist. And as far as my project management work goes, I'm responsible for projects that fall in the sectors of public open data
00:41
and cybersecurity. And I'm here today to follow up a little bit from the previous presentation by Daniele, so as to explain a little bit about what my agency does, and to give you some examples of how public open data policy work translates into projects on the ground, such as the one funding the project
01:01
connected to this conference, the Geo-Harmonizer Project. So the agency I work for is one of six executive agencies all based in Brussels. The agency was set up by the European Commission as of the current multi-annual financial framework to manage funding programs in the fields of health,
01:22
digital technologies, food safety, industry, and space. And our mission is to implement projects that strengthen Europe in these domains, thus helping European society to become more healthy, resilient, and fair, and for European industry to become more competitive.
01:40
Overall, the expected total budget to be managed by Hadea and the new MFF will amount to over 20 billion, which will be broken down as per this slide. But what we are interested in for the purpose of today's presentation in terms of digital content is the 800 million, and that is earmarked for the digital Europe program,
02:02
and the 1.7 billion earmarked for the digital part of the second Connecting Europe facility. In addition to this, we will also continue to manage the legacy programs related to the areas presented in the previous slide, such as the digital part
02:22
of the first Connecting Europe facility program, for which we were responsible for a budget of around 600 million, and from which funding for the likes of a geo-harmonizer is derived. 410 projects are currently being implemented, and 310 have already been closed.
02:41
So whilst the program is closed now from the planning perspective, meaning that there'll be no new calls under CEF 1, we're about halfway through the program in terms of implementation. So new calls will be available under the Connecting Europe facility 2 and the DEP program that Daniele mentioned in his presentation.
03:04
Now, in terms of objectives, the main aim of the telecoms part of the Connecting Europe facility was to facilitate cross-border interaction between public administrations, businesses, and citizens from different European countries, in which very different standards
03:22
or national systems may have been deployed. Now, in order to facilitate a cross-border interaction, supported projects under the Connecting Europe facility telecom were required to contribute to the creation of a European ecosystem of interoperable and interconnected digital services.
03:42
As a simple example, consider the movement of health data across national borders. In order to ensure the continuity of care and safety of citizens seeking healthcare outside of their home country, the CEF telecom is funding projects under the eHealth digital service infrastructure.
04:02
And this will therefore facilitate exchanges of health data and the provision of cross-border e-prescription and patient summary services. Now, there's several DSIs in a variety of sectors, but all follow the same structure. They're made up of a core service platform,
04:21
which acts as a bridge between different national solutions and systems, and then by generic services shown here on the left that help national systems to connect to and interact with the core service platform. So the GeoHarmonizer project, as an example, is participating in the development of generic services
04:42
in the field of public open data. So there's 15 different DSIs. DSIs is the acronym I use for digital service infrastructures. There's 15 currently being managed under the CEF telecom, which cover a wide range of different sectors
05:01
in areas such as justice, cybersecurity, health, cultural heritage, and of course, public open data, which has received 4.2 million euros so far over 35 projects. So turning now to public open data more specifically,
05:21
which is the DSI connected to today's conference. As mentioned before, all DSIs are made up of core service platforms and by generic services, and public data is no different. In this case, the core service platform is the European data portal,
05:40
which acts as a one-stop shop to provide harmonized access to data sets created and managed by public bodies in the member states. With regards to the generic services, all projects funded under this DSI contribute to the availability of harmonized content at the EU level and to its cross-border and cross-domain reuse.
06:03
In other words, our projects under public open data generate data sets covering very diverse areas such as biodiversity, air quality data, geospatial data, and they generate services addressing several public policy areas of key importance,
06:21
such as forest fires control, smart farming, mobility and air pollution monitoring, or even the digitalization of cultural resources. Another interesting point to note about public open data is that some technologies being employed by public open data projects
06:41
are currently trending topics, such as the linked open data paradigm, high-performance computing, big data analytics, and artificial intelligence and machine learning algorithms. So out of the 35 projects receiving funding, seven of them have already been finalized,
07:02
and the generated data sets have been uploaded to the European data portal, covering the areas shown in the slide, which include the environment, geospatial data, meteorological data, research and innovation, transport, science and technology,
07:21
and government and the public sector. With the current ongoing projects, however, we expect this number to grow both on areas covered, as well as the number of data sets available. Now, to give you some more concrete examples of some of our projects, I've enlisted the help of some of our project coordinators
07:41
who've kindly helped me in preparing the following part of my presentation. So the first project I would like to present is called the Trefer project, which brings together 10 partners from Italy and Spain to develop innovative and sustainable services, combining air quality, weather conditions, and traffic flows.
08:02
So the motivation behind the project lies in the concern that European nations have about air quality, which is estimated to cause around 400,000 deaths a year in Europe. Since the main source of air pollution includes road traffic, domestic heating, and industrial combustion, the project focuses on the impact of traffic
08:23
on urban air quality. In this respect, the project aims to provide citizens and decision makers with tools to rapidly understand, forecast, and improve the air quality conditions at the urban level. So far, it's been implemented in six EU cities
08:40
of varying sizes, which shows the flexibility and replicability of the program. The cities are Zaragoza, Firenze, Modena, Livorno, Santiago de Compostela, and Pisa. So from its outset,
09:01
the project aimed at estimating the level of pollution on an urban scale to achieve four main results, which include the definition of a standard set of metadata, which would be able to be represented in urban air quality maps, the provision of real time estimations of air pollution
09:21
in the participating cities on an urban scale. And in order to provide such a service, a set of low cost air quality sensors have been deployed and their measurements have been combined with measurements by official city regulatory air quality stations in order to build informative maps
09:42
of the different levels of pollution in the urban areas. Another achievement was the development of a service for predictions of urban air quality based on weather forecast and traffic flows.
10:02
And this service makes use of high performance computing technologies in order to compute the estimation of the diffusion of pollutants in the urban area. And lastly, the project sought to publish open data sets describing urban air quality maps, prediction maps,
10:21
and traffic flows in the six EU cities covered by the project. And in this respect, more than 800 data sets have been published on local open data portals, and more than 500 appear on the European data portal. Moreover, the project has developed a set of mobile apps,
10:42
web apps, and map visualization tools for providing citizens and public administrations with real time information on the estimated levels of pollution in the urban areas and allowing them to explore air pollution forecasts and analyze new scenarios. These apps have a strong potential
11:01
to improve the quality of life of these urban areas by helping policymakers identify trends, seasonal events, abnormal behaviors, and detecting possible hotspots. They also make citizens aware of the strong impact that traffic has on air quality.
11:28
So two main methodologies have been used in the project, and one for air quality monitoring and one for air quality forecasting. For air quality monitoring, a new urban air quality sensor network has been installed
11:43
in Santiago de Compostela, Zaragoza, and Modena, and the air quality sensor networks in Pisa, Firenze, and Livorno have been enriched with new sensors. A total amount of 162 new low-cost air quality sensors
12:06
for monitoring nitric acid, nitrogen dioxide, carbon monoxide, and ozone have been integrated thanks to this project. And next, given that low-cost sensors
12:20
require a constant monitoring and a precise calibration to provide air pollutant concentrations, different calibration strategies based on AI techniques were compared and tested during the project in all cities. And then after a collocation period where sensors were placed
12:40
near the official air quality stations in the cities to be trained, they were then moved to different locations in the urban areas to measure the air quality at local conditions. And then finally, based on the measurements provided, the low-cost sensors, so the measurements provided,
13:01
based on the measurements provided by the low-cost sensors and using interpolation techniques, the project was able to release real-time urban air quality maps for the six cities roughly every 10 to 15 minutes. Now, in terms of the air quality forecasting method,
13:23
simulation models have been used based on weather conditions and traffic emissions. So to emulate urban traffic flows, a traffic model has been employed based on real-time data collected by traffic sensors. Essentially, the traffic model creates a digital twin of the road network
13:42
and traffic flows of the city in semi-real time. Then, given historical traffic flows in each road of the city and the urban vehicle fleet, it was possible to automatically compute the total nitrogen oxide emissions for each road of the network.
14:02
After this, an open-source simulation software was employed to simulate how emitted particles move in the air considering winds, weather conditions, and the shape of the buildings in the urban area. And the model also took into account the nitrogen oxide emissions generated by traffic flows
14:22
and also other emission sources such as house heating, waste management, energy consumption, et cetera. And it was then able to produce a dispersion map that shows nitrogen oxide concentration values on an urban grid of two to four meters for every hour
14:43
for the following 48 hours. So that was the Trafer project. And the trafer.eu website contains all information about the project and dissemination activities carried out. And moreover, a Twitter account and a YouTube channel
15:02
provides multiple multimedia materials. And in case of specific questions, don't hesitate to contact either myself or the project coordinator. This brings me to the second project that I'd like to present to you today, which is called the Grapevine Project,
15:21
which is a project that aims at providing open information to farmers potentially affected by diseases that affect grapeyards so that they can apply more effective treatments. The project brings together seven partners from Spain and Greece and through the use of existing open data,
15:42
high performance computing and data infrastructures, the project's creating predictive models based on deep learning techniques to improve the prevention and control of mildew and other grapevine diseases in the wine cultivation sector. So a bit of motivation behind the project.
16:02
According to Eurostat, grapevines were grown in EU on 3.2 million hectares of land in 2015 with 2.4 million wine grower holdings and representing about 45% of the world's total area where grapevines are grown. However, grapevines can be affected
16:21
by many kinds of pests and diseases which can be devastating on the crops. So the need thus arose for the development of a proper protection method against these pests together with sustainable production systems. So the idea is that better monitoring and early reaction against highly destructive diseases
16:42
will enable a decrease in the amount of fungicide and the number of protective treatments. So in view of this, the project proposes a predictive model based on a series of different agroclimatic and biological variables to assess risk
17:01
and predicting the evolution of pests and providing knowledge to decision makers. The hope is that the results will enable farmers to focus the treatments at the appropriate moments so as to reduce the expansion of pathogens. So in order to fulfill its goal,
17:20
data is collected from several different sources including onsite through the phytosanitary surveillance network of Aragon, through sensors for capturing spores and insects, through the project partners' own meteorological stations, through open data sources such as satellite imaging
17:42
from Copernicus, hyper spectral and multispectral image capturing and weather forecasts. So all this data then feeds into the high-performance computing system through a data manager and data is processed through the different models to provide
18:01
the outputs for the forecasts. The resulting output is a map with the risk level for the different pests per cultivation plot. So the farmers can apply the corresponding phytosanitary products or pesticides only in the areas at risk.
18:22
In terms of benefits and impact, the project contributes to sustainable agriculture by promoting environmentally friendly methods of farming, such as less widespread use of pesticides, by improving food safety and consumer protection and by increasing profitability
18:41
through the optimization costs. At the same time, the development by the project of replicable and adaptable IT services in the sector opens the door for the use of open data and other data infrastructure technologies to be applied to other fruit and vegetable crops in the agricultural sector.
19:02
And it also provides an opportunity for other regions in Europe facing similar issues to replicate the services. Like in the previous example, the project website, grapevine-project.eu contains all information about the project and the dissemination activities
19:22
carried out. There's also a Twitter account and a YouTube channel where you can get more multimedia materials. And in case of specific questions, do not hesitate to contact me or the project coordinator. So that brings me to the end of my presentation. It's been a privilege for me to join you today.
19:42
And I look forward to receiving any questions you may have at the time specified by Tom. And thanks again to the organizers for the invitation to speak today. And I wish you continued success in the continuation of your conference and in the continuation of your project. Thank you very much.
20:02
Thank you, Mark. Yes, you cannot see the people, but there's about, I think, close to 40 people in the conference center. And there's also about, it's 27 online.
20:20
So we have time for one question. Any questions? Yes, maybe I will hit in the question. How does the Hadiya, so what do you do with the projects after they finish? Do you have something like,
20:42
so after the project's finished, do you still take projects or do you keep track, like universities keep track of students through alumni network or something? Do you have something like that? Yeah, we continue to monitor them beyond the end. For a short while, not forever.
21:02
But the hope is that, so it all begins with the policy, as Daniela explained. Then it comes to us for the implementation. The hope is that the results are then used by the policymakers again. And I often get questions from different commission services asking about the results of projects
21:21
long after they've closed. So yes, but this isn't an eternal question. Monitoring.