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This deliverable has been released in December 2022, at M36 of the project, and its main objective is to specify the final integration results between the PolicyCLOUD components. This deliverable will follow the methodology of D6.2 and D6.8 that were respectively submitted in M12 (December 2020) and M24 (December 2021) which have two main pillars: Define common practices for integration and validation of the outcomes of the project Detail the cloud environment the project will make use of to demonstrate the results Regarding the former, GitLab will be the base code repository for the project, where the project already owns an organizational account. Over GitLab [1], the trunk-based development branching policy has been applied, as we considered it the most suitable policy given the project characteristics. Also, GitLab’s issue reporting tool has been adopted, as it is fully integrated with GitLab’s features. The test bed to support the demonstrators has been deployed over EGI’s (EGI) infrastructure where flexibility is one of the critical features. This deliverable abstractly incorporates all the changes and implementations that WP2, WP3, WP4 and WP5 had made during the second year of the project. More details about the components and the actual implementation can be found in the related WP deliverables [7] [8] [9]. In detail, the schemas of the data have been finalized so the standard version that we defined initiated the data import to the repository of PolicyCLOUD. Moreover, the infrastructure (IaaS) and the platform deployment (PaaS/ Serverless) have been restructured and reshaped based on the latest needs of the components. EGI deployed the new flavour of PolicyCLOUD to the Openstack Infrastructure and IBM made the proper changes to the Openwhisk middleware for the serverless and other services. The related WP deliverables highlight detailed information and instructions for each component change that in total orchestrate the PolicyCLOUD engine.

This deliverable is the fourth version of the Data Management Plan (DMP) for the PolicyCLOUD project, released in month 36 (December 2022). Previous versions on which this document is also based and from where the base contents have been updated are D1.2 Data Management Plan [1], D1.3 Data Management Plan M12 [2], and D1.4 Data Management M24 [3]. It provides a final description of the project’s datasets according to FAIR principles. The overall purpose of this document is to support the data management lifecycle for all data that will be collected, stored, processed or generated by the project in order to maximise its access, according to the H2020 Pilot on Open Research Data (ORDP) in which the project participates. A Data Management strategy is defined to provide the general rules and mechanisms for the access management of project data. Each dataset in the project is identified and described and information is provided on to which extent it is standard compliant, and how the FAIR principles are applied. A total of 10 datasets have been identified in this version of the deliverable, being most of them data that will be collected and processed by the three remaining pilot use cases. Other datasets are mostly stakeholders’ information and public deliverables produced within the project scope., This deliverable is submitted to the EC and not yet approved.

The third and final version of the Cloud Infrastructure, Incentives Management and Data Governance software prototype includes the cloud gateways and APIs, the cloud provisioning mechanisms, the implemented version of the algorithms as well as the data governance tools according to the D3.1 [1], D3.4 [2] and D3.7 [3] specifications and is built upon the first and second versions of the prototype described in D3.2 [4] and D3.5 [5] respectively. The prototype’s cloud infrastructure is supported by RECAS-BARI and is utilized by EGI through cloud gateways. These gateways allow the prototype to gather data from heterogenous data sources, such as Twitter and the global terrorism database and have integrated microservices to serve the needs of the different PolicyCLOUD pilots. The final version of the Incentives Management tool is also provided in this deliverable. This final version has been integrated with the Policy Development Toolkit (PDT) and has been deployed in the EGI Cloud. This third version of the prototype also includes the latest updates of the ABAC based access control mechanism and the Keycloak integration. This is broken down to 8 key components that have been combined to provide fine-tuned and secure access control and authentication. Specifically, Keycloak has been integrated with the Marketplace, the Gateways and the PDT and custom access policies have been developed for the gateways microservices. Finally, both the introduction of a XACML editor to ease access policies creation and the integration of EGI Check-in, an alternative way to authenticate to the prototype with academic and social credential, enhance the user experience in the PolicyCLOUD platform, This deliverable is submitted to the EC, not yet approved.

The third and final version of the PolicyCLOUD Conceptual Model & Reference Architecture (originally submitted as Deliverable D2.2 in September 2020 [20] with the second version submitted as D2.6 in June 2021 [21]) is presented in this document. The PolicyCLOUD Conceptual Model presents the overall project concept along 2 main axes. Along the first data axis PolicyCLOUD delivers Cloud Gateways and APIs to access data sources and adapt to their interfaces so as to simplify interaction and data collection from any source. Along the second main axis, the Policies Management Framework of PolicyCLOUD allows the definition of forward-looking policies as well as their dynamic adaptation and refocusing to the population they are applied on. Based on the project’s offerings along the main two axes of the Concept, five main building blocks (in a layered manner) define its Architecture: (1) The Cloud Based Environment and Data Acquisition, (2) Data Analytics, (3) the Policies Management Framework, (4) the Policy Development Toolkit and (5) The Marketplace. The architecture also includes a Data Governance Model, Protection and Privacy Enforcement and the Ethical Framework as depicted in Figure 2. The architecture allows for integrated data acquisition and analytics. It also allows data fusion with processing and initial analytics (see 7.6.5) as well as seamless analytics (see 7.6.6) on hybrid data at rest. Integration in PolicyCLOUD follows three directions: (i) architecture integration, (ii) integration with the cloud infrastructure and (iii) integration with Use Case scenarios through the implementation of end-to- end scenarios. Additional integration activities take place along the two frameworks of PolicyCLOUD, (a) the Data Governance model, protection and privacy enforcement mechanism and (b) the Ethical and Legal Compliance framework. For end-to-end data path analysis we have used two Use Case scenarios: (i) the scenario of Use Case 1: “Radicalization incidents” and the scenario of Use Case 2: “Visualization of negative and positive opinions on social networks for different products”. The new updates in this final document provide the following: Analysis of how External Frameworks can be integrated with PolicyCLOUD (section 7.6.11.4); Presentation of the overall Conceptual View and architecture of the Data Marketplace (section 7.9.1); Outline of the mechanisms developed for initialising the Policy Development Toolkit with Policy Model components and the visualization of results (section 7.8.3); Analysis of the Ethical and Legal Compliance Framework positive interventions to the PolicyCLOUD architecture, including the addition of specific fields/parameters to the registration Application Programming Interfaces to be populated with details regarding each individual analytics tool and dataset/data source (section 7.5); Presentation of the integration of the Data Governance model, protection and privacy enforcement mechanisms with the Policy Development Toolkit, the cloud gateways and the marketplace (section 7.10.2), and within the same context, the integration of EGI-Check-in with Keycloak including the integration of the Data Governance model, protection and privacy enforcement mechanisms with the Kubernetes cluster. The document also addresses the Reviewers’ comments to the previous version of the deliverable (Deliverable D2.6), included in the second review report. In order to address these comments, additional updates of Deliverable D2.7 include: (i) links to specific user/stakeholder requirements (D2.5), (ii) descriptions and implementation details for the two remaining pilot Use Cases (Sofia and London) and (iii) reference to EOSC and to the role of the Conceptual Model & Reference Architecture document for the identification of the relevant services and of their providers, and description of the onboarding process based on Deliverable D3.4 [22]., This deliverable is submitted to the EC, not yet approved.

The third and final version of the PolicyCLOUD Conceptual Model & Reference Architecture (originally submitted as Deliverable D2.2 in September 2020 [20] with the second version submitted as D2.6 in June 2021 [21]) is presented in this document. The PolicyCLOUD Conceptual Model presents the overall project concept along 2 main axes. Along the first data axis PolicyCLOUD delivers Cloud Gateways and APIs to access data sources and adapt to their interfaces so as to simplify interaction and data collection from any source. Along the second main axis, the Policies Management Framework of PolicyCLOUD allows the definition of forward-looking policies as well as their dynamic adaptation and refocusing to the population they are applied on. Based on the project’s offerings along the main two axes of the Concept, five main building blocks (in a layered manner) define its Architecture: (1) The Cloud Based Environment and Data Acquisition, (2) Data Analytics, (3) the Policies Management Framework, (4) the Policy Development Toolkit and (5) The Marketplace. The architecture also includes a Data Governance Model, Protection and Privacy Enforcement and the Ethical Framework as depicted in Figure 2. The architecture allows for integrated data acquisition and analytics. It also allows data fusion with processing and initial analytics (see 7.6.5) as well as seamless analytics (see 7.6.6) on hybrid data at rest. Integration in PolicyCLOUD follows three directions: (i) architecture integration, (ii) integration with the cloud infrastructure and (iii) integration with Use Case scenarios through the implementation of end-to- end scenarios. Additional integration activities take place along the two frameworks of PolicyCLOUD, (a) the Data Governance model, protection and privacy enforcement mechanism and (b) the Ethical and Legal Compliance framework. For end-to-end data path analysis we have used two Use Case scenarios: (i) the scenario of Use Case 1: “Radicalization incidents” and the scenario of Use Case 2: “Visualization of negative and positive opinions on social networks for different products”. The new updates in this final document provide the following: Analysis of how External Frameworks can be integrated with PolicyCLOUD (section 7.6.11.4); Presentation of the overall Conceptual View and architecture of the Data Marketplace (section 7.9.1); Outline of the mechanisms developed for initialising the Policy Development Toolkit with Policy Model components and the visualization of results (section 7.8.3); Analysis of the Ethical and Legal Compliance Framework positive interventions to the PolicyCLOUD architecture, including the addition of specific fields/parameters to the registration Application Programming Interfaces to be populated with details regarding each individual analytics tool and dataset/data source (section 7.5); Presentation of the integration of the Data Governance model, protection and privacy enforcement mechanisms with the Policy Development Toolkit, the cloud gateways and the marketplace (section 7.10.2), and within the same context, the integration of EGI-Check-in with Keycloak including the integration of the Data Governance model, protection and privacy enforcement mechanisms with the Kubernetes cluster. The document also addresses the Reviewers’ comments to the previous version of the deliverable (Deliverable D2.6), included in the second review report. In order to address these comments, additional updates of Deliverable D2.7 include: (i) links to specific user/stakeholder requirements (D2.5), (ii) descriptions and implementation details for the two remaining pilot Use Cases (Sofia and London) and (iii) reference to EOSC and to the role of the Conceptual Model & Reference Architecture document for the identification of the relevant services and of their providers, and description of the onboarding process based on Deliverable D3.4 [22].

Evidence-based policymaking (EBP) is not new, but its face is changing rapidly in the data- driven public sector, mainly due to the following factors: Evolving meaning of evidence: from scientific opinions and policy evaluations to data- driven insights drawn from modelling and simulations coming from a multitude of data types and sources: from open data to citizen-generated data;. New actors coming into play: EBP is no longer reserved to social scientists, statistical offices and policy evaluators, but now open to data scientists and data savvy public servants; More attention for agenda setting and policy design, while EBP traditionally focuses on policy implementation and evaluation; Different relation between policy and evidence: from reactive and ex-post to proactive and ex-ante; From “What worked?” To “What will work?” The added value of a more data-driven approach to evidence-based policymaking seems clear, but can it fulfil its promises in practice? And what is needed to scale up the use of policy prediction? This paper presents evidence from the Data-Driven Policy Clusterto shed light on the questions above. It discusses the critical roles of interoperability and data sharing for the advancement of policy prediction in EBP. Then, concrete applications in different policy domains show what policy prediction looks like in practice and what challenges policy stakeholders encounter. The paper concludes with policy recommendations on how data can be leveraged for policy prediction at a larger scale than what is currently the case: Start small, experiment, then scale fast through co-creation Foster a culture of innovation building on successful experiments and pilots Leverage European data sharing infrastructures to increase data availability and accessibility across policy areas and government levels Create trusted data spaces to facilitate data sharing by citizens and businesses Secure the necessary multidisciplinary resources Ensure European building blocks for data-driven policy prediction

This entry contains all the slides from the presentations given during theEvidence Based Policymaking in Europe Summit 2021 which was organised by the Data Driven Policy Clustertook place 9-10 December 2021.

This deliverable has been released in December 2021, at M24 of the project, and its main objective is to specify the updated integration results between the Policy Cloud components. So, this document is an updated version of the previous deliverable (D6.2 Integration of Results: Policy Cloud Complete Environment). This deliverable will follow the methodology of D6.2 that was submitted in M12 (December 2020) which has two main pillars: Define common practices for integration and validation of the outcomes of the project Detail the cloud environment the project will make use of to demonstrate the results Regarding the former, GitLab will be the base code repository for the project, where the project already owns an organizational account. Over GitLab, the trunk-based development branching policy has been applied, as we considered it the most suitable policy given the project characteristics. Also, GitLab’s issue reporting tool has been adopted, as it is fully integrated with GitLab’s features. The test bed to support the demonstrators has been deployed over EGI’s (EGI) infrastructure where flexibility is one of the critical features. This deliverable abstractly incorporates all the changes and implementations that WP2, WP3, WP4 and WP5 had made during the second year of the project. More details about the components and the actual implementation can be found to the relative WP deliverables.

This deliverable summarizes the work that has been currently done in the scope of T2.1 (“Requirements Elicitation & State of the Art Analysis”) until M12. It is the second of the series of deliverables of this task, whose main objective is to specify the use case scenarios, their involved datasets and their relevant user requirements as well as the system and technical requirements that are being imposed by the platform. As mentioned in the previous version of this deliverable, the purpose of this series is to track those requirements throughout the project and update them during the progress of the project. As a result, this second version depicts the additional technical requirements that have been identified after the delivery of the first versions of the scientific reports and provides an extended version of the user scenarios, the definition of their dataset, along with an extensive survey regarding the regulatory constraints. As mentioned in the first version of this deliverable, the approach that has been followed is twofold: A top-down approach has been followed with respect to the user requirements that were initially collected by the use case providers themselves, after specifying the business goals and objectives of the use case, along with a concrete definition of the scenario. In this version, additional requirements have been identified, after the first or pilot workshops that took place during this period (M07-M12) with the engagement of end users of the domain that provided valuable input, which is reflected in this version of the deliverable. However, as these workshops will continue to take place in the forthcoming period, the final version of the user requirements will be delivered in the third iteration of this document. Moreover, regarding the bottom-up approach, whose objective is to additionally complement and identify/analyse the technical requirements, they have been updated with respect to the progress that has been made by the work carried out in the technical work packages, which are focusing on the platform technological needs. The result of the analysis of both the outcomes of the pilot workshops and the scientific reports of the technical work packages is an updated list of measurable unambiguous requirements that will validate the initial design of the overall architecture of the Policy Cloud platform, focusing on serving all different needs of the various use cases of the project, with the target to be aligned with the requirements defined in this document. As the project is still progressing, a final version of this deliverable will help the architecture designers of the platform and its software developers to adjust and validate the overall architecture and its implementation accordingly, as the principles of the agile methodology demand. Moreover, as our scope is to continuously keep track with the latest technological advances and how we can take advantage of them while developing our platform, the analysis of the state-of-the-art analysis regarding the major technologies that are envisioned to be exploited has been extended, while this document maintains the list of several projects whose technological assets might be candidates to be incorporated in the overall solution. This deliverable has been released on M12 of the project, and its main aim is to specify the advanced description of the scenarios of the use cases, which drove the user requirements based on their perspective, and to provide a list of updated technical requirements, as foreseen by the technical partners that are being involved in the design of the overall architecture of the platform. A final version of this document will be released in M22.

This deliverable is the second version of the Data Management Plan (DMP) for the Policy Cloud project, provided in month 12 of the project. It provides a description of the project’s datasets according to FAIR principles. The overall purpose of this document is to support the data management lifecycle for all data that will be collected, stored, processed or generated by the project in order to maximise its access, according to the H2020 Pilot on Open Research Data (ORDP) in which the project participates. A Data Management strategy is defined to provide the general rules and mechanisms for the access management of project data. Each dataset in the project is identified and described and information is provided on to which extent it is standard compliant, and how the data will be available, accessible interoperable and reusable. A total of 20datasets have been identified in this version of the deliverable, being most of them data that will be collected and processed by the four pilot use cases. Other datasets are mostly stakeholders’ information and public deliverables produced within the project scope. This DMP is a living document and will be updated during the course of the project in new versions at M24 and M 36.

This deliverable has been released on M12 of the project, and its main objective is to specify the initial integration results between the Policy Cloud components. This deliverable has two main pillars: 1.-define common practices for integration and validation of the outcomes of the project, and 2.-detail the cloud environment the project will make use of to demonstrate the results. Regarding the former, GitLab will be the base code repository for the project, where the project already owns an organizational account. Over GitLab, the trunk-based development branching policy will be applied, as it is considered the most suitable policy according to the project characteristics. Also, GitHub’s issue reporting tool will be adopted, as it is fully integrated with GitHub’s features. The test bed to support the demonstrators will be deployed over EGI’s(EGI) infrastructure where flexibility is one of the main features. This deliverable abstractly incorporates all the changes and implementations thatWP2, WP3, WP4 and WP5 had made during the last year. More details about the components and the actual implementation can be found to the relative WP deliverables.

The first version of the Cloud Infrastructure, Incentives Management and Data Governance software prototype includes the cloud gateways and APIs, the cloud provisioning mechanisms, the implemented version of the algorithms as well as the data governance tools according to the D3.1 specifications. The prototype’s cloud infrastructure will be supported by RECAS-BARI and will be utilized by EGI through cloud gateways. These gateways allow the prototype to gather data from heterogenous data sources, such as from twitter and the global terrorism database. This first version of the prototype also includes an ABAC based access control mechanism suitable for Policy Cloud. This is broken down to 3 key components that can be combined, along with a test client, in order to demonstrate the access control capabilities to the use cases and to proceed with the definition of an accurate data model, based on the provided feedback. An updated version of this prototype will be delivered and documented at a later stage for deliverables D3.4 and D3.7.

This document is the first deliverable of WP3. It provides information about the work performed in tasks T3.1, T3.3, T3.4, and T3.6. As it covers various tasks, this document provides the information regarding a) cloud provisioning and utilization of cloud resources for PolicyCLOUD, b) the design of the cloud gateways and their APIs, c) the incentives management and identification in the scope of PolicyCLOUD, and d) the data privacy and governance mechanism. This document is the initial iteration of this report while two more iterations will be provided through the documents D3.4 (due on M20) and D3.7 (due on M32)

This deliverable is the initial version of the Data Management Plan (DMP) for the PolicyCLOUD project and it provides a first description of the datasets available according to FAIR principles. The overall purpose of this document is to support the data management lifecycle for all data that will be collected, processed or generated by the project in order to maximise its access, according to the H2020 Pilot on Open Research Data(ORDP) in which the project participates. A Data Management strategy is defined to provide the general rules and mechanisms for the access management of project data. Each dataset in the project is identified and described and information is provided on to which extent it is standard compliant, and how the data will be available, accessible interoperable and reusable. A total of 17 datasets have been identified in this first version of the deliverable, being most of them data that will be collected and processed by the four pilot use cases. Other datasets are mostly stakeholders’ information and public deliverables produced within the project scope. This DMP is a living document and will be updated during the course of the project in new versions at M12, M24 and M36.

Governments around the world face policy issues that require strategies and solutions using new technologies, access to data, new analytical tools and techniques and social crowdsourcing analysis for the successful implementation and acceptance of public policies and their later assessment. Data is a fundamental resource for carrying out all government activities and governments everywhere, and at all levels, are looking into the opportunities of data-driven innovation. In this context, the PolicyCLOUD environment will introduce a holistic methodology for policy modelling and management based on data, providing a toolkit that allows both stakeholders and engaged citizens to create policies by exploiting the PolicyCLOUD models and analytical tools on various datasets, contexts and policy models. The document D7.2: Market Analysis and Business Potentials is the first deliverable of the PolicyCLOUD business- related activities, setting the market context where the project’s results will be exploited and giving an initial insight about these results. Following the proven Atos’ “Innovation Hub Methodology”, that divides the project time period in two different phases (analysis and strategy), the Consortium has conducted several activities, notable amongst those is the Value Proposition Canvas Workshop. This was an interactive session during First General Assembly, held remotely on the 7th May 2020, focusing on the PolicyCLOUD core offering and stakeholders that could obtain the greatest value of the solution. Initial versions of deliverables D2.1: State of the art and requirement analysis and D2.2: Conceptual Model and Reference Architecture have given input to this deliverable, especially in the analysis of the technological context and in the identification of the project’s outcomes and their main features. The results of this deliverable D7.2 will in turn come back to the technical Work Packages (WPs) and will influence the architecture design with the aim of solving real problems that users have. Ultimately, this is the goal of the business activities of the project, ensure that the finished technology is usable, engaging end users from the initial phases of the project and facilitating its further use. The feedback expected from the Impact Creation Board in relation to this deliverable will be key to design next activities and to support project’s innovation., This version is submitted to the EC, not yet aproved, {"references":["IG POLICY CANVAS. 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