Context within which Public Open Data projects are being supported by the European Commission, and other project examples
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Muskat, Mark Vella
Mark Vella Muskat is the project officer at European Health and Digital Executive Agency (HaDEA). In this talk, he showed examples of Public Open Data projects supported by the European Commission.
 And so my name is Mark Vela Muscat and I'm a project manager in the field of telecommunications at the newly formed European Health and Digital Executive Agency, 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 Hadea 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 and cybersecurity. And I'm here today to follow up a little bit from the previous presentation by Daniela 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 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, 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. 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. So 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 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 of the first connecting Europe facility program, for which we are responsible for a budget of around 600 million and from which funding for the likes of a geoharmonizer is derived. 410 projects are currently being implemented and 310 have already been closed. So whilst the program is closed now from the planning perspective, meaning that there will be no new calls under SEF 1, we are 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 Daniela mentioned in his presentation. 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 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. 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 health care outside of their home country, the SEF telecom is funding projects under the eHealth digital service infrastructure. And this will therefore facilitate exchanges of health data and the provision of cross-border ePrescription 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, 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 Geo-Harmonizer project as an example is participating in the development of generic services in the field of public open data. So there's 15 different DSIs. DSIs is what is the acronym I use for digital service infrastructures. There's 15 currently being managed under the SEF telecom, which cover a wide range of different sectors 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, 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, which acts as a one-stop shop to provide harmonized access to datasets 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. In other words, our projects under public open data generate datasets covering very diverse areas such as biodiversity, air quality data, geospatial data, and they generate services addressing several public policy areas of key importance, such as at 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 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, and the generated datasets 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, 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 datasets 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 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 Traffare project, which brings together 10 partners from Italy and Spain to develop innovative and sustainable services, combining air quality, weather conditions and traffic flows. 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 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 60 cities of varying sizes, which shows the flexibility and replicability of the program. The cities are Saragosa, Firenze, Modena, Livorno, Santiago de Compostela and Pisa. So from its outset, 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 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 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. 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, 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, 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 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. So two main methodologies have been used in the project, 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 in Santiago de Compostela, Zaragoza, and Modena. And the air quality sensor networks in Pisa, Ference, and Livorno have been enriched with new sensors. A total amount of 162 new low-cost air quality sensors for monitoring nitric acid, nitrogen dioxide, carbon monoxide, and ozone have been integrated thanks to this project. Next, given that low-cost sensors 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. Then after a collocation period where sensors were placed 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, 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, 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 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. 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. The model also took into account the nitrogen oxide emissions generated by traffic flows 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 for the following 48 hours. So that was the Traffair project and the traffair.eu website contains all information about the project and dissemination activities carried out. Moreover, a Twitter account and a YouTube channel 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, 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, 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. 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 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 will enable a decreasing 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 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, data is collected from several different sources, including on site 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 from Copernicus, hyperspectral 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 the outputs for the forecasts. The resulting output is a map with the risk level for the different pest per cultivation plot so the farmers can apply the corresponding Phytosanitary products or pesticides only in the areas at risk. 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 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 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 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 and I look forward to receiving any questions you may have at the time specified by Tom. 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. Thank you, Mark. Yes. You cannot see the people but there's about, I think, close to 40 people at the conference center and there's also about, it's 27 online. So we have a time for one question. Any questions? Maybe I will hit in the question. How does the Hadea, so what do you do with the projects after they finish? Do you have something like, so after the projects finish, do you still track 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 to them beyond the end for a short while, not forever. The hope is that, so it all begins with the policy as Daniela explained, then it comes to us for the implementation and the hope is that the results are then used by the policy makers again. And I often get questions from different commission services asking about the results of projects long after they've closed. So yes, but this isn't an internal monitoring. Let's get started. Let's get started. OK. Go ahead. Got it. OK. Let's go ahead and do some monitoring. OK. Let's go. OK. OK. OK. OK. OK. OK. OK. OK. OK. OK.
 And so my name is Mark Vela Muscat and I'm a project manager in the field of telecommunications  at the newly formed European Health and Digital Executive Agency, 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 Hadea 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 and cybersecurity.  And I'm here today to follow up a little bit from the previous presentation by Daniela  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 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, 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.  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.  So 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 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 of the first connecting  Europe facility program, for which we are responsible for a budget of around 600 million  and from which funding for the likes of a geoharmonizer is derived.  410 projects are currently being implemented and 310 have already been closed.  So whilst the program is closed now from the planning perspective, meaning that there will  be no new calls under SEF 1, we are 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  Daniela mentioned in his presentation.  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 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.  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 health care  outside of their home country, the SEF telecom is funding projects under the eHealth digital  service infrastructure.  And this will therefore facilitate exchanges of health data and the provision of cross-border  ePrescription 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, 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 Geo-Harmonizer project as an example is participating in the development of generic  services in the field of public open data.  So there's 15 different DSIs.  DSIs is what is the acronym I use for digital service infrastructures.  There's 15 currently being managed under the SEF telecom, which cover a wide range of different  sectors 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, 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, which acts as a  one-stop shop to provide harmonized access to datasets 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.  In other words, our projects under public open data generate datasets covering very diverse  areas such as biodiversity, air quality data, geospatial data, and they generate services  addressing several public policy areas of key importance, such as at 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 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,  and the generated datasets 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, 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 datasets 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 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 Traffare project, which brings  together 10 partners from Italy and Spain to develop innovative and sustainable services,  combining air quality, weather conditions and traffic flows.  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 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 60 cities of varying sizes, which shows the flexibility  and replicability of the program.  The cities are Saragosa, Firenze, Modena, Livorno, Santiago de Compostela and Pisa.  So from its outset, 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 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 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.  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, 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, 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 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.  So two main methodologies have been used in the project, 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  in Santiago de Compostela, Zaragoza, and Modena.  And the air quality sensor networks in Pisa, Ference, and Livorno have been enriched with  new sensors.  A total amount of 162 new low-cost air quality sensors for monitoring nitric acid, nitrogen  dioxide, carbon monoxide, and ozone have been integrated thanks to this project.  Next, given that low-cost sensors 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.  Then after a collocation period where sensors were placed 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, 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, 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 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.  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.  The model also took into account the nitrogen oxide emissions generated by traffic flows  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 for the following 48 hours.  So that was the Traffair project and the traffair.eu website contains all information about the  project and dissemination activities carried out.  Moreover, a Twitter account and a YouTube channel 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, 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, 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.  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 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  will enable a decreasing 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 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, data is collected from several different sources, including  on site 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 from Copernicus, hyperspectral 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 the outputs for  the forecasts.  The resulting output is a map with the risk level for the different pest per cultivation  plot so the farmers can apply the corresponding Phytosanitary products or pesticides only  in the areas at risk.  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 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 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 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 and I look forward to receiving any questions  you may have at the time specified by Tom.  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.  Thank you, Mark.  Yes.  You cannot see the people but there's about, I think, close to 40 people at the conference  center and there's also about, it's 27 online.  So we have a time for one question.  Any questions?  Maybe I will hit in the question.  How does the Hadea, so what do you do with the  projects after they finish?  Do you have something like, so after the projects finish, do you still track 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 to them beyond the end for a short while, not forever.  The hope is that, so it all begins with the policy as Daniela explained, then it comes  to us for the implementation and the hope is that the results are then used by the policy  makers again.  And I often get questions from different commission services asking about the results  of projects long after they've closed.  So yes, but this isn't an internal monitoring.  Let's get started.  Let's get started.  OK.  Go ahead.  Got it.  OK.  Let's go ahead and do some monitoring.  OK.  Let's go.  OK.  OK.  OK.  OK.  OK.  OK.  OK.  OK.  OK.  OK.
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