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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.

This document is an incremental update of deliverables “D5.2 Cross-sector Policy Lifecycle Management: Design and Open Specification 1”, September 2020, and “D5.4 Cross-sector Policy Lifecycle Management: Design and Open Specification 2”, August 2021. It details the specification of the tools and technologies used to model Public Policies (PPs) as well as the design of the policy development toolkit (including the visualization framework), and the mechanisms required for compilation of policy collections to perform cross-sector analysis. The purpose of this series of deliverables is to keep track of the specifications throughout the project and update them as the project progresses. This deliverable is the last of the series, has been released in M32 (August 2022) of the project, and its main objective is to describe the approach to the policy modelling and validation that will support policy makers in the design, evaluation, optimization and adaptation of policies. The Policy Development Toolkit (PDT), along with the Policy Model Editor (PME), constitute the front-end of the PolicyCLOUD platform, which allows policy makers to create, update and validate policies. The PDT backend, which hides the complexity of storing the data models into persistent storage, implements the services that the front-end uses to display the content to the user and provide the necessary user experience., 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].

This document is the final version of the Use Case Scenarios Definition & Design. It provides an updated version of the use case scenarios description, and completes the information presented there. In addition, this deliverable collects the refined versions of the visualization requirements and KPIs per use case. KPIs are classified in three categories: business, technical and scenario related. The modelled policies will be realized / implemented and monitored through the Policy Development Toolkit against these KPIs. The use cases have been defined taking into account the information collected from the different stakeholders participating in the pilots, as well as from the previous experience and know-how of the project partners. Several workshops have been organized by the project to gather feedback and requirements from relevant stakeholders. This document is the basis for the definition of the main features to be provided by PolicyCLOUD tools and models in the different pilots’ implementation.

This document is the second of the series of deliverables that will detail the evaluation process. In particular, this report details the outcomes of the evaluation of the PolicyCLOUD technologies and the benefits they provide to the use cases obtained in the co-creation & evaluation workshops. This document will feed back to the architecture specification. D6.14 explains the implementation of the evaluation methodology differentiating between Impact Analysis (IA) evaluations and Quality Validations (QV). The implementation has been carried out during different workshops for each use case. As an introduction, the Public Policies Implementation Process is described, considering how the PolicyCLOUD project contributes to this aim at the different stages of this process, presenting also the way the policy definition and implementation process is linked to the evaluation methodology proposed in this document. The most important improvement from Deliverable D6.5 is the implementation and the results of the evaluation for the different uses cases. The impact analysis reported by the policy makers, highlights that the main problems they face, are lack of data, inaccurate data and lack of standards. This is a major barrier to implementing new policies in any field. In addition, data is decentralised and fragmented and very difficult to access. All this makes the quality of data very low and unreliable. In general, we have obtained valuable feedback from the point of view of quality assessment of the platform. This feedback will guide the technical developments during 2022. Indicatively, some of the feedback we have received request for the environment to have the capability to export results, to provide more than one graph or type of graphs per scenario, to enable the comparison of information, to provide better labelling and data explicability, and to translate the environment into the local language.

This document is the second of the series of deliverables that will detail the evaluation process. In particular, this report details the outcomes of the evaluation of the PolicyCLOUD technologies and the benefits they provide to the use cases obtained in the co-creation & evaluation workshops. This document will feed back to the architecture specification. D6.14 explains the implementation of the evaluation methodology differentiating between Impact Analysis (IA) evaluations and Quality Validations (QV). The implementation has been carried out during different workshops for each use case. As an introduction, the Public Policies Implementation Process is described, considering how the PolicyCLOUD project contributes to this aim at the different stages of this process, presenting also the way the policy definition and implementation process is linked to the evaluation methodology proposed in this document. The most important improvement from Deliverable D6.5 is the implementation and the results of the evaluation for the different uses cases. The impact analysis reported by the policy makers, highlights that the main problems they face, are lack of data, inaccurate data and lack of standards. This is a major barrier to implementing new policies in any field. In addition, data is decentralised and fragmented and very difficult to access. All this makes the quality of data very low and unreliable. In general, we have obtained valuable feedback from the point of view of quality assessment of the platform. This feedback will guide the technical developments during 2022. Indicatively, some of the feedback we have received request for the environment to have the capability to export results, to provide more than one graph or type of graphs per scenario, to enable the comparison of information, to provide better labelling and data explicability, and to translate the environment into the local language.

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 document contains the analysis of the state of the art of the baseline technologies that will be used in the scope of the project. Moreover, the elicitation of the requirements coming both from the use cases and the technological partners is included. This deliverable summarizes the work that has been done in the scope of T2.1 (���Requirements Elicitation & State of the Art Analysis���). It is the third and last version 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. 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 third and last version depicts the final technical requirements that have been identified after the delivery of the first two versions of the scientific reports, providing an extended version of the user scenarios, the definition of their datasets, along with an extensive survey regarding the regulatory constraints. As mentioned in the previous versions 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 successful demonstration of the initial prototype that took place during this period (January 2021 ��� June 2021), being also demonstrated in the first review of the project, gathering additional information coming from the engagement of end users of the domain that provided valuable input, which is reflected in this last version of the deliverable. Moreover, a bottom-up approach has been applied, whose objective is to additionally complement and identify/analyse the technical requirements, which 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., This deliverable is submitted to the EC, not yet approved.

This document is an incremental update of deliverable D5.2 [1] and details the specification of the tools and technologies used to model Public Policies (PPs) as well as the design of the policy development toolkit (including the visualization framework), and the mechanisms required for compilation of policy collections to perform cross-sector analysis. The purpose of this series of deliverables is to keep track of the specifications throughout the project and update them as the project progresses. This deliverable has been released in M20 (August 2021) of the project, and its main objective is to describe the approach to the policy modelling and validation that will support policy makers in the design, evaluation, optimization and adaptation of policies. The Policy Development Toolkit (PDT), along with the Policy Model Editor (PME), constitute the front-end of the PolicyCLOUD platform, which allows policy makers to create, update and validate policies. The PDT backend, which hides the complexity of storing the data models into persistent storage, implements the services that the front-end uses to display the content to the user and provide the necessary user experience.

This document is an incremental update of deliverable D5.2 [1] and details the specification of the tools and technologies used to model Public Policies (PPs) as well as the design of the policy development toolkit (including the visualization framework), and the mechanisms required for compilation of policy collections to perform cross-sector analysis. The purpose of this series of deliverables is to keep track of the specifications throughout the project and update them as the project progresses. This deliverable has been released in M20 (August 2021) of the project, and its main objective is to describe the approach to the policy modelling and validation that will support policy makers in the design, evaluation, optimization and adaptation of policies. The Policy Development Toolkit (PDT), along with the Policy Model Editor (PME), constitute the front-end of the PolicyCLOUD platform, which allows policy makers to create, update and validate policies. The PDT backend, which hides the complexity of storing the data models into persistent storage, implements the services that the front-end uses to display the content to the user and provide the necessary user experience., This deliverable is submitted to the EC, not yet approved., {"references":["PolicyCLOUD. D5.2 \"Cross-sector Policy Lifecycle Management Design and Open Specification 1\". Armend Duzha et al. 2020.","Gagnon, M. L., Labonté, R. (2010) \"Framing Health and Foreign Policy: Lessons for Global Health Diplomacy\", Globalization and Health, Vol. 6 (14). https://doi.org/10.1186/1744-8603-6-14","Patton, C.V., and Sawicki, D.S., (1993) \"Basic Methods of Policy Analysis and Planning\", Vol. 7. Prentice Hall Englewood Cliffs, NJ.","Scharpf, F.W., (1997) \"Games Real Actors Play: Actor-centered Institutionalism in Policy Research\". Westview Press.","Moutselos, K., Kyriazis, D., Diamantopoulou, V., Maglogiannis, I., (2018) \"Trustworthy Data Processing for Health Analytics Tasks\", IEEE International Conference on Big Data, p. 3774-9.","Angular, https://angular.io","Angular Material, https://material.angular.io/","AM charts, https://www.amcharts.com/","Java 8, https://docs.oracle.com/javase/8/docs/technotes/guides/language/index.html","JSR-331, https://jcp.org/en/jsr/detail?id=311","JSR-339, https://jcp.org/en/jsr/detail?id=339","JSR-370, https://jcp.org/en/jsr/detail?id=370","Jersey, https://eclipse-ee4j.github.io/jersey/","PolicyCLOUD. D3.3 \"PolicyCLOUD Societal and Ethical Requirements and Guidelines\". Alice Audino at al. 2020."]}

This document provides the first update to the Conceptual Model and Reference Architecture of PolicyCLOUD (the initial document has been submitted as Deliverable D2.2). This second version (Deliverable D2.6) provides the definition of the overall architecture and its components, focuses on the integration aspects of the architecture and presents example scenarios and associated data sources from the project���s Use Cases including end-to-end data path analysis. The final update of the deliverable will be published in M30. A second version of the PolicyCLOUD Conceptual Model & Reference Architecture (originally submitted as Deliverable D2.2) is presented in this document. The final update of the deliverable will be submitted in M30. 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. The main focus of the updates of the document is on the Integration in PolicyCLOUD which 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���., This deliverable is submitted to the EC, not yet approved., {"references":["Apache Mesos, \"http://mesos.apache.org/\"","IBM Cloud Object Storage, https://www.ibm.com/cloud/object-storage","PolicyCLOUD. D3.1 - Cloud Infrastructure Incentives Management and Data Governance Design and Open Specification 1. Ledakis, Giannis. 2020.","Load balancing, Moleculer \"https://moleculer.services/docs/0.14/balancing.html\"","Fault tolerance, Moleculer \"https://moleculer.services/docs/0.14/fault-tolerance.html\"","J. Kreps, N. Narkhede, and J. Rao, \"Kafka: A distributed messaging system for log processing\", NetDB, 2011.","Malone, Thomas W., Robert Laubacher, and Chrysanthos Dellarocas. \"The collective intelligence genome.\" MIT Sloan Management Review 51, no. 3 (2010): 21.","PolicyCLOUD. D3.4 - Cloud Infrastructure Incentives Management and Data Governance Design and Open Specification 2. Ledakis, Giannis. 2021.","PolicyCLOUD, D3.3 PolicyCLOUD's Societal and Ethical Requirements & Guidelines, Audino Alice, 2020.","Gehra B., Leiendecker J. & Lienke G. (2017), White Paper. Compliance by Design: Banking's Unmissable Opportunity, \"https://image-src.bcg.com/Images/Compliance-by-Design-Dec2017_tcm9- 198779.pdf\" retrieved 2020-11-28.","Balboni P., Taborda Barata M., Botsi A. & Francis K. (2019), Accountability and Enforcement Aspects of the EU General Data Protection Regulation – Methodology for the Creation of an Effective Compliance Framework and a Review of Recent Case Law, Indian Journal of Law and Technology, 15(1), 102-259.","JSON, \"http://json-ld.org/\"","Yamada, I., & Shindo, H. (2019). Neural attentive bag-of-entities model for text classification. arXiv preprint arXiv:1909.01259.","Attardi, G., Buzzelli, A., & Sartiano, D. (2013). Machine Translation for Entity Recognition across Languages in Biomedical Documents. In CLEF (Working Notes).","Olszewski, Robert. (2001). Generalized feature extraction for structural pattern recognition in time-series data."]}

This document is the second version of D6.3 Use Case Scenarios Definition & Design, submitted in September 2020. It provides an updated version of the use case scenarios description, and completes the information presented there. In addition, this deliverable collects the refined versions of the visualization requirements and KPIs per use case. KPIs are classified in three categories: political, technical and business. The modelled policies will be realized / implemented and monitored through the Policy Development Toolkit against these KPIs. The use cases have been defined taking into account the information collected from the different stakeholders participating in the pilots, as well as from the previous experience and know-how of the project partners. Several workshops have been organized by the project to gather feedback and requirements from relevant stakeholders. This document is the basis for the definition of the main features to be provided by Policy Cloud tools and models in the different pilots’ implementation.

This document is the second version of D6.3 Use Case Scenarios Definition & Design, submitted in September 2020. It provides an updated version of the use case scenarios description, and completes the information presented there. In addition, this deliverable collects the refined versions of the visualization requirements and KPIs per use case. KPIs are classified in three categories: political, technical and business. The modelled policies will be realized / implemented and monitored through the Policy Development Toolkit against these KPIs. The use cases have been defined taking into account the information collected from the different stakeholders participating in the pilots, as well as from the previous experience and know-how of the project partners. Several workshops have been organized by the project to gather feedback and requirements from relevant stakeholders. This document is the basis for the definition of the main features to be provided by Policy Cloud tools and models in the different pilots’ implementation.

The purpose of this deliverable is to describe the end to end (E2E) scenarios that will explore how users will effectively use the Policy Cloud platform. The given scenarios will describe the different steps required in order to perform the modelling of the policy. The scenarios will also include details of the necessary configurations and settings needed to determine the output of the visualization module. This document is the first of a series of deliverables that will detail the implementation and experimentation process. The E2E scenarios will be used to experiment the adaptability of the Policy Development Toolkit (PDT)in conjunction with several use case-based scenarios. This document will include a single scenario from each respective pilot and further details on alternative scenarios will be included in the upcoming D6.10 Use Case Scenarios Definition & Design M16in deliverable in April 2021. The experimentation process for software usually consists of four phases: definition, planning, operation and interpretation. This approach was adopted for the design of the E2E scenarios. Taking the four phases into consideration the E2E scenarios descriptions can be used to achieve the definition and planning stages. The E2E helps explore how well the PDT can be adapted for various situations. At the current stage of the project there needs to be a clear definition of the end users experience and expected results. In order to achieve this, the visualizations and analytical tools will help create the full E2E scenario. The contents within deliverable D2.2 Conceptual Model & Reference Architecture and D2.4 State of the Art & Requirements Analysis was used to specify the available analytical tools that were available for the user stories. This approach was necessary to avoid the E2E scenarios becoming unattainable in terms of implementation to the PDT. There was also input provided from D6.3 Use Case Scenarios Definition & Design as well in relation to the use case scenarios. The final result of these series of deliverables will be the implementation and experimentation of the use cases based on the E2Escenarios detailed within this report.

This document is the first of a series of deliverables that will detail the evaluation process. It presents the proposed methodology, the time planning ofthe evaluation methods proposed withinthe project lifecycle, the specific objectives they pursue and their expected impact. It provides an initial approach to the evaluation methodology differentiating between Impact Analysis (IA)evaluations which are intended to assess the expected impact of the project and how evidence based policies contribute to the policy modelling process and its implementation, and Quality Validations (QV), which try to determine how to validate the innovative tools and modules developed within the project, specifically the Policy Development Toolkit(PDT). Asanintroduction the Public Policies Implementation Process is described, considering how the Policy Cloud project contributes to this aim at the different stages of this process, presenting also the way the policy definition and implementation process is linked to the evaluation methodology proposed in this document. The Evaluation and Recommendation Process section begins with a brief introduction to the key points onwhichthemethodologyis based:impact assessment, technology acceptanceand validation of the interaction and usability aspects. With these concepts in mind, the proposed methodology for evaluation and recommendation is presented.It consistsof different interventions throughout theproject to evaluate, both the expected impact of Policy Cloudas a project that could contribute to evidence-based policy development and, to thisend, whetherthe solutions provided withintheproject, includingthe Policy Cloud PDT, will contribute to this function and to what extent. Finally, in the Use Cases Evaluation section, the particularities for the evaluation of each use case will be defined. At this stage of the project it is not necessary to have the basis of the evaluation very closely established, but it is necessary to clearly identify the most relevant policy makers for each use case, and their functions. The final result of these deliverables, once the different phases will be completed, isto understand how the Policy Cloud PDT integrates into the working practices of the policy makers and how they use the toolkit. To gather all the data and insights they will be interviewed to reportthe benefits, unexpected inconveniences and all the possible outcomes to be able to identify best practices and lessons learnt to achieve new improvements.

This document is the first of a series of deliverables that will detail the evaluation process. It presents the proposed methodology, the time planning of the evaluation methods proposed within the project lifecycle, the specific objectives they pursue and their expected impact. It provides an initial approach to the evaluation methodology differentiating between Impact Analysis (IA)evaluations which are intended to assess the expected impact of the project and how evidence based policies contribute to the policy modelling process and its implementation, and Quality Validations (QV), which try to determine how to validate the innovative tools and modules developed within the project, specifically the Policy Development Toolkit(PDT). As an introduction the Public Policies Implementation Process is described, considering how the Policy Cloud project contributes to this aim at the different stages of this process, presenting also the way the policy definition and implementation process is linked to the evaluation methodology proposed in this document. The Evaluation and Recommendation Process section begins with a brief introduction to the key points on which the methodology is based: impact assessment, technology acceptance and validation of the interaction and usability aspects. With these concepts in mind, the proposed methodology for evaluation and recommendation is presented. It consists of different interventions throughout the project to evaluate, both the expected impact of Policy Cloud as a project that could contribute to evidence-based policy development and, to this end, whether the solutions provided within the project, including the Policy Cloud PDT, will contribute to this function and to what extent. Finally, in the Use Cases Evaluation section, the particularities for the evaluation of each use case will be defined. At this stage of the project it is not necessary to have the basis of the evaluation very closely established, but it is necessary to clearly identify the most relevant policy makers for each use case, and their functions. The final result of these deliverables, once the different phases will be completed, isto understand how the Policy Cloud PDT integrates into the working practices of the policy makers and how they use the toolkit. To gather all the data and insights they will be interviewed to report the benefits, unexpected inconveniences and all the possible outcomes to be able to identify best practices and lessons learnt to achieve new improvements.

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 session showcases examples of Smart Government initiatives across Europe that integrate AI, Digital Twins and Big Data solutions. A focus lies on co-creating services with the use of the 3 aforementioned technologies. Thethe three panel members are:Mr. Lieven Raes Smart City expert at Information Flanders, Belgium, Ms. Leire Orue-Echevarria Arrieta Coloud Technologies expert at TECNALIA R&I, Spain, andIskra Yovkova from the Municipality of Sofia wholeadsPolicy Cloud's use case of urban policy making through analysis of crowdsourced data.

This deliverable compiles the aspects related to the Development of the pilot cases of the Policy Cloud project.

This deliverable compiles the aspects related to the Development of the pilot cases of the Policy Cloud project.

The PolicyCLOUD Conceptual Model & Reference Architecture are presented in this report (Deliverable D2.2) released in M8 of the project. Updates of the deliverable will be published in M18 and M30. 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 model the 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 is exploited for the definition of forward-looking policies which are dynamically adapted and methodically focused on the population that are applied on. Five layered building blocks 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 includes a Data Governance Model, Protection and Privacy Enforcement and the Ethical Framework. The architecture allows for integrated acquisition and analytics, as also data fusion with processing and initial analytics combined with seamless analytics on hybrid data at rest. A number of scenarios developed for the Use Cases of PolicyCLOUD are also included in the document, in order to serve as end-to-end examples for the demonstration of the data ingest flow and data exploitation and for the analysis of the processing and data transformations along the complete data path. Problem statement, main objectives, Key Performance Indicators and data sources sections are provided for each scenario in this first release of Deliverable 2.2 while the detailed end-to-end data path analysis will be prepared during the completion of the first prototypes in M10 and will be included in the next update of the document in M18. The overall architecture and Use Case scenarios have been discussed by all partners (i) during the kick-off meeting, (ii) during the development of the preliminary specification as an internal report made available to partners and (iii) during specialized internal workshops integrating constituent architectures and specialized internal workshops discussing Architecture integration and end-to-end Use Case journey. Finally, conclusions are provided in the last section of the document.

This deliverable will include the specification of the tools used to model and design the policies as well as the design of the policy development toolkit, and the mechanisms required for the compilation of policies collections to perform cross-sector analysis and policy making. It will also architect the tools to identify groups for data-driven policies experimentation and adaptation.

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. D5.2 Roadmap for Future Research Directions. Mureddu, Francesco. 2019.","EC Europa. Funding & Tenders. https://ec.europa.eu/info/funding- tenders/opportunities/portal/screen/opportunities/topic-details/dt-governance-12-2019-2020, retrieved 2020-05-20","Strategyzer. Value Proposition Canvas. https://www.strategyzer.com/canvas/value-proposition-canvas, retrieved 2020-06-01","Mind Tools. SWOT Analysis. https://www.mindtools.com/pages/article/newTMC_05.htm, retrieved 2020- 06-01","EC Europa. A European strategy for data. https://ec.europa.eu/digital-single-market/en/policies/building- european-data-economy, retrieved 2020-06-05","EC Europa. From the PSI to the Open Data Directive. https://ec.europa.eu/digital-single-market/en/public- sector-information-psi-directive-open-data-directive, retrieved 2020-06-05","EC Europa. The European Cloud Initiative. https://ec.europa.eu/digital-single-market/en/european-cloud- initiative, retrieved 2020-06-05","EC Europa. Entrepreneurship and SMEs. https://ec.europa.eu/growth/smes_en, retrieved 2020-05-06","EC Europa. Digital Innovation Hubs. https://ec.europa.eu/digital-single-market/en/digital-innovation-hubs, retrieved 2020-06-08","EC Europa. 2050 long-term strategy https://ec.europa.eu/clima/policies/strategies/2050_en, retrieved 2020-06-01","Harward Business Review. What Data Scientist really Do. https://hbr.org/2018/08/what-data-scientists- really-do-according-to-35-data-scientists, retrieved 2020-06-15","EC Europa. The new European Interoperability Framework. https://ec.europa.eu/isa2/eif_en, retrieved 2020-05-09","Markets and Markets. Big Data Market. https://www.marketsandmarkets.com/Market-Reports/big- data-market-1068.html, retrieved 2020-05-20","Market Data Forecast. Europe Big Data Market. https://www.marketdataforecast.com/market- reports/europe-big-data-market, retrieved 2020-05-20","Grand View Research. Big Data Market Size. https://www.grandviewresearch.com/industry- analysis/big-data-industry, retrieved 2020-05-20","Market Research Future. Data Analytics Market Size. https://www.marketresearchfuture.com/reports/data-analytics-market-1689, retrieved 2020-05-20","Lisbon Council. The European Data Market Monitoring Tool Report. http://datalandscape.eu/sites/default/files/report/EDM_D2.5_Second_Report_on_Policy_Conclusions_final_ 13.06.2019.pdf#page=50&zoom=100,92,97, retrieved 2020-06-15","Gold Stein Research. Europe Big Data Market. https://www.goldsteinresearch.com/report/europe-big- data-market-trends-size-forecast, retrieved 2020-05-20","BDVA. SRIA. http://www.bdva.eu/sites/default/files/EuropeanBigDataValuePartnership_SRIA__v3.pdf, retrieved 2020-06-14","InkWood Research. Europe Big Data Market. https://www.inkwoodresearch.com/reports/europe-big- data-market/, retrieved 2020-05-20"]}