D5.3 Strategies towards sustainable open data ecosystems

Towards a sustainable Open Data ECOsystem D5.3 Strategies towards sustainable open data ecosystems This project has received funding from the European Unionʼs Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the authorʼs view and in no way reflect the European Commissionʼs opinions. The European Commission is not responsible for any use that may be made of the information it contains. D5.3 Strategies towards sustainable open data ecosystems Project Acronym ODECO Project Title Towards a sustainable Open Data ECOsystem Grant Agreement No. 955569 Start date of Project 01-10-2021 Duration of the Project 48 months Deliverable Number D5.3 Deliverable Title Strategies towards sustainable open data ecosystems Dissemination Level Public Deliverable Leader University of the Aegean (UAEGEAN) Submission Date 02-06-2025 Author Charalampos (Harris) Alexopoulos (UAEGEAN), Mohsan Ali (UAEGEAN) Co-author(s) Abdul Aziz (UNIZAR), Alejandra Celis Vargas (AAU), Ashraf Shaharudin (TU Delft), Caterina Santoro (KUL), Dagoberto Herrera (UNIZAR) Davide Di Staso (TU Delft), Giorgos Papageorgiou (FAROSNET S.A.) ,Héctor Ochoa Ortiz (UNICAM), Liubov Pilshchikova (TU Delft) María Elena López Reyes (AAU), Maria Ioanna Maratsi (UAEGEAN), Ramya Chandrasekhar (CNRS), Silvia Cazacu (KUL), Umair Ahmed (UNICAM) Document history Version # Date Description (Section, page number) Author & Organisation V0.0 29-08-2024 Kick-off meeting Charalampos (Harris) Alexopoulos (UAEGEAN), Mohsan Ali (UAEGEAN) V0.2 11-15-2024 Preliminary Toc and initial Methodology drafting Charalampos (Harris) Alexopoulos (UAEGEAN), Mohsan Ali (UAEGEAN) V0.3 13-12-2024 Initial inputs from the ESRs – extraction of key-elements Abdul Aziz (UNIZAR), Alejandra Celis Vargas (AAU), Ashraf Shaharudin (TU Delft) Caterina Santoro (KUL), Dagoberto Herrera (UNIZAR), Davide Di Staso (TU Delft), Giorgos Papageorgiou (FAROSNET S.A.) , Héctor Ochoa Ortiz (UNICAM), Liubov Pilshchikova (TU Delft), María Elena López Reyes (AAU), Maria Ioanna Maratsi (UAEGEAN), Mohsan Ali (UAEGEAN), Ramya Chandrasekhar (CNRS), Silvia Cazacu (KUL), Umair Ahmed (UNICAM) V0.4 05-01-2025 Inputs from the ESRS – explaining keystrategies Abdul Aziz (UNIZAR), Alejandra Celis Vargas (AAU), Ashraf Shaharudin (TU Delft) Caterina Santoro (KUL), Dagoberto Herrera (UNIZAR), Davide Di Staso (TU Delft), Giorgos Papageorgiou (FAROSNET S.A.) , Héctor Ochoa Ortiz (UNICAM), Liubov Pilshchikova (TU Delft), María Elena López D5.3 Strategies towards sustainable open data ecosystems Version # Date Description (Section, page number) Author & Organisation Reyes (AAU), Maria Ioanna Maratsi (UAEGEAN), Mohsan Ali (UAEGEAN), Ramya Chandrasekhar (CNRS), Silvia Cazacu (KUL), Umair Ahmed (UNICAM) V0.4a 30-01 - 2025 Inputs from the ESRs – Explaining Open Data Ecosystems components Abdul Aziz (UNIZAR), Alejandra Celis Vargas (AAU), Ashraf Shaharudin (TU Delft) Caterina Santoro (KUL), Dagoberto Herrera (UNIZAR), Davide Di Staso (TU Delft), Giorgos Papageorgiou (FAROSNET S.A.) , Héctor Ochoa Ortiz (UNICAM), Liubov Pilshchikova (TU Delft), María Elena López Reyes (AAU), Maria Ioanna Maratsi (UAEGEAN), Mohsan Ali (UAEGEAN), Ramya Chandrasekhar (CNRS), Silvia Cazacu (KUL), Umair Ahmed (UNICAM) V0.5 18-02-2025 First draft for review Charalampos (Harris) Alexopoulos - UAEGEAN, Mohsan Ali - UAEGEAN V0.5a 27-02-25 Revision of sections 1-4 and contributions to sections 5 - 7 Melanie Dulong de Rosnay, CNRS V0.5b 15-03-2025 Second draft. Processed feedback obtained from the peer review process Second draft. Integration of feedback received from beneficiaries and collected during Steering Supervisory Board meeting Charalampos (Harris) Alexopoulos - UAEGEAN, Mohsan Ali - UAEGEAN 20-03-2025 Review second draft Melanie Dulong de Rosnay, CNRS Joep Crompvoets, KU Leuven Andrea Polini, UNICAM Bastiaan van Loenen, TUD V0.6 31-03-2025 Final report Charalampos (Harris) Alexopoulos - UAEGEAN, Mohsan Ali - UAEGEAN 18-04-2025 Review draft final Melanie Dulong de Rosnay, CNRS Joep Crompvoets, KU Leuven Andrea Polini, UNICAM Bastiaan van Loenen, TUD V0.6 27-05-2025 Final report Charalampos (Harris) Alexopoulos - UAEGEAN, Mohsan Ali - UAEGEAN V0.8 28-05-2025 Approval Bastiaan van Loenen, TUD V1.0 02-06-2025 Final editing Danitsja van Heusden, TUD D5.3 Strategies towards sustainable open data ecosystems Table of Contents Abbreviations ..................................................................................................................................................................... 7 Executive summary .......................................................................................................................................................... 8 1 Introduction ............................................................................................................................................................... 9 1.1 Objectives ...................................................................................................................................................... 9 1.2 Key terms described .................................................................................................................................. 9 1.3 Structure of this deliverable .................................................................................................................10 2 Methodology ..........................................................................................................................................................11 2.1 Preliminary research ................................................................................................................................11 2.2 Data collection ..........................................................................................................................................11 2.3 Analysis.........................................................................................................................................................12 2.4 Synthesis of strategies and approaches ..........................................................................................13 3 Preliminary Research – Identification of Challenges ...............................................................................14 3.1 Current open data systems and challenges in the literature ..................................................14 3.2 Mapping the identified challenges in open data systems to the Open Data Ecosystemsʼ drivers 16 4 Data Collection .......................................................................................................................................................18 4.1 User driven strategies .............................................................................................................................18 4.2 Inclusive strategies...................................................................................................................................18 4.3 Circular strategies .....................................................................................................................................19 4.4 Skill based strategies ..............................................................................................................................19 5 Analysis - Sustainable Open Data Ecosystem Framework ....................................................................20 5.1 Designing the Open Data Ecosystem (ODE) framework ..........................................................20 5.2 Explaining sustainable open data ecosystem frameworkʼs components ...........................20 5.2.1 Open Data Ecosystem .......................................................................................................................20 5.2.2 Open data infrastructure .................................................................................................................21 5.2.3 Enabling environment .......................................................................................................................22 5.2.4 Governance ...........................................................................................................................................23 5.2.5 Resources ...............................................................................................................................................24 5.2.6 Stakeholder groups ............................................................................................................................25 5.2.7 Feedback/evaluation/redefinition ................................................................................................27 5.2.8 Data exchange .....................................................................................................................................27 5.2.9 Data quality ...........................................................................................................................................28 5.2.10 Collaboration and network effects ..............................................................................................29 5.2.11 Co-creation ...........................................................................................................................................31 5.2.12 Incentives ...............................................................................................................................................31 5.2.13 Value ........................................................................................................................................................32 5.2.14 New concepts (AI4Data and Data4AI) ........................................................................................33 5.3 Towards a framework of an ODE ........................................................................................................34 6 Analysis and Synthesis of Strategic Approaches .......................................................................................36 6.1 Strategies towards a user driven open data ecosystem ...........................................................36 6.1.1 Coordinated data stewardship across governmental and non-governmental actors 37 6.1.2 Enhancing open data platforms ...................................................................................................38 6.1.3 Establish effective feedback mechanisms .................................................................................38 D5.3 Strategies towards sustainable open data ecosystems 6.1.4 Develop a standardized open data portal interface to make the data publication process easier .........................................................................................................................................................38 6.1.5 Technical openness of the datasets .............................................................................................39 6.1.6 Empowering marginalized stakeholders: encouraging inclusive participation in ODEs 39 6.2 Strategies towards an inclusive open data ecosystem ..............................................................40 6.2.1 Enable multiple data access points .............................................................................................40 6.2.2 Facilitating data sharing among non-government data holders.....................................41 6.2.3 Building shared goals and values in an open data ecosystem .........................................41 6.2.4 Improving open data accessibility through user-friendly and multilingual design..42 6.2.5 Scoping local problems and building collaborative networks for open data solutions 42 6.2.6 Strategies for facilitating collaboration between domain experts and data experts 43 6.2.7 Adopting thematic classification of datasets from European Data Portal (EDP) .......43 6.2.8 Practical implementation of CARE and FAIR principles in open data legal frameworks 43 6.2.9 Establishing and implementing a national or supra-national consortium for open data sharing .............................................................................................................................................................44 6.2.10 Thematical annotation of open data ..........................................................................................44 6.2.11 Intermediation in providing tools for diverse datasets for data augmentation and quality enhancement ...........................................................................................................................................45 6.2.12 Low-code tools provision for data analysis ..............................................................................45 6.3 Strategies towards a circular ODE......................................................................................................45 6.3.1 Resource provision and support for small sized NGOs .......................................................46 6.3.2 Bridging stakeholders through boundary objects ................................................................46 6.3.3 Knowledge hubs for open data infrastructure and expertise exchange ......................47 6.3.4 Unlocking open data potential through value creation and networked interactions 47 6.3.5 Designing interactive and adaptive visualizations for dynamic ODEs ...........................48 6.3.6 Employing open data standards and formats .........................................................................48 6.3.7 Adopting scalable infrastructure technologies .......................................................................49 6.3.8 Assessing the implementation of interactive visualization tools for data exploration and comprehension .............................................................................................................................................49 6.3.9 Maximizing open data utilization through a purpose driven approach .......................50 6.3.10 Make training in data skills and literacy available .................................................................50 6.3.11 Adaptive engagement strategies .................................................................................................50 6.4 Integration of all strategies to the ODE framework clickable version .................................51 7 Recommendations ................................................................................................................................................53 7.1 Technical recommendations ................................................................................................................53 7.1.1 Improve discoverability by adopting thematic structured classification models ......53 7.1.2 Promoting standardization through linked open data for semantic interoperability 53 7.1.3 Establishing effective feedback mechanisms for ODE improvement.............................53 7.1.4 Standardizing open data portal interfaces to simplify data publication ......................53 7.1.5 Improve technical usability and openness ...............................................................................54 7.2 Governance recommendations...........................................................................................................54 7.2.1 Build shared goals and values by promoting shared culture ............................................54 D5.3 Strategies towards sustainable open data ecosystems 7.2.2 Scope local problems and build collaborative networks with local stakeholders ....54 7.2.3 Facilitate collaboration between domain experts and open data experts ..................54 7.2.4 Empowering marginalized stakeholders for inclusive ODEs .............................................54 7.2.5 Establishing a governance framework for open data validation .....................................55 7.2.6 Enabling data analysis through low-code tools and training ...........................................55 8 Limitations................................................................................................................................................................56 9 Conclusion ...............................................................................................................................................................57 References .........................................................................................................................................................................58 Annex 1 - Open Data Ecosystem Strategies ........................................................................................................67 List of figures Figure 1: ODECO deliverables and tasks relevant to this deliverable .......................................................... 9 Figure 2: Methodology for developing strategies and approaches to ODEs .........................................11 Figure 3: Methodology for the designing the open data ecosystem framework .................................13 Figure 4: Identified challenges in open data systems and dealing with them through designing and strategizing ODE framework .............................................................................................................................16 Figure 5: components of a sustainable Open Data Ecosystem framework .............................................20 Figure 6: A possible visualisation of an open data ecosystem framework ..............................................35 Figure 7: Overarching strategies per ODE driver ...............................................................................................36 Figure 8: User driven open data ecosystem strategies and additional approaches connected to a strategy ...............................................................................................................................................................................37 Figure 9: Inclusive open data ecosystem strategies and additional approaches connected to a strategy ...............................................................................................................................................................................40 Figure 10: Circular open data ecosystem strategies and additional approaches connected to a strategy ...............................................................................................................................................................................45 Figure 11: Screenshot of the clickable version developed to easily navigate in the open data ecosystem framework, drivers and corresponding strategies ......................................................................52 Figure 12: Clicking intermediaries on the open data ecosystem framework, popups a full list of key elements per drivers, and strategies per drivers. ...............................................................................................52 List of tables Table 1: A sample strategy for inclusive open data ecosystems ..................................................................12 Table 2: Workshops conducted for framework design and identification of strategies .....................12 Table 3: Identified challenges in open data systems and mapping them to the open data ecosystemʼs drivers ........................................................................................................................................................17 Table 4: Strategies for user driven open data ecosystems .............................................................................67 Table 5: Strategies for inclusive open data ecosystems ..................................................................................72 Table 6: Strategies for circular open data ecosystems .....................................................................................77 Table 7: Strategies for skill-based open data ecosystems ..............................................................................85 D5.3 Strategies towards sustainable open data ecosystems 7 Abbreviations D Deliverable EDP European Data Portal ESR Early-Stage Researcher FAIR Findable, Accessible, Interoperable, and Reusable Principles ICT Information and Communication Technologies NG non-government NGO Non-governmental Organisation NLP Natural Language Processing NPO Non-Profit Organisation OD Open Data ODE Open Data Ecosystem ODP Open Data Platform ODI Open Data Infrastructure OGD Open Government Data WP Work Package Nr Partner Partner short name Country Beneficiary 1 Technische Universiteit Delft TU Delft Netherlands 2 Katholieke Universiteit Leuven KUL Belgium 3 Centre National de la Recherche Scientifique CNRS France 4 Universidad de Zaragoza UNIZAR Spain 5 Panepistimio Aigaiou UAEGEAN Greece 6 Aalborg Universitet AAU Denmark 7 Università degli Studi di Camerino UNICAM Italy 8 Farosnet S.A. FAROSNET S.A. Greece Partner organisations 1 7eData 7EDATA Spain 2 Digitaal Vlaanderen DV Belgium 3 City of Copenhagen COP Denmark 4 City of Rotterdam RDAM Netherlands 5 CoC Playful Minds CoC Denmark 6 Derilinx DERI Ireland 7 ESRI ESRI Netherlands 8 Maggioli S.p.A MAG Italy 9 National Centre of Geographic Information CNIG Spain 10 Open Knowledge Belgium OKB Belgium 11 SWECO SWECO Netherlands 12 The government lab GLAB United States of America 13 Agency for Data Supply and Infrastructure ADSI Denmark 14 GFOSS Open Technologies Alliance GFOSS Greece 15 Inno3 Consulting IC France 16 Regione Marche RM Italy 17 Open Data Institute ODI United Kingdom D5.3 Strategies towards sustainable open data ecosystems 8 Executive summary This deliverable aims at developing an overarching sustainable framework and strategy by addressing the key challenges faced by current open data systems, which are supplier-driven, linear, exclusive, and effort-based, limiting their ability to create sustainable and value-generating Open Data Ecosystems (ODEs). The main objective of this deliverable was to design an overarching sustainable ODE framework and to propose strategies to pave the way towards a user driven, circular, inclusive, and skill-based Open Data Ecosystem (ODE). To achieve such an overarching sustainable framework and strategies, previous research done in this project has been utilized from different perspectives, such as for bridging the open data supply and demand, for incorporating the different aspects required for ensuring circularity, and for integrating requirements key to ensuring inclusiveness. Building on the work of Van Loenen et al. (2021) and further developing the whole ODECO project based on this work, the deliverable proposes a transformative vision for open data systems by incorporating the ODE drivers, namely user driven, inclusive, circular, and skill-based, towards sustainable and value-creating ODEs. The goal is to move away from fragmented, governmentcentric open data systems toward ecosystems that maximize value for all stakeholders— government, NGOs, researchers, businesses, and citizens alike—while ensuring fair value distribution and fostering innovation. The deliverable introduces a comprehensive ODE Framework that integrates components such as stakeholder groups, data provision and usage, driving forces, governance, open data infrastructure, and enabling environments. By utilizing the ODE framework components, we have illustrated their interactions, portraying the complexity of an ODE. We also identified 83 strategies to further ODE development. Further synthesis gives 29 distinct strategies. This resulted in eleven recommendations for institutions and actors producing, using or otherwise contributing to the ODE, to be implemented in order to support sustainable ODEs. These 11 recommendations may improve usability and accessibility, quality, and participation through a set of 5 technical and 6 governance strategies. In conclusion, achieving a sustainable ODE requires a multifaceted approach that balances inclusivity, circularity, user driven engagement, and skill-based empowerment. This deliverable represents a step forward in the evolution of ODEs. By addressing the gaps in current systems and leveraging the proposed strategies, the framework and strategies outlined in this study pave the way for a more inclusive, equitable, and innovative open data future. Ultimately, this work contributes to the broader goal of harnessing open data as a powerful tool for societal and economic transformation. D5.3 Strategies towards sustainable open data ecosystems 9 1 Introduction Numerous studies have examined the concepts of open data and how building open data ecosystems (ODE) can thrive open data initiatives. The motivation behind each study differs with respect to the ODEs elements. In a collective paper, the consortium has explained the underlining causes that hinders ODEs to achieve sustainability and value creation (see Van Loenen et al., 2021). It was identified that current open data systems are supplier-driven, exclusive, linear, and effortbased (Van Loenen et al., 2021). Transforming current open data systems to user driven, inclusive, circular, and skill-based can drive the way towards value-creating and sustainable ODEs1. 1.1 Objectives The objective of this deliverable is to develop an overarching sustainable framework/strategy to arrive at a user driven, circular, inclusive, and skill based ODE. It integrates ODECO Work Packages (WP) WP2, WP3, WP4, T5.1 and T5.2 results, and builds further on the results from the individual research projects of the fifteen Early Stage Researchers (ESRs) (see Figure 1). Figure 1: ODECO deliverables and tasks relevant to this deliverable 1.2 Key terms described In this report, we use several rather complex terms frequently. Here we provide their description or definition: • Open data ecosystem: “a cyclical, sustainable, demand-driven environment oriented around agents that are mutually interdependent in the creation and delivery of value from open data” (Van Loenen et al. (2018, p. 5). • Open data ecosystem drivers: (1) user driven, (2) circular, (3) inclusive, and (4) skill-based: o User driven: User driven focus on the user needs and demands for the data, such as what data they need, in which format, how is the potential use and reuse. o Inclusive: an inclusive ODE allows any actor to participate in the ODE: “The inclusion of problem owners, i.e., citizens, public administrators, interest groups with a clear view of critical problems to solve, would instead call for an open and broader process based on participation and co-creation. The interaction with industry partners, civil society, and governments is essential to ensure that open data initiatives are inclusive. Inclusive ODEs address society's needs at large by ensuring individuals and disadvantaged groups (including elderly, women, disabled) have access to and benefit 1 Please note that the skill-based driver was not part of the granted ODECO project. It was introduced later in the project (see Van Loenen et al., 2021) D5.3 Strategies towards sustainable open data ecosystems 16 To summarise the analysis, the limitations of current open data systems—such as being supplierdriven, exclusive, linear, and effort-based—hinder their sustainability and value creation (Van Loenen, Zuiderwijk, Vancauwenberghe, et al., 2021). Challenges related to data discoverability, stakeholder collaboration, governance, and quality further impede the effectiveness of open data initiatives (Kitsios et al., 2017; Wiener et al., 2016). Additionally, issues such as policy fragmentation, lack of feedback loops, and insufficient incentives for data sharing make it difficult to establish a well-functioning open data landscape (Zuiderwijk et al., 2014, 2016). The absence of a structured, transparent, and collaborative framework within open data systems results in inefficient data use, limiting its potential for economic growth, policy improvements, and innovation (Gupta et al., 2020; Lnenicka et al., 2022). Therefore, transitioning towards a sustainable ODE is not just beneficial but essential. By integrating governance structures, stakeholder engagement, data value mechanisms, and sustainability measures, ODEs can address the shortcomings of traditional open data systems, ensuring that open data is not only published but effectively utilized to generate economic, social, and policy-driven impact. The full list of challenges of current open data systems is presented in Figure 4. The challenges were extracted from the literature (Van Loenen et al., 2021; Gupta et al., 2020; Lnenicka et al., 2022; Kitsios et al., 2017; Wiener et al., 2016; Runeson et al., 2021; Styrin et al., 2017; van Schalkwyk et al., 2016; Zuiderwijk et al., 2016; Zuiderwijk et al., 2014). Furthermore, these limitations are technical, organizational, and governanceand policy-related. In the end, we proposed that an ODEs framework, along with actionable strategies and approaches, can effectively address these challenges and lead to a sustainable and value-creating ODE. Figure 4: Identified challenges in open data systems and dealing with them through designing and strategizing ODE framework 3.2 Mapping the identified challenges in open data systems to the Open Data Ecosystemsʼ drivers The identified challenges have been categorized based on ODE drivers, aiming to find solutions through these drivers. For example, how open data drivers address these challenges and by what means. Specifically, the user driven approach in an ODE enhances adaptability, scalability, and innovation by ensuring data is discoverable, high-quality, and efficiently shared with stakeholders as per their needs. Some challenges are cross-cutting and require continuous efforts to be resolved, such as infrastructure, resources, governance, and skills; this we call horizontal challenges. The vertical challenges are those that we can easily map to the ODE drivers somehow by understanding their scope and relevance. For instance, challenges with adaptability, scalability, D5.3 Strategies towards sustainable open data ecosystems 17 data discoverability, quality, and innovation-oriented data are mapped to the user driven driver of the ODE. Similarly, the remaining current open data systems challenges as mentioned in Figure 4 are mapped to the user driven, inclusive, and circular drivers of the ODE. The mapping institutions come from the explanations of each challenge as described throughout this section. Table 3: Identified challenges in open data systems and mapping them to the open data ecosystemʼs drivers Vertical challenges User driven Inclusive Circular Adaptable Transparent Sustainable Scalable Collaborative Value driven Discoverable data Clear Boundaries Clear roles of involved stakeholders Efficient Data sharing Involving organizations Interoperable Quality Not limited to governments Economic value-creation Innovation oriented Clear Boundaries Horizontal challenges Infrastructure Resources Governance Skill based D5.3 Strategies towards sustainable open data ecosystems 18 4 Data Collection We analysed ODECO reports and the research results of the ODECO ESRs to identify the components and their connections within the ecosystem framework, and propose strategies. Descriptions of proposed strategies were requested from the ESRs and supervisors for each element (we considered any important remarks, recommendations, conclusions, or key outcomes regarding ODE drivers and subsequent benefits related to these drivers as key elements) for the purpose of their inclusion and implementation in the ODE framework. In this way, our aim was to identify and understand the components of the ODE framework and their relevance to the ODE drivers and how they may further contribute to strategies for achieving a sustainable ODE. The data collected included identified strategies stemming from conducted individual ODECO research and collective deliverables, which directly or indirectly improve the ODEs. The summary of the strategies for corresponding ODE drivers are explained below (for detailed information we refer to annex 1 Table 4, Table 5, Table 6, and Table 7). 4.1 User driven strategies The strategies outlined for enhancing ODEs emphasize the importance of collaboration, shared culture, accessibility, and sustainability. Public administrations and non-governmental organizations (NGOs) could form partnerships to co-develop open data initiatives, sharing knowledge and resources to respond to the evolving needs of stakeholders. Cultivating an organizational culture that values open data and ensuring data quality through profiling, feedback mechanisms, and user driven interfaces can improve accessibility and usability. Furthermore, integrating open data across domains and adhering to technical standards, FAIR (findable, accessible, interoperable, and interoperable) principles (GO FAIR, 2016), and CARE principles (Research Data Alliance International Indigenous Data Sovereignty Interest Group., 2019) promote interoperability and facilitate seamless data sharing. Building open data communities and providing training opportunities for users will help develop capacity and foster greater engagement. Supporting open licenses, ethical considerations in data sharing (e.g., privacy and confidentiality), and transparency in legal frameworks is essential to ensuring responsible and sustainable data reuse. Lastly, ensuring the economic sustainability of ODEs through diverse funding (public, private and hybrid) models is expected to support their long-term success. Collectively, these strategies aim to create ODEs that are user driven, interoperable, and responsive to the needs of various users, ultimately contributing to their broader impact and sustainability. 4.2 Inclusive strategies The strategies for enhancing ODEs from the inclusive perspective emphasize the importance of non-government data publication, shared infrastructures, and multi-format data access. There is a focus on addressing barriers to local community access, supporting non-governmental contributions, and aligning the interests of diverse stakeholders to foster collaboration. Recommendations include community centric design, lowering data access barriers, and creating governance mechanisms for bottom-up initiatives. Furthermore, interdisciplinary collaboration, the design of inclusive open data events, and the institutionalization of these events are crucial. Legal frameworks should account for power dynamics and promote equity, integrating CARE principles alongside FAIR. Additionally, the availability of platforms for non-government open data is encouraged, with the suggestion of establishing a consortium for collective open data sharing and governance. In this deliverable, inclusiveness mainly focuses on open data contributions from the nongovernment data. However, this view appears too limited. Inclusivity should not be restricted to providing open data to the ODE; rather, it should encompass any valuable contribution from nongovernment actors to the ODE—this may include new datasets, tools, insights, services, or D5.3 Strategies towards sustainable open data ecosystems 19 collaborative practices that enhance the ecosystem as a whole. Recognizing these broader contributions reflects a more comprehensive and realistic understanding of what sustains an ODE over time. This marks an important shift from our initial perception of inclusivity and a more mature interpretation of sustainable ODEs. In addition, the inclusivity concept should be reconsidered to include also dataset inclusivity, as true inclusiveness in open data goes beyond who contributes data—it must also ensure that the data itself reflects diverse contexts, needs, and populations, particularly those often underrepresented or marginalized in data collection and sharing processes. Therefore, dataset inclusivity should also be considered in this context. 4.3 Circular strategies The strategies outline efforts to enhance the circularity of ODEs by promoting equitable value distribution. Strategies include fostering collaboration among governments, NGOs, and businesses, adopting open standards, and validating metadata. Another strategy to drive participation in the ODE is to incentivize participation both financially and non-financially. Additionally, leveraging intermediaries through advisory services is crucial for facilitating data sharing and integration, while promoting the use of open-source software further supports these efforts. In this deliverable (and the overall project), circularity has primarily focused on non-government (NG) actors utilizing Open Government Data (OGD) and contributing enhanced versions back to the ODE, thereby creating a loop of value creation and fair distribution. However, also this view appears too limited. Circularity should not be restricted to the return of improved OGD alone; rather, it should encompass any valuable contribution from open (government) data users back to the open data ecosystem (including the open data provider). This may include improved open (government) datasets, but also tools, new insights, services, or collaborative practices that enhance the ecosystem as a whole. Recognizing these broader contributions reflects a more comprehensive and realistic understanding of what sustains an ODE over time. This marks an important shift from our initial perception of circularity and a more mature interpretation of sustainable ODEs. 4.4 Skill based strategies The strategies related to the skill-based drivers of the ODE emphasize capacity building, data quality enhancement, and the empowerment of intermediaries through accessible tools. These strategies include initiatives such as providing data augmentation tools to improve data reliability and trust, as well as developing low-code platforms to support educators and data intermediaries in fostering broader participation and usability across diverse data environments. Strengthening skills and capabilities within the ecosystem is essential for ensuring sustainable and effective open data utilization. D5.3 Strategies towards sustainable open data ecosystems 20 5 Analysis - Sustainable Open Data Ecosystem Framework The capabilities of open data ecosystems come with challenges in realizing their full potential. By analysing these challenges and leveraging research conducted individually and in deliverables within this project and beyond, we designed an open data ecosystem (ODE) framework. 5.1 Designing the Open Data Ecosystem (ODE) framework The first attempt to design the ODE framework was made in Ali et al. (2025). For this deliverable, we adapted the ODE framework of Ali et al., (2025) with the help of interactive workshops and cocreation techniques by asking workshop participants to reflect on the proposed ODE framework and its components. In the first workshop, the initial version of the framework was explained to the participants, asking their opinions on the placement of components and their connections with other components. Some components were renamed, deleted, or enriched through the discussion. In the subsequent two workshops, we further improved the framework and added policies, challenges, and recommendations. Another example of the changes is that, initially, governance was not included as a component in the original framework. However, after the workshops governance was incorporated. Figure 5 presents a preliminary set of ODE framework components along with the beyond-stateof-the-art work required for each component. Each of them is explained in more detail below. Figure 5: components of a sustainable Open Data Ecosystem framework 5.2 Explaining sustainable open data ecosystem frameworkʼs components 5.2.1 Open Data Ecosystem Description: The ODE is considered pivotal for sustainable value creation through open data utilization. In this study, it is used as an umbrella term, as shown in Figure 5, and with the inner red circle encompassing all major components that play a direct or indirect role in the ODE's sustainable value creation. The ODE is comprised of a dynamic environment covering aspects related to open data stakeholders, driving forces, value creation, governance, and open data infrastructure. The motive behind the design of the ODE is to identify all components and their dynamic connections. Furthermore, the identification of malfunctions in the current ecosystem will allow for the proposition of the appropriate strategic approaches in order to establish a wellfunctioning ODE. In ODEs, stakeholder groups work collaboratively to stimulate the potential of D5.3 Strategies towards sustainable open data ecosystems 21 open data. Despite the potential of ODEs, they face several challenges regarding the standardization of tools, techniques, and methods for implementing open data initiatives, as well as generating value through the use and reuse of open data based on needs and requirements. Addressing these challenges can enhance value creation and support the sustainability of the ODE. State of the art: Several studies have explored the concept of ODEs (Styrin et al., 2017; Van Loenen, Zuiderwijk, Vancauwenberghe, et al., 2021; Zuiderwijk et al., 2014). ODE concepts are at very beginning stage of development, facing challenges with proper actor inclusion and roles, value-creation, value-distribution, user-needs elicitation, quality of data, utilization of artificial intelligence, adding value to the open data, and making all these steps transparent within ODEs and beyond. Open data systems face challenges to achieve value-creation and sustainability due to supply and demand mismatch, exclusion of certain user groups and domains, linear valuecreation, and lack of skills have been explored at domain and country level such as finance, legal, education, geographic, and agricultural (Van Loenen, Zuiderwijk, Vancauwenberghe, et al., 2021). Open data impacts and its realization depends on stimulation of technical, social, and political resources, beyond the data supply but also making a contribution chain of activities around the datasets (Davies, 2011). The ODE complexity and challenge space observed from its multifaceted and multiprofessional nature and even gets multiplied when heterogeneity from the data, domains, and actors and each of them having their own specific challenges at different spatial and temporal levels gets into this. Beyond the state of the art: We have examined the diversified nature of ODE concepts and associated challenges with them, which suggests a clear need for the ODE framework creation — containing all the major components. The role of ODE frameworkʼs components in stimulating the ODE drivers. Furthermore, proposing and formalizing strategies for each ODE frameworkʼs components, so that actual ODE can be sustained and value-creating for all involved actors. Additionally, emerging AI technologies, such as the open-source AI models, can both benefit from and contribute to ODEs. The formalization of ODE frameworks and corresponding strategies would be one step towards achieving sustainability and value-creating ODEs. 5.2.2 Open data infrastructure Description: The technological components of ODEs, including tools, standards, services, and software applications, support data storage, sharing, and operations such as data integration and interoperability across diverse data domains, form the infrastructure of ODEs. Open data infrastructure reinforces the entire ODE by enabling smooth data sharing and supporting collaborative activities. Open data infrastructure faces challenges with the advancement of technologies, such as existing usage of obsolete technologies and unstandardized practices. State of the art: Several studies emphasize the importance of scalable and interoperable infrastructures to ensure the sustainability of ODEs. Kirstein et al., (2020) describe the idea of largescale open data management platforms based on semantic web technologies. Conde et al., (2024) further explore metadata-driven integration of open data portals into data spaces, advocating for automated metadata generation and validation through standardized models. Grossman et al., (2016) propose the data commons model, which co-locates data, storage, and computational resources with common analysis tools, emphasizing persistent digital identifiers, metadata services, APIs, and data portability for interoperability. Collectively, these studies underscore the need for modern, scalable, and standardized open data infrastructures that leverage semantic technologies, metadata-driven integration, and interoperable frameworks to enhance the effectiveness and long-term viability of ODEs. D5.3 Strategies towards sustainable open data ecosystems 22 Beyond the state of the art: The future of ODEs could embrace adaptive and self-optimizing data infrastructures that evolve in response to real-time conditions and user interactions, what we could define as living open data infrastructures. Unlike traditional open data repositories, which rely on periodic updates and manual interventions, living open data infrastructures would incorporate machine learning, automation, and feedback loops to enhance data quality, relevance, and interoperability dynamically. By integrating AI-driven mechanisms, open datasets could learn from usage patterns, detect inconsistencies, and refine their structure and content based on community contributions and contextual changes. This approach would foster greater responsiveness, accuracy, and efficiency, ensuring that open data remains continuously updated and aligned with user needs. Moreover, self-optimizing datasets could enhance cross-domain interoperability by adapting to evolving data standards and seamlessly integrating new information sources. However, realizing this vision requires addressing key challenges, such as transparency in algorithmic decision-making, and safeguards against biases in automated learning processes. Additionally, governance frameworks would need to balance openness with reliability, ensuring that self-optimizing datasets remain verifiable and accountable to the broader open data community. By embracing living open data infrastructures, future ODEs could move beyond static data repositories, enabling a more dynamic, intelligent, and sustainable approach to data sharing and collaboration. 5.2.3 Enabling environment Description: Enabling the environment is meant to cover aspects of sustainable ODE such as open data market (e.g., business models for sustainability), culture, regulations, and other capacity building, and community participation activities that support or hinder the success of open data initiatives. Community participation is important as ODEs assume the utilization of data outside the systems that generated them to create value. The proper arrangements of the enabling requirements reduce barriers to open data sharing and use, promote FAIR principles, drive innovation, and foster transparency across diverse cultural contexts. In the enabling environment there is relevant to consider the ethical considerations such as bias, privacy concerns, and misuse of open data (Zuiderwijk & Janssen, 2014b). State of the art: It is important to understand that any enablement requirement is part of a bigger system of values in which the definition of transparency and participation is negotiated. Knowledge production is political and shaped by power dynamics that enable or limit participation in open data initiatives (Bates, 2013; Johnson, 2014; Kitchin, 2024) . More specifically, some actors lack the skills to make sense of open data (Perovich et al., 2020). This is particularly true for marginalized communities who cannot actively participate in open data initiatives or who might not be represented by data due to data gaps (Milan, 2020; Ruijer et al., 2024). Further, AI and data infrastructures complicate and dilute the process of tracing data origins, making value assessment challenging (Publications Office of the European Union., 2024). Power imbalances shape data access and usage, leading to selective openness, where dominant actors (e.g., corporations) benefit more than smaller entities or individuals (Broomfield, 2023; Gurstein, 2011). Beyond the state of the art: Starting from the consideration that ODEs also bring conflicts among values, different governance strategies can be envisaged in terms of the study and practice of ODEs: Study the extent to which open data regulations mandate or promote data sharing that is not meant to be repurposed in the data economy. For instance, the implementing regulation on High-Value Datasets (2023/138) prioritizes economic value, thus enabling greater economic/market value rather than other values, such as participation (Broomfield, 2023). Understand the role of commercial actors in open data by adding obligations for open data sharing in the public interest, as seen in Barcelonaʼs ‘data sovereignty clauses,ʼ which require private service providers to share data in an open, machine-readable format within procurement contracts (Monge et al., 2022). Rely on private law mechanisms to promote equitable data use, as D5.3 Strategies towards sustainable open data ecosystems 23 in the case of stronger share-alike obligations that prevent the risk of open data exploitation (Data Science Law Lab, 2024). 5.2.4 Governance Description: Major element of ODE, deals with setting the roles, defining rules of open data initiatives and open data itself, and mechanisms for open data handling (managing open data responsibly, justifiably, and endurably) throughout open data life cycle and beyond. This sets the rules such as resolving conflicts and power imbalances among stakeholders (data providers, intermediaries, and users), preserving accountability, adopting/developing standards. Good governance leads the ODE to essential components such as trust, data quality, and FAIR use of open data. Open data governance is opaque, with accountability dispersed across private-public partnerships (J. Taylor, 2017; L. Taylor et al., 2022). This lack of transparency makes it difficult to track data movement, integrity, and security, leading to risks such as unintended data repurposing (e.g., U.S. open science data used for facial recognition in China) (L. Taylor et al., 2022). State of the art: Governance in ODEs is essential for maintaining sustainable data sharing and reuse among stakeholders, including governments, businesses, and individuals (Pollock, 2011; Van Loenen, Zuiderwijk, Vancauwenberghe et al., 2021). Traditional governance models have largely followed hierarchical and market-based mechanisms (Chantillon et al., 2017; Santoro et al., 2024), but research highlights the benefits of participatory, commons-based approaches (Gurnstein, 2011; Ostrom, 1990), aiming for greater inclusivity and efficiency. ODE governance must balance regulation, stakeholder involvement, and sustainable data sharing. Hierarchical models rely on strict regulations, ensuring compliance but limiting adaptability (Weerakkody, Janssen, et al., 2017). Market-based governance uses financial incentives to encourage participation but may exclude resource-limited actors (Moore, 1993, 2006). Network-based governance fosters collaboration and trust (Provan & Kenis, 2008), but can suffer from coordination issues (Johnson, 2014). A growing body of research supports commons-based governance, following Ostromʼs (1990) self-governing principles. This approach promotes boundary-making, shared decisionmaking, and legal frameworks for sustainability (Davies & Perini, 2016). Commons-based governance facilitates collective management of data resources, improving accessibility and fostering innovation (Cavanillas et al., 2016). Non-governmental data holders face barriers in ODEs, including legal, financial, and technical challenges (Reggi & Dawes, 2022a). Many private entities hesitate to share data due to intellectual property concerns and competitive risks (European Commission, 2020). To address these issues, governance strategies must provide regulatory clarity and incentives. Policy interventions such as mandatory data-sharing for publicly funded research or tax benefits for businesses that contribute data can bridge gaps (Weerakkody, Kapoor, et al., 2017). Collaborative platforms enable businesses, NGOs, and researchers to negotiate agreements, fostering trust and mutual benefits (Chantillon et al., 2017). Empirical studies indicate that public-private partnerships improve datasharing practices (Hossain et al., 2016). User engagement is crucial for ODE sustainability, requiring contributions beyond data consumption (Yuan, 2019). Barriers such as limited technical literacy, lack of awareness, and unclear feedback mechanisms hinder participation (Huijboom & Van den Broek, 2011). Motivation theories offer insight into engagement. Self-Determination Theory underscores autonomy, competence, and social connection as key drivers (Ryan & Deci, 2000). Users contribute more when their efforts have meaningful impact and when platforms are user-friendly and rewarding (Wenger, 1998). Fostering communities of practice strengthens engagement by encouraging knowledge-sharing and innovation (Bachtiar et al., 2020). Effective governance should include gamification, recognition systems, and streamlined contribution mechanisms (Davies & Perini, 2016). Open data portals allowing user annotations and dataset updates improve participation (Safarov et al., 2017b). Partnerships with educational institutions and advocacy groups further enhance long-term user engagement (Hossain et al., 2016). D5.3 Strategies towards sustainable open data ecosystems 24 Beyond the state of the art: Governance frameworks must evolve to address emerging challenges like data sovereignty, AI in decision-making, and the digital divide (Kitchin & Lauriault, 2014). Hybrid models combining hierarchical, market-based, and commons-based approaches can provide regulatory compliance while fostering innovation and collaboration (Micheli et al., 2020; Van Dijck et al., 2018). AI-driven compliance mechanisms can streamline governance by monitoring data-sharing agreements, while blockchain-based smart contracts can enhance transparency (Davies & Perini, 2016; Gurnstein, 2011). However, these technologies must be implemented carefully to prevent exacerbating power asymmetries (Ruijer et al., 2024) and environmental footprint. Ensuring inclusivity in ODE governance remains vital. Marginalized groups often face obstacles in data access and contribution, reinforcing inequalities (Meijer & Potjer, 2018). Policies promoting digital literacy and community-led governance can help address these disparities (Ruijer et al., 2024). Governance strategies must prioritize equity, ensuring open data benefits are widely distributed and decision-making remains inclusive. 5.2.5 Resources Description: Resources of ODEs cover dimensions like datasets, metadata models, tools, and funding to manage the data, and analytical models which can support data accessibility and usability across wider domains. These resources are important to attain innovation and benefit from open data. Better resource management, allocation, and utilization are required to ensure transparency and accountability of the ODE for fair value creation and distribution. State of the art: According to the Open Data Barometer3, open data initiatives that have support from the government and sustainable availability and access to required resources have a high chance of achieving their defined goals and impact. Open data initiatives development and implementation needs continuous availability of resources (e.g., finance) (Donker & van Loenen, 2017) . Data curation and sharing solutions are considered important and they provide solution for “a common metadata tracking framework, providing tools and resources to create and manage large, heterogeneous data sets in a coherent manner, and allowing users of (parts of) data sets to ‘connect the metadata dots” (Sansone et al., 2012). The data resources and tools are considered a characteristic of an ODE (Zuiderwijk et al., 2014). The data resources cover several aspects, including licensing, linked data, data packaging, patching, pull requests, and data merging options. The data resources should also include ICT preparedness, ICT usage, architecture, and resource allocation (Zuiderwijk et al., 2014). A study by Zuiderwijk et al. (2016) mentions that open data policies should be oriented and pay attention to the availability and quality of five types of resources: open data, open information technology, internal IT, required knowledge, and governance. Resources on the open data producer's side are needed, but some resources are also needed on the data user's side, such as open datasets, the skills required to analyse the data, stakeholder collaboration and network effects, and competitive advantage based on open data. Open data resources required to create a competitive advantage have also been discussed in the study by Zuiderwijk et al. (2016). Even companies can acquire the required resources from their allies to generate commercial value in ODEs. Resources from different companies may lead to value creation and give companies an opportunity to rely on specific resources that are important to them. The need for resources also varies from company to company. Resource needs are also expandable for all stakeholders to some extent. Beyond the state of the art: The availability of resources is mostly focused on the producer's side, and less attention is given to the open data users and intermediaries' side. There is also a need for funding models to cover public-private partnerships and open innovations. Itʼs also crucial that sustainable funding is provided for other parts of the ODE, including data 3 http://opendatabarometer.org/ D5.3 Strategies towards sustainable open data ecosystems 25 infrastructure, governance, and community engagement. We have seen studies that discuss data packaging and patching, but cross-platform interoperability remains an issue. Work is required toward the development of a standardized resource framework covering different kinds of resources (technical, social, financial, and even human). Considering social capital as a quantifiable resource in ODEs is also important. 5.2.6 Stakeholder groups Stakeholder groups can be categorized into three groups: producers, intermediaries, and users. The producerʼs stakeholder group is responsible for data generation and provision through the ODE. The intermediariesʼ stakeholder groups are responsible for a diverse type of tasks such as intermediating, training, or developing applications. Finally, user stakeholder groups are using open data as per their need and developing applications or making decisions based on open data. Roles may overlap across different actor groups. For instance, citizens can serve as data providers (such as through platforms like OpenStreetMap), while also being data users and beneficiaries of open data initiatives. Similarly, application developers frequently take on multiple roles as intermediaries, users, and enablers who contribute to both the technical and functional layers of the ecosystem. Academia is another cross-cutting actor, actively involved in data provision, stewardship, advocacy, and research, thereby influencing both policy and practice. Furthermore, data users are not per se passive recipients—they often provide feedback that shapes data quality and relevance and simultaneously benefit from the insights and services enabled by open data. This fluidity of roles underscores the dynamic and interconnected nature of the open data landscape. In some cases, it has also seen that roles and actors could be the same, for instance, intermediaries can also be enablers/ data steward. Producers Description: This group of stakeholders are responsible for generating and providing open data. They are also known as data providers (Heijlen & Crompvoets, 2021) or data publishers (Charalabidis et al., n.d.; Hrustek et al., 2023). Their role is to make sure that the data being produced is relevant, useful, accurate, and accessible to the other stakeholders. They require to maintain data quality and adherence to data maintenance aspects all along the way of data life cycle. State of the art: They are responsible for the open data supply in the ODE, and this open data supply is considered their best effort contributed to the ecosystem. They are responsible for dataset creation or generation (Buteau et al., 2018; Oliveira et al., 2017) and/or publishing (Corbett et al., 2020) open data to fulfil end-user demands in the ODE. They may share open data with intermediaries to enrich the data and prepare it (Alexopoulos et al., 2014). Data provision is not always based on demand and needs. Data should be considered for publication if it can be used for service development for citizens, provide value to companies, or contribute to economic improvement (Köster & Suárez, 2016). Often, open data producers are required to develop a framework (legal or regulatory) pertaining to data opening, data sharing, and data use (Dawes et al., 2016; Fang et al., 2024). Governments are the most common providers of open data, but a few studies also mention businesses or private enterprises as open data producers (see, for example, Ali et al., 2024). Beyond the state of the art: In the ODE, the inclusive nature of an ODE emphasizes data provision from non-governmental sources (Van Loenen, Zuiderwijk, Vancauwenberghe, et al., 2021). Data integration techniques should be adopted so that new datasets can be generated from existing ones. The provision of high-quality and demand-oriented datasets can contribute to value creation and sustainability in ODEs. Non-governmental organizations should also be included as D5.3 Strategies towards sustainable open data ecosystems 32 organisational culture, which means they have an awareness about the social impact of open data sharing. Additionally, the organisational culture of NPOs is often less hierarchical, and employees can propose and take part in projects they enjoy, which means they have a presence of engagement or enjoyment activities to motivate data sharing. Another incentive is the community support and social impact of open data sharing and open data projects. It is motivating for smallsized NPOs to continue the project/data sharing, if the impact and support are evident, as their resources are otherwise limited, so they must prioritise which projects to work on/data to open. Beyond the state of the art: For non-profit and non-governmental organizations, important incentives to open data sharing and value generation are training to improve data-literacy skills of the employees together with raising awareness of open data concepts. Smaller sized NPOs and NGOs may not have a dedicated programmer or skilled employee to share the data properly as open or reuse the existing open data to create social value. To motivate these actors, they need to have access to training and the ability to employ skilled employees. Another incentive for NPOs and NGOs is more financial cooperation that would support data-skilled employees and help NPOs run their open data projects long-term. There are existing examples of such cooperation, such as the Open Knowledge Foundation networkʼs prototype fund, that can help small organisations or activists financially with their projects and data literacy training. A similar solution should be promoted, especially across the countries, to help distribute the resources to the NGOs with less support that needed for ODE engagement. Moreover, the creation of a common platform for NGOs to share the data, like open data government portals, could work as another incentive. The creation of such a portal could help with the availability of appropriate technical tools and availability of resources that many NGOs lack and, thus, are unable to share their data (Petti, 2024). 5.2.13 Value Description: Well-organized open data initiatives generate values of different types, such as tangible and intangible values. These values can be growth in the economy, social awareness and empowerment, and sustainability in the field from which this open data is generated and shared. The value generation and its fair distribution is a long-standing discussion topic in open data. State of the art: The current state of the art in value generation within ODEs showcases the balance between the autonomous, self-interested actors and the collaborative mechanisms that enable value creation. Davies (2011) emphasizes that successful value generation requires mobilizing a combination of technical, social, and political resources while fostering coordination and self-organization among ecosystem participants (Davies, 2011). Csáki (2019) further elaborates on ODEs as dynamic systems where actors and groups create shared meaning and value around open data, with their interactions shaping the ecosystemʼs growth and health (Csáki, 2019). Oliveira and Lóscio (2018) identify four core elements of an ODE—resources, roles, actors, and relationships—underscoring actorʼs flexibility in adopting various roles (Oliveira & Lóscio, 2018). Strategies for maximizing value are shifting from top-down interventions to communitydriven approaches, recognizing the inadequacies of hierarchical models for handling multidirectional data flows. Beyond the state of the art: While current research focuses on value creation in the ODE, there is a need to deepen the knowledge and understanding of value distribution mechanisms and their adaptability in the dynamic environment of an ODE. The development of value distribution algorithms in response to emerging stakeholder scenarios is crucial. Additionally, the creation of feedback loops and ecosystem health metrics can help facilitate value creation and fair distribution within the ODE. Furthermore, introducing new circular business models into the ODE and exploring how they can integrate with the circular economy and multi-stakeholder collaborations will contribute to more resilient and adaptable ODEs. D5.3 Strategies towards sustainable open data ecosystems 33 5.2.14 New concepts (AI4Data and Data4AI) Description: Artificial intelligence may be considered as a driving force to drive the synergies in the field of open data. Its purpose is two-fold, firstly, using AI to improve the open data at various stages such as quality, metadata, alignment with standards. On the other hand, use of AI to develop applications and use-cases using open data to bring innovation and value-generation. AI4Data means improving open data in terms of quality (including also interoperability, integration, and metadata generation). Data4AI means using open data as a source and bringing AI analytical capabilities to foster innovation. Data4AI covers the aspects of making open data available for AI models training and testing. Advancement in AI4Data and Data4AI can open new opportunities in data-driven solutions or easy data product developments. State of the art: Artificial Intelligence has revolutionized how open data is used, whether in data quality enhancements or innovative applications. The AI4Data paradigm focuses on enhancing open data at various stages, such as generating or improving metadata, enhancing data quality, making data interoperable, and standardizing it. Several studies have explored these topics, and they are still being investigated avidly following the advent of more advanced models. For instance, Ahmed et al., (2024) created a hybrid method (BRYT) combining conventional and advanced methodologies such as BERT, RAKE, YAKE, TextRank to extract keywords for open data sets. Song et al., (2024) utilize contemporary Large Language Models (LLMs) such as LLAMA and Mistral to extract metadata from more descriptive fields inside the dataset. Turgunbaev (2024) discusses various conventional machine learning methodologies for the particular use case of metadata extraction. Giglou et al., (2024) explore state of the art LLMs such as GPT, Llama, Mistral to perform ontology mapping ultimately as a use case for data interoperability. Bakker & Scala, (2024) perform ontology learning from textual data using contemporary LLM: GPT-4o. Alharbi et al., (n.d.) employ LLMGPT to create an ontological structure – Knowledge Graph from medical texts. Maratsi et al., (2024) explore multiple ontologies and their possible interconnected mapping. Moreover, they employ GPT 3.5 and GPT 4 to generate the mappings and validate its accuracy on manual data. Data4AI focuses on utilizing open data to enhance the development of AI applications. Open datasets provide significant opportunities for training and improving machine learning models. Access to high-quality open data speeds up AI research by lowering the costs and time required for data collection and preparation. Weber et al., (2024) emphasize the significance of open datasets for training machine learning models. They reflect the challenges associated with transparency, data access, and data curation, crucial for advancing open-source AI development. Arbeláez et al., (2024) underscore the significance of employing open data, which might be diverse and even unlabelled, as a better cost-effective alternative to custom-labelled data which might be expensive and might hinder development. Alam et al., (2023) highlight the role of open data and explainable AI in enhancing transparency, accountability, and interpretability in healthcare AI models. Employing diverse open data, the models can achieve better reliability and accuracy, promoting trust and openness. State-of-the-art reflects how the synergy between AI4Data and Data4AI can create robust feedback loops. Improved data quality and metadata quality (AI4Data) lead to greater model performance, while AI applications and innovations (Data4AI) can further enhance data collection methods through the use of tools using AI. This dual advancement opens opportunities for new data-driven solutions, ranging from automated decision-making systems to scalable data products that address societal challenges. Beyond the state of the art: The future holds significant potential for AI4Data and Data4AI. It would revolutionize data curation and AI model training. The current state particularly focuses on improving data quality, interoperability, and accessibility. Moving forward, we can also focus on self-optimizing dynamic ecosystems that use AI and reinforcement learning to maintain the integrity of data, metadata, and interoperability standards. From conventional static systems, it D5.3 Strategies towards sustainable open data ecosystems 34 would move to more dynamic systems that would be self-sufficient in addressing changing contexts and user needs. For instance, whatever data-related needs are communicated to the systems via LLMs, which understand the context of user needs, it would trigger the pertinent submodel to address it and resolve it. Moreover, data in ontological structure and knowledge graphs could prove to be a game changer for transparency, interoperability, and, ultimately, for training models. We could leverage the semantic capabilities of LLMs to build domain-specific ontologies to structure open data. It would highly enhance the interoperability and openness of the models that will be trained through those. The domain-specific ontologies and structure would highly enhance the AI model training for any particular domain task. Apart from being accurate it would turn out to be highly transparent and explainable in terms of its predictions. In the future, we may also see the emergence of AI-driven data marketplaces that facilitate the dynamic exchange and monetization of open data through blockchain-based platforms. These platforms would potentially enable data sharing by ensuring provenance, secure access, and fair compensation. This approach would significantly enhance data-driven innovation and promote economic growth on a global scale. 5.3 Towards a framework of an ODE The framework and associated components require dedicated efforts to transform the open data realm and fill the gaps/problems by proposing specific strategies for mitigation. Eventually, by filling these gaps through the proposed strategies, a sustainable ODE framework can be formed. We have illustrated the ODE framework by first detailing the component-level aspects and exploring how each component can be theoretically enhanced through support from the literature and beyond state-of-the-art work. We then further extended this by analysing the interactions among the components themselves, aiming to make the ODE more user-driven, inclusive, circular, and skill-based. Figure 6 presents the resulting ODE framework outlining the components beyond their concepts including interactions and working within ODEs. Our ODE framework includes stakeholders, data, drivers, incentives, values, collaboration/network effect, co-creation, and open data infrastructure. Data quality and data exchange appear as possible driving forces in ODEs. Ahmed (2023) addresses challenges in data findability, accessibility, usability, and value creation through enhanced data quality. The literature also links these forces to evolving concepts that integrate artificial intelligence. Ahmed (2023) advocates using AI to overcome challenges in open data management—a notion in line with AI4Data—while Patel et al. (2023) emphasize data-centric innovations that exemplify Data4AI. Data quality and data exchange are key driving forces in ODEs, ensuring interoperability and usability, while AI4Data enhances data processing, and Data4AI relies on open data for training AI models (Ahmed, 2023; Ahmed et al., 2024; Kijanović et al., 2024). These components are integrated within the ODE framework. Each component of the ODE framework has been mapped to its corresponding drivers using different colours. Green boundaries represent user driven aspects, red indicates the circularity of the ODE framework, blue represents inclusiveness, and purple highlights skill-based components. In some cases, more than one driver corresponds to components. The components of the ODE framework are very diverse in nature — encompassing entities (stakeholders), processes (data exchange and collaboration), intangible components (such as values and incentives), and technical elements (infrastructure). This diversity is intentional, as the framework aims to provide a holistic view of ODEs, where multiple dimensions — social, technical, institutional, and economic — interact and co-evolve. Rather than limiting the framework to one type of component, this inclusive approach captures the complexity and interconnectedness of real-world ODEs. We conducted interactive workshops to reach the visualization of the ODE framework and associated components. The ODE framework was refined through these interactive workshops, discussions, and feedback within the consortium during the preparation of this deliverable. D5.3 Strategies towards sustainable open data ecosystems 35 Figure 6: A possible visualisation of an open data ecosystem framework In Figure 6, we observe numerous interactions among diverse and multidisciplinary components, which raises concerns about the complexity, understandability, and straightforward application of the ODE framework. We have realized that this complex representation is difficult to grasp, indicating the need for further refinement — possibly by developing a less complex framework or breaking the overall framework into smaller, simpler sub-frameworks. This approach would help achieve better understanding of the ODE framework, its components, and the necessary support for ODE drivers. This idea will be considered as a future improvement to the ODE framework. D5.3 Strategies towards sustainable open data ecosystems 36 6 Analysis and Synthesis of Strategic Approaches In this chapter, we present the analysis and synthesis of strategies for sustainable ODEs. We have collected 83 proposed strategies (see annex 1). We grouped and explained the proposed strategies based on their relationship with ODE Frameworkʼs components, and ODE drivers. Thorough analysis resulted in 29 distinct strategies; these 29 distinct strategies are listed in 6.1, 6.2, and 6.3 subheadings. These strategies foster the sustainability of an ODE. They reflect a wide range of multidisciplinary notions to be considered to design an ODE and further make it sustainable. In this chapter, we link the 29 unique strategies to the ODE drivers and present them accordingly (see Figure 7). Figure 7: Overarching strategies per ODE driver 6.1 Strategies towards a user driven open data ecosystem Strategies pertaining to a user driven ODE include coordinated data stewardship between governmental and non-governmental actors, ensuring open data platforms incorporate user driven design, feedback mechanisms, and emerging technologies. Standardized interfaces, technical openness, and accessibility tools empower marginalized stakeholders and facilitate seamless data interaction. Institutional support, legal mandates, and funding policies strengthen sustainability, while open data events and needs assessments align data availability with societal and economic priorities. NGOs and industry collaborations enhance data usability, while tax incentives and funding models encourage sustained contributions. By fostering awareness of open dataʼs social impact and aligning private interests with public value, participation and engagement in the ecosystem can be further expanded. Figure 8 illustrates the strategies (first degree) and additional approaches (second degree) for the sustainable ODE under the “user driven” driver. D5.3 Strategies towards sustainable open data ecosystems 37 Figure 8: User driven open data ecosystem strategies and additional approaches connected to a strategy 6.1.1 Coordinated data stewardship across governmental and non-governmental actors The effective coordination of ODEs requires well-defined governance structures, typically managed by governmental actors. Governments play a crucial role in ensuring that data is collected, maintained, and shared in a way that maximizes its usability and societal impact. They set policies, establish standards, and ensure transparency, making ODEs more accessible and functional. However, relying solely on government institutions to discharge these functions can lead to inefficiencies, bottlenecks, and scalability limitations. By introducing incentives, nongovernmental actors—including private sector organizations, academic institutions, NGOs, and civic tech communities—can take on key roles in managing, improving, and disseminating open data. This distributed governance approach fosters a more dynamic, resilient, and sustainable circular data ecosystem where multiple actors collaborate to enhance data reusability, quality, and accessibility. By allowing non-governmental actors to participate in open data governance with the right incentives—such as funding, recognition, data access privileges, or policy influence, the system benefits from diverse expertise, increased innovation, and a broader engagement base. This approach also ensures that data remains continuously updated, relevant, and applied to real-world challenges, reinforcing data circularity. ODEʼs driver User driven ODEʼs framework components Governance Additional approaches • Continued implementation of legal mandates for open government data • The public administration should institutionalize and support open data events • Ensuring open data initiatives balance public and commercial interest • Institutionalizing funding support policy support for open data infrastructure • Enhancing coordination and efficiency in open data management • Mandating open data usage and quality feedback in government agencies • Cultivating strong organisational cultures oriented towards open data • Developing a pipeline to engage the other open data providers as well. D5.3 Strategies towards sustainable open data ecosystems 38 6.1.2 Enhancing open data platforms This strategy fosters enhancing open data platforms' interface and feedback mechanisms by adopting user driven design, making use of new technologies wherever this is possible, e.g., Natural Language Processing, Virtual and Augmented Reality, AI, and Chatbots. This strategy aims to improve usability, inclusivity, and engagement in open data portals by adopting user driven design principles and integrating advanced technologies. Natural Language Processing (NLP) and AI-powered chatbots can simplify dataset discovery and interaction, reducing barriers for nontechnical users. Active feedback loops ensure continuous platform improvement by allowing diverse user groups including non-technical users to provide input and feedback on the data services. By fostering adaptive, intelligent, and inclusive open data portals, this strategy strengthens the user driven and inclusive dimensions of the ODE, improving both data accessibility and greater user engagement. ODEʼs driver User driven ODEʼs framework components Data provision, Enabling Environment, Infrastructure Additional approaches • Community-centric design prioritizes the needs, preferences and involvement of communities in the ODE. • By making resources (financial, time, people/workforce) available, data contribution can be enabled/leveraged • Introduce tools for unified data and physically impaired user groups. 6.1.3 Establish effective feedback mechanisms Establishing effective feedback mechanisms strengthens the user driven and circular aspects of the ODE by enabling continuous data quality improvement, user engagement, and adaptive platform development. Feedback mechanisms such as feedback forms, dataset ratings, comments on the datasets, reporting issues related to the datasets, and discussion forums of the open data portals allow users to contribute insights and suggest enhancements. This fosters collaboration between data providers and users, ensuring that datasets remain relevant, accurate, and valuable for diverse stakeholders. Integrating structured feedback loops, such as user surveys, AI-driven sentiment analysis, and real-time engagement dashboards, enhance platform responsiveness and trust in open data services. Ultimately, this approach leads to a more dynamic, inclusive, and userresponsive ODE, facilitating greater adoption and impact across sectors. ODEʼs driver User driven ODEʼs framework components Data Usage Additional approaches • Facilitating Needs Assessment and Communication in ODEs 6.1.4 Develop a standardized open data portal interface to make the data publication process easier A standardized open data portal interface can streamline the data publication process by providing a user-friendly, consistent framework for data entry, metadata documentation, and access management. By establishing uniform guidelines for formatting, categorization, and tagging, such an interface reduces inconsistencies and ensures that datasets are easily discoverable and interoperable across different platforms. Additionally, automation features, such as built-in data validation and version control, can enhance data quality while minimizing the effort required from contributors. Open APIs and export functionalities further facilitate data sharing and integration with other analytical tools, maximizing the usability and impact of published datasets. Ultimately, a well-designed standardized interface simplifies the workflow for data providers, encourages broader participation, and enhances the accessibility and reliability of open data for researchers, policymakers, and the general public. D5.3 Strategies towards sustainable open data ecosystems 39 ODEʼs driver User driven ODEʼs framework components Open data infrastructure 6.1.5 Technical openness of the datasets By opening the datasets, it technically means to follow open standards, formats, and other protocols to enhance the data integration across different systems and to facilitate the better accessibility of the open data. Technical openness contributes to the technical interoperability of open data. This strategy can be put into action by bringing the technical openness of the open data to the portals and making the open data publication as per technical standards adopted. For example, as 5-star open data states that the higher the star, the more the data is machine-readable (or technically open), in this way, data accessibility and reuse can be maximized. Furthermore, providing access to all technical solutions (regarding choosing metadata, data formats, and comparing their benefits and disadvantages) can help in achieving openness by technical means possible. Providing access to the open data through the means of REST APIs and following Open API specifications can also enhance the technical openness of open data. Providing open API explorer is a plus in the open data portal functionalities. Developing and utilizing the methodology to quantify the open data technical openness should be considered before making it online to the wider stakeholders. ODEʼs driver User driven ODEʼs framework Components Data Provision 6.1.6 Empowering marginalized stakeholders: encouraging inclusive participation in ODEs Ensuring that less represented, less powerful, and disadvantaged actors have opportunities to provide feedback and actively contribute strengthens the inclusive and user driven dimensions of the ODE. Powerful organizations such as government agencies, large corporations, and regional institutions should proactively engage all diverse user groups, NGOs, and small-scale data contributors by creating accessible, multilingual, and low-barrier feedback channels. Providing incentives, training programs, and capacity-building initiatives can empower these groups to participate meaningfully. Additionally, adopting collaborative governance models, such as community-driven data stewardship and participatory policymaking, ensures that feedback is not only collected but also acted upon. By bridging power imbalances in data contribution and decision-making, this strategy enhances equity, diverse representation, and the long-term sustainability of ODEs. ODEʼs driver User driven ODEʼs framework components Governance Additional approaches • Open data events should be designed to be accessible by non-specialist users • Aligning citizen needs and datasets for higher open data value not only in economic but also societal context • NGOsʼ current and potential contributions can be in the form of different values that constitute social values, as these organisations cover a wide range of activities and exist as intermediaries rather than in end-user or producer only form. They are helping the users and the data provided by collaborating and addressing both and by adding value through these collaborations • Leveraging tax incentives to support open data contributions (e.g., government agencies, companies, non-profit organizations, and citizens) • Incentivizing support for open data and open-source projects by training and support • By aligning private value and interests with open data sharing, data contribution can be enabled/leveraged (e.g., ensuring that data D5.3 Strategies towards sustainable open data ecosystems 40 contributors—especially non-government actors—see tangible benefits from participating in ODE) • By making stakeholders aware of the social impact of open data sharing, data contribution can be enabled/leveraged 6.2 Strategies towards an inclusive open data ecosystem To achieve a sustainable ODE under the "Inclusive" driver, several strategies are emphasized. These include enabling multiple data access points, fostering collaboration among non-government data holders, and enhancing accessibility through user-friendly, multilingual design. Establishing shared goals, local problem-solving networks, and partnerships with industry players can further strengthen inclusivity. It also includes the implementation of FAIR and CARE principles, metadata repositories, open standards, and anonymization tools ensures usability while maintaining privacy and security. Legal frameworks, tax incentives, and public financing support infrastructure and long-term sustainability. Additionally, fostering awareness, supporting data intermediaries, and institutionalizing open data initiatives help drive adoption and engagement across diverse stakeholders. Figure 9 illustrates the strategies (first degree) and additional approaches (second degree) for the sustainable ODE under the “inclusive” driver. Figure 9: Inclusive open data ecosystem strategies and additional approaches connected to a strategy 6.2.1 Enable multiple data access points This strategy aims at (a) enabling diverse data formats and API options, and (b) building accessible user interfaces to enhance usability for non-technical users. Firstly, providing datasets not only through downloadable files but also through API endpoints would be an option for supporting a variety of formats. In this manner, conversion of datasets across formats would be easier programmatically, reducing the effort to download files and then feeding them into applications. Secondly, making data accessible through API endpoints and open data portal interfaces should be friendly for non-technical users too. This will make open data accessible for both technical and non-technical users. ODEʼs driver Inclusive ODEʼs framework components User Stakeholderʼs groups Additional approaches • By making appropriate technical tools available, data contribution can be enabled/leveraged • The FAIR Principles should be applied to achieve the user driven ODE. • Follow some kind of quality standards and data profiling techniques to achieve the user-needs regarding the data quality and usability. D5.3 Strategies towards sustainable open data ecosystems 41 • Use open standards, open formats available to engage the users with diverse needs. Some users require data in bulk, others require APIs, and others require Linked open data • Use of open licenses should be encouraged 6.2.2 Facilitating data sharing among non-government data holders This strategy aims at analysing the barriers, motivations, and technical requirements for nongovernment data holder groups to share their data and propose technical mechanisms to enable/facilitate this purpose. To analyse the barriers, motivations, and technical requirements for non-government data holder groups to share their data, it is essential to conduct a thorough examination of the specific challenges these groups face. Barriers may include concerns related to data privacy, security, intellectual property and other rights (e.g., personal data), and the potential for competitive disadvantages. Motivations for data sharing often stems from the desire to enhance innovation, foster collaboration, and gain access to public or academic research. Identifying these drivers can help create tailored solutions to overcome obstacles. Technical requirements for data sharing include ensuring interoperability, standardization of data formats, and the availability of secure and user-friendly platforms that facilitate seamless data exchange. It is also important to consider the scalability and sustainability of these technical solutions. To enable data sharing, propose mechanisms such as the development of open-source data platforms, the implementation of robust data anonymization techniques, the use of clear licensing frameworks, and the provision of incentives such as funding, recognition, or access to new markets. These mechanisms should be designed to ensure that data holders feel confident in sharing their data while maximizing the societal value of open data. ODEʼs driver Inclusive ODEʼs framework components Enabling Environment Additional approaches • Partnerships can encourage non-government data holders to contribute open data, in addition to being users of open data. • Enhancing coordination and efficiency in open data management • Supporting and leveraging crowdsourced open data in Government Initiatives • Role of open data intermediaries in providing support for data supply and reuse • By making appropriate technical tools available, data contribution can be enabled/leveraged • Implement metadata repositories • Provide documentation for metadata usability • Follow some kind of quality standards and data profiling techniques to achieve the user-needs regarding the data quality and usability • Use/develop tools to anonymize the open data 6.2.3 Building shared goals and values in an open data ecosystem This strategy aims at creating shared goals and values among diverse stakeholders within an ODE. This ensures cohesive collaboration and maximizes the ecosystemʼs ability to deliver value across its users, contributors, and beneficiaries. To implement shared goals and values within an ODE, it is crucial to engage diverse stakeholders—such as government agencies, private sector entities, researchers, and citizens—early in the process. This can be achieved by organizing workshops, forums, or consultations to align a common vision, such as improving transparency, innovation, or social good. Clear communication of the ecosystem's objectives and mutual benefits fosters a collaborative culture where all participants understand their roles in data sharing, usage, and contribution. ODEʼs driver Inclusive ODEʼs framework components Collaboration, Values Additional approaches • By making data-sharing communities exist, data contribution can be enabled/leveraged D5.3 Strategies towards sustainable open data ecosystems 48 6.3.5 Designing interactive and adaptive visualizations for dynamic ODEs Visualizing ODEs as multi-layer networks provides a useful framework for understanding the complex interactions among actors, data flows, and governance structures. However, for such visualizations to be effective, it is critical to first define distinct layers—such as data providers, platforms, users, and legal frameworks—and identify relevant stakeholders, including government, private sector, and civil society entities. These relationships, encompassing data sharing, policy development, and collaborations, can be mapped using network techniques from graph theory, which allows the visualization of data flows and the identification of centralized or decentralized elements within the ecosystem. The use of advanced data visualization tools like D3.js, Gephi, or Cytoscape enhances interactivity by allowing users to explore the ecosystem through zooming, filtering, and drilling down into layers. While these dynamic features promote deeper engagement, they also introduce the risk of oversimplification or confusion if not carefully structured. Additionally, ensuring that these visualizations are linked to real-time data sources via APIs presents challenges related to data accuracy and consistency, as the ecosystem must continuously evolve to account for new data sources and stakeholders. Incorporating user feedback mechanisms—such as annotations, voting, or suggestions—can promote sustained engagement, but the process may introduce biases if not carefully moderated. The integration of machine learning for predictive insights may further complicate the interpretation of trends if the underlying algorithms are not transparent or if they privilege certain data patterns over others. Ultimately, while combining multi-layer network mapping with interactivity and real-time updates holds significant potential for managing ODEs, the design and implementation of such visualizations must carefully address challenges related to data accuracy, user engagement, and algorithmic transparency to avoid misrepresentation or overinterpretation. ODEʼs driver Circular ODEʼs framework components Enabling Environment, Data Usage, Infrastructure Additional approaches • Need to build skillset in the field of data visualisation and build capacity regarding data issues (fragmentation, aggregated data, unreliable data, and sensitive data) 6.3.6 Employing open data standards and formats The Open Data Platform (ODP) requires backing for data integration and interoperability from a range of stakeholders, including governmental and non-governmental entities. Employing open data standards and formats can streamline data accessibility and sharing within ODEs, enhancing their circularity. ODEʼs driver Circular ODEʼs framework components Resources, open data infrastructure, Data Provider Additional approaches • Adopt open standards for datasets • Implement metadata validation tools for consistent data quality • Enhance the data creation process by employing several technical mechanisms and technologies to improve critical aspects of data creation, quality and integration, such as augmenting semantic interoperability with the help of Large Language Models to automate or semiautomate processes that improve it, improving metadata annotation in open data portals using AI, or validating/checking technical compliance of data on the web • The data should be findable to a wide range of users with diverse search options • Need to build skillset in the field of data visualisation and build capacity regarding data issues (fragmentation, aggregated data, unreliable data, and sensitive data) • Open data community portal development helps in the identification of issues with data, the possible use of data, and future improvements. • Focus should be put on - different types of - interoperability and data portability D5.3 Strategies towards sustainable open data ecosystems 49 • Open data intermediaries could consider providing an open data platform based on federated architecture 6.3.7 Adopting scalable infrastructure technologies As the amount of data is increasing in various ways, managing and providing smooth access and other operations (querying, filtering, downloading, etc.) on a large scale require an underlying infrastructure with scalability in terms of memory and processing. Big data and cloud-based technologies are good options. Instead of storing large amounts of data on premises, adopting cloud infrastructure is a viable alternative. A hybrid approach (combining on-premises and cloud infrastructure) can also be considered. ODEʼs driver Circular ODEʼs framework components Resources, open data infrastructure, Data Provider Additional approaches • Public financing and maintenance of shared open infrastructures, like the European Open Science Cloud • Enhance the data creation process by employing several technical mechanisms and technologies to improve critical aspects of data creation, quality and integration, such as augmenting semantic interoperability with the help of Large Language Models to automate or semiautomate processes that improve it, improving metadata annotation in open data portals using AI, or validating/checking technical compliance of data on the web • Enhance data licensing and anonymization by employing technological facilitators to achieve this purpose, such as Machine Learning, data encryption, GenAI and legal interoperability facilitators. • Supporting open-source software development • Implement metadata repositories • Provide documentation for metadata usability • Use of open licenses should be encouraged • To ensure sustainability of ODEs, different types of funding models should be considered • Open data intermediaries could consider providing an open data platform based on federated architecture 6.3.8 Assessing the implementation of interactive visualization tools for data exploration and comprehension The implementation of interactive visualization tools for data exploration requires careful evaluation to ensure they address both user needs and data complexities. While platforms like, Plotly, and Tableau and library like D3.js enable dynamic interaction with datasets, there is a risk of oversimplification or information overload. Excessive options may overwhelm users, while customization features—such as colour choices—can distort data interpretations if not carefully managed. Ensuring that visualizations balance flexibility with clarity is crucial to prevent misinterpretation. Moreover, interactive features can introduce confirmation bias, where users manipulate the data to confirm preconceived beliefs rather than explore it objectively. Contextual guidance, such as explanatory annotations, is essential to help users understand data limitations. Real-time data integration is beneficial but comes with challenges regarding accuracy and consistency, as delays or inconsistencies could affect the reliability of the tool. While inclusivity features, like colour-blind-friendly palettes and screen-reader compatibility, are important, they often limit design flexibility. Additionally, real-time collaboration can raise concerns about data privacy, particularly in sensitive environments. Feedback loops for refining tools are important but can be biased by the most vocal users, highlighting the need for evidence-based iterations driven by data-driven insights. In conclusion, interactive visualization tools have great potential, but their design must address issues of accessibility, data integrity, and user behaviour to provide meaningful, reliable insights while avoiding misrepresentation. D5.3 Strategies towards sustainable open data ecosystems 50 ODEʼs driver Circular ODEʼs framework components open data infrastructure 6.3.9 Maximizing open data utilization through a purpose driven approach In many ODEs, the traditional approach is user driven, where the primary focus is on enabling access to data and letting users define its applications. While this fosters broad engagement, it can also lead to fragmented, inefficient, or even unintended uses of data, with varying levels of impact. A purpose-driven approach, in contrast, prioritizes clear societal goals as the guiding principle for data publication, management, and reuse. Rather than merely making data available and hoping it finds useful applications, this model proactively aligns open data initiatives with pressing societal challenges—such as climate action, public health, urban resilience, and social equity. By embedding purpose and social value into the core of ODEs, stakeholders—including governments, businesses, researchers, and civil society—can ensure that data-driven initiatives lead to meaningful, measurable, and responsible outcomes. This approach also encourages ethical considerations, prevents data misuse, and fosters sustainable long-term engagement in the data ecosystem. ODEʼs driver Circular ODEʼs framework components ODE Additional approaches • Aligning citizen needs and datasets for higher open data value not only in economic but also societal context • Ensuring open data initiatives balance public and commercial interests • By aligning private value and interests with open data sharing, data contribution can be enabled/leveraged • Use open standards, open formats available to engage the users with diverse needs. Some users require data in bulk, others require APIs, and others require Linked open data • Focus should be put on - different types of - interoperability and data portability 6.3.10 Make training in data skills and literacy available In media organizations, the lack of skills is prominent, although the gap has been identified both by media organizations and academics in the journalism sector. University programs in journalism now include data journalism as a topic in their curriculum, and several workshops and online training opportunities exist for journalists to acquire initial data skills that they can apply in their work. While younger journalists are more exposed through their studies, tools and opportunities also exist for more senior professionals to develop these skills. ODEʼs driver Circular ODEʼs framework components Incentives Additional approaches • Open data events should be designed to be accessible by non-specialist users 6.3.11 Adaptive engagement strategies ODEs thrive when stakeholders actively contribute, access, and reuse data. However, a one-sizefits-all approach to engagement often falls short in addressing the diverse needs, incentives, and capabilities of different actors, including governments, businesses, researchers, non-profits, and local communities. Adaptive engagement strategies emphasize flexibility and customization in how stakeholders interact with open data. Rather than relying on static outreach methods or rigid policies, adaptive engagement involves continuously evolving strategies to accommodate changing user needs, technological advancements, and societal priorities. This ensures that engagement remains relevant, dynamic, and inclusive. D5.3 Strategies towards sustainable open data ecosystems 51 By employing data-driven insights, feedback loops, and iterative engagement models, this strategy fosters sustained participation, trust, and value creation in the ODE. Adaptive engagement is key to data circularity, as it ensures that data is continuously updated, refined, and applied across different use cases, maximizing its lifecycle and impact. ODE Drivers Circular ODEʼs framework components Incentives Additional approaches • Incentivizing support for open data and open-source projects • To ensure sustainability of ODEs, different types of funding models should be considered 6.4 Integration of all strategies to the ODE framework clickable version Figure 11 is a screenshot of the developed clickable version to easily navigate through the ODE framework, drivers, and corresponding strategies per ODE frameworkʼs components. The second level of the information, after clicking intermediaries can be seen in Figure, it lists all the key elements per drivers, and strategies to include/fulfil these strategies per drivers have been popped up on the screen. In a similar way, users can navigate through the ODE frameworkʼs components and their relationship with ODE drivers, key-elements, and strategies. A clickable version is available online through the GitHub repository4. The ODE Framework provides a practical, holistic guide for ODE initiators to design, evaluate, and strengthen their initiatives. By clearly outlining key components—such as stakeholders, data flows, governance, infrastructure, and driving forces—the framework helps users understand how these elements interact and co-evolve. The clickable online version allows initiators to explore each component in detail, supported by strategies drawn from workshops and research. This enables them to identify gaps, apply best practices, and tailor strategies—such as promoting user drivenness, ensuring data quality, and fostering inclusive collaboration—to build sustainable and impactful ODEs. For example, if the Greek government is considering the development of a national ODE, the ODE Framework could serve as a valuable foundational tool to structure and guide their efforts. By offering a comprehensive view of essential components—such as stakeholders (ministries, agencies, citizens, private sector), data provision and usage, governance models, and technical infrastructure—the framework provides a clear roadmap for action. Using the clickable online version, government officials could explore each component in greater depth, supported by practical strategies and examples. For instance, they could focus on engaging citizens through user driven initiatives, enhancing data quality and accessibility, and fostering collaboration between public and private sectors. The framework also highlights key drivers like inclusiveness and circularity, which could inform policy decisions and promote long-term sustainability. By adopting the ODE Framework, the Greek government would be well-positioned to take a strategic, informed, and holistic approach to building a robust, future-ready ODE. 4 https://mohsanaliac.github.io/OpenDataEcosystemFramework/ D5.3 Strategies towards sustainable open data ecosystems 52 Figure 11: Screenshot of the clickable version developed to easily navigate in the open data ecosystem framework, drivers and corresponding strategies Figure 12: Clicking intermediaries on the open data ecosystem framework, popups a full list of key elements per drivers, and strategies per drivers. D5.3 Strategies towards sustainable open data ecosystems 53 7 Recommendations This final section aims at proposing recommendations for institutions and actors producing, using or otherwise contributing to the ODE, to be implemented in order to support sustainable ODEs. The 29 strategies led to the creation of 11 recommendations in technical and governance categories. These 11 recommendations may improve usability and accessibility, quality, and participation through a set of 5 technical and 6 governance strategies. They were designed to be scalable and to enable them to make a difference with limited budgets. To sustain the ecosystem, the facilitation of open data sharing among non-government data holders is crucial. Encouraging private entities, research institutions, and civil society organizations to share data requires addressing legal, technical, and organizational barriers. Establishing clear licensing frameworks, anonymization techniques, and secure open data-sharing mechanisms can help build trust among non-government data holders. Open-source platforms can further support seamless collaboration by ensuring interoperability and compliance with existing data standards. Partnerships and coordination efforts should also be strengthened to promote contributions from diverse stakeholders. 7.1 Technical recommendations 7.1.1 Improve discoverability by adopting thematic structured classification models Standardizing the classification of datasets ensures consistency and improves data discoverability. Adopting established classification frameworks, such as those used by the European Data Portal, facilitates interoperability and alignment with broader data ecosystems. Thematic categorization enhances the usability of datasets, enabling more efficient search, retrieval, and analysis by end users. Moreover, enhancing metadata quality for improved data discoverability to maximize the impact of open data, policymakers should enforce rigorous metadata requirements, ensuring datasets include comprehensive descriptions, provenance, and structured classifications. This will improve data discoverability, support machine-readable formats, and enable more effective data utilization by researchers, businesses, and the public sector. 7.1.2 Promoting standardization through linked open data for semantic interoperability Governments should prioritize the adoption of Linked Open Data principles to strengthen semantic interoperability. By leveraging ontologies such as SKOS and Wikidata, data portals can interlink datasets more effectively, allowing for better contextualization, cross-domain analysis, and enhanced knowledge extraction. Governments and organizations should establish mandatory compliance with widely accepted interoperability standards such as DCAT, FOAF, and Schema.org. Ensuring adherence to these standards will facilitate seamless data exchange across platforms, enhance data usability, and promote a more integrated ODE. This will strengthen open data Interoperability Through Standardization. 7.1.3 Establishing effective feedback mechanisms for ODE improvement To enhance user engagement and data quality, the establishment of feedback mechanisms such as user surveys, dataset ratings, and discussion forums on open data platforms should be included. These tools allow users to report issues, suggest improvements, and engage in continuous dialogue with data providers. Integrating AI-driven sentiment analysis and real-time dashboards can help improve platform responsiveness and trust, creating a dynamic and userresponsive ecosystem that fosters continuous data quality improvement and broader adoption. 7.1.4 Standardizing open data portal interfaces to simplify data publication Governments should adopt standardized interfaces for open data portals that streamline the data publication process. A uniform framework for data entry, metadata documentation, and access management will reduce inconsistencies and ensure datasets are discoverable and interoperable D5.3 Strategies towards sustainable open data ecosystems 54 across platforms. Automation tools like data validation and version control can enhance data quality, while open APIs and export functionalities will maximize data sharing and integration, ultimately improving accessibility and encouraging broader participation in the ODE. 7.1.5 Improve technical usability and openness Enhancing data usability requires offering diverse access points, including various data formats, APIs, and user-friendly interfaces that accommodate both technical and non-technical users. Applying FAIR (Findable, Accessible, Interoperable, and Reusable) principles, open standards, and data quality profiling techniques ensures greater accessibility and engagement. By enabling multiple entry points, users can retrieve and interact with data more efficiently, fostering broader adoption and utilization of open data. Policymakers should encourage the adoption of technical openness in data publication by adhering to machine-readable formats, following Open API specifications, and utilizing technical standards that improve data integration across systems. Offering tools such as API explorers and fostering collaboration on technical solutions will ensure that datasets are accessible and reusable. A methodology to quantify the technical openness of datasets should be developed to ensure that open data platforms meet the highest standards of data interoperability. Ensuring inclusivity in open data platforms involves designing intuitive interfaces, offering interactive visualizations, and providing multilingual support. Features such as screen reader compatibility, high-contrast visuals, and accessible navigation cater to diverse user needs, including those with disabilities. By reducing usability barriers, open data becomes more accessible to a wider audience, driving engagement and effective utilization. 7.2 Governance recommendations 7.2.1 Build shared goals and values by promoting shared culture Fostering collaboration among government agencies, private sector actors, researchers, and the public necessitates the development of shared objectives and values. Organizing stakeholder consultations, workshops, and collaborative forums can help align goals and create a common vision for open data initiatives. Promoting a culture of transparency, innovation, and communitydriven solutions strengthens long-term commitment to open data practices. 7.2.2 Scope local problems and build collaborative networks with local stakeholders Addressing local challenges through open data requires conducting needs assessments and actively engaging with local stakeholders. By identifying specific problems and collaborating with communities, policymakers can develop targeted data-driven interventions. Building sustainable data-sharing networks and fostering partnerships with local organizations help ensure that open data initiatives effectively respond to regional priorities and constraints. 7.2.3 Facilitate collaboration between domain experts and open data experts Bridging the gap between subject matter experts and data scientists enhances the quality and applicability of open data solutions. Structured initiatives such as hackathons, interdisciplinary workshops, and collaborative research projects provide opportunities for knowledge exchange. Additionally, offering incentives like grants and training programs encourages long-term engagement and fosters innovation in data-driven decision-making. 7.2.4 Empowering marginalized stakeholders for inclusive ODEs To create a more inclusive ODE, governments should ensure that marginalized groups, such as small-scale data contributors and NGOs, have the resources and opportunities to engage meaningfully in data-sharing activities. This can be achieved by creating accessible, multilingual feedback channels, offering capacity-building programs, and incentivizing participation through training and support. By adopting collaborative governance models and fostering community- D5.3 Strategies towards sustainable open data ecosystems 55 driven data stewardship, policymakers can bridge power imbalances and ensure that the ODE is equitable and sustainable. 7.2.5 Establishing a governance framework for open data validation A dedicated governance framework should be implemented to oversee the technical validation of open data, ensuring data consistency, reliability, and compliance with best practices. This framework should include automated validation tools, periodic audits, and collaborative stakeholder engagement to maintain high data quality standards. 7.2.6 Enabling data analysis through low-code tools and training To enhance the accessibility of open data for non-technical users, open (government) data holders should support the development and distribution of low-code data analysis tools. These platforms will allow users with varying levels of expertise to analyse open data and generate insights. They should also provide training for educators and intermediaries on how to use these tools effectively, ensuring that a wide range of stakeholders can participate in the data-driven decision-making process. Moreover, open (government) data holders should encourage the development and distribution of data augmentation and validation tools that support various dataset types. These tools, possibly developed by intermediaries within the ODE, should be adaptable to different data types and intended uses. By enhancing data quality through these tools, governments can ensure that open data remains accurate, relevant, and usable, further improving its value to stakeholders across sectors. D5.3 Strategies towards sustainable open data ecosystems 56 8 Limitations While we have made significant strides in designing a framework for a sustainable ODE and strategies promoting its sustainability, we discovered several limitations in this work. First, technology is constantly evolving, which has been a challenge. As we worked on designing the ODE framework over the years, new tools and methods emerged, which sometimes required to update our work or to reassess the relevancy of studied technologies. Technologies like AI and data infrastructure are rapidly evolving, and our strategies may not always keep up. Second, multidisciplinary research played a huge role in shaping our work, but it also made things more complicated. We brought in expertise from different fields, all highly relevant to the ODE, which was great for broadening our approach. However, this also led to moments of confusion as we tried to merge different ideas, methods, and terminology. This added complexity to the research process. Furthermore, managing multidisciplinary research posed a significant challenge and introduced limitations in our work. Some of the key limitations we observed include differing terminologies and frameworks, varying research methodologies, conflicting priorities and expectations, and challenges with data compatibility and integration. Additionally, the design and validation of our ODE framework was not a simple task. It required a delicate balance between making it flexible enough for different contexts while still providing a solid structure. The process was iterative, and as we progressed, we found ourselves refining ideas more than expected, which led to several new challenges along the way. Lastly, as we moved through the project, our understanding of key concepts like inclusiveness and circularity evolved. This shift helped us improve our approach, but it also showed how hard it is to keep a long-term project consistent when new insights keep emerging. D5.3 Strategies towards sustainable open data ecosystems 57 9 Conclusion The main objective of this deliverable was to design an overarching sustainable ODE framework and to propose strategies to pave the way towards a user driven, circular, inclusive, and skill-based Open Data Ecosystem (ODE). The proposed ODE framework serves as a middle step a comprehensive prototype for achieving this vision. It integrates key components such as stakeholders, data provision and usage, ODE drivers, driving forces, new concepts (e.g., AI4Data and Data4AI), incentives, values, collaboration, and infrastructure. By emphasizing continuous focus on resources, governance, and enabling environments, the framework provides a structured approach to addressing the multi-dimensional challenges of ODEs. The development of the framework also resulted in a better understanding of the concepts of inclusivity and circularity of ODE. Initially, inclusiveness was framed primarily around the inclusion of non-governmental actors as data providers, and circularity focused on non-government entities enhancing and returning open government data (OGD) back into the ecosystem. However, our findings indicate that these definitions were too narrow. Inclusiveness must also encompass equity in data access and usability—addressing issues of representation, language, and digital capacity—while integrating user driven principles such as the FAIR framework. Similarly, circularity should not be limited to improved OGD but expanded to any valuable contribution users of open data make to the ecosystem, whether through improved open datasets, tools, services, or practices. We applied an inductive research methodology to identify key elements of ODE which were further mapped to the ODE drivers and corresponding ODE framework components so that we can make them actionable strategies tailored to particular ODE framework components and drivers. We arrived at a set of 29 strategies (section 6.1, 6.2, and 6.3 subheadings) and additional approaches after synthesizing 83 initial strategies. This resulted in eleven recommendations for institutions and actors producing, using or otherwise contributing to the ODE, to be implemented in order to support sustainable ODEs. These 11 recommendations may improve usability and accessibility, quality, and participation through a set of 5 technical and 6 governance strategies. To sustain the ecosystem, the facilitation of open data sharing among non-government data holders is crucial. Encouraging private entities, research institutions, and civil society organizations to share data requires addressing legal, technical, and organizational barriers. Establishing clear licensing frameworks, anonymization techniques, and sustainable open data-sharing mechanisms can help build trust among non-government data holders. Open-source platforms can further support seamless collaboration by ensuring interoperability and compliance with existing data standards. Partnerships and coordination efforts should also be strengthened to promote contributions from diverse stakeholders. In conclusion, achieving a sustainable ODE requires a multifaceted approach that balances inclusivity, circularity, user driven engagement, and skill-based empowerment. This deliverable represents a step forward in the evolution of ODEs. By addressing the gaps in current systems and leveraging the proposed strategies, the framework and strategies outlined in this study pave the way for a more inclusive, equitable, and innovative open data future. Ultimately, this work contributes to the broader goal of harnessing open data as a powerful tool for societal and economic transformation. D5.3 Strategies towards sustainable open data ecosystems 64 Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist , 55 (1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 Safarov, I., Meijer, A., & Grimmelikhuijsen, S. (2017a). Utilization of open government data: A systematic literature review of types, conditions, effects and users. In Information Polity: The International Journal of Government & Democracy in the Information Age (Vol. 22, Issue 1, pp. 1–24). Safarov, I., Meijer, A. J., & Grimmelikhuijsen, S. (2017b). The Challenges of Open Data Use: A Systematic Review of the Literature. Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign , 4 (1), 5–18. https://doi.org/10.1080/15710880701875068 Sansone, S.-A., Rocca-Serra, P., Field, D., Maguire, E., Taylor, C., Hofmann, O., Fang, H., Neumann, S., Tong, W., Amaral-Zettler, L., Begley, K., Booth, T., Bougueleret, L., Burns, G., Chapman, B., Clark, T., Coleman, L.-A., Copeland, J., Das, S., … Hide, W. (2012). Toward interoperable bioscience data. In Nature Genetics (Vol. 44, Issue 2, pp. 121–126). https://doi.org/10.1038/ng.1054 Santoro, C., Flores, C. C., Nikiforova, A., Zuiderwijk, A., & Crompvoets, J. (2024). Exploring Governance Modes in Open Data Initiatives: Insights from France and Ireland . Proceedings of Ongoing Research, Practitioners, Posters, Workshops, and Projects of the International Conference EGOV-CeDEM-ePart 2024. Schwoerer, K. (2022). Whose open data is it anyway? An exploratory study of open government data relevance and implications for democratic inclusion. Information Polity , 1 (1). https://doi.org/10.3233/IP-220008 Share-PSI 2.0. (2016). The network for innovation in European public sector information . https://www.w3.org/2013/share-psi/PR3-20160826/pdf Song, H., Bethard, S., & Thomer, A. (2024). Metadata Enhancement Using Large Language Models. Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) , 145– 154. https://doi.org/10.18653/v1/2024.sdp-1.14 Sorri, K., & Seppänen, M. (2021, June 20). Co-creation of Ecosystem-level Value Propositions. Innovating Our Common Future: ISPIM Innovation Conference . Styrin, E., Luna-Reyes, L. F., & Harrison, T. M. (2017). Open data ecosystems: An international comparison. In Transforming Government: People, Process and Policy (Vol. 11, Issue 1, pp. 132–156). https://doi.org/10.1108/TG-01-2017-0006 Sugg, Z. (2022). Social barriers to open (water) data. Wiley Interdisciplinary Reviews: Water , 9 (1). https://doi.org/10.1002/wat2.1564 Taylor, J. (2017). Going Public: Using the Cloud to Improve Project Delivery. In Information Systems Management (Vol. 34, Issue 2, pp. 105–116). Taylor, L., Mukiri-Smith, H., Petročnik, T., Savolainen, L., & Martin, A. (2022). (Re)making data markets: An exploration of the regulatory challenges. Law, Innovation and Technology , 14 (2), 355–394. https://doi.org/10.1080/17579961.2022.2113671 Thomas, D. R. (2006). A General Inductive Approach for Analyzing Qualitative Evaluation Data. American Journal of Evaluation , 27 (2), 237–246. https://doi.org/10.1177/1098214005283748 Toorajipour, R., Oghazi, P., & Palmié, M. (2024). Data ecosystem business models: Value propositions and value capture with Artificial Intelligence of Things. International Journal of Information Management , 78 , 102804. https://doi.org/10.1016/j.ijinfomgt.2024.102804 Toots, M., McBride, K., Kalvet, T., Krimmer, R., Tambouris, E., Panopoulou, E., Kalampokis, E., & Tarabanis, K. (2017). A Framework for Data-Driven Public Service Co-production. In M. Janssen, K. Axelsson, O. Glassey, B. Klievink, R. Krimmer, I. Lindgren, P. Parycek, H. J. Scholl, & D. Trutnev (Eds.), Electronic Government (Vol. 10428, pp. 264–275). Springer International Publishing. https://doi.org/10.1007/978-3-319-64677-0_22 D5.3 Strategies towards sustainable open data ecosystems 65 Tseng, H.-L., & Nikiforova, A. (2025). Navigating the High-Value Dataset Landscape From Determination to Impact: Lessons From Taiwanʼs OGD Ecosystem. Information Polity , 15701255241297864. https://doi.org/10.1177/15701255241297864 Turgunbaev, R. (2024). Machine learning and its use in the automatic extraction of metadata from academic articles. International Journal of Engineering and Computer Science , 1–7. Van Dijck, J., Poell, T., & De Waal, M. (2018). The Platform Society (Vol. 1). Oxford University Press. https://doi.org/10.1093/oso/9780190889760.001.0001 Van Loenen, B., Zuiderwijk, A., Vancauwenberghe, G., Lopez-Pellicer, F. J., Mulder, I., Alexopoulos, C., Magnussen, R., Saddiqa, M., Dulong De Rosnay, M., Crompvoets, J., Polini, A., Re, B., & Casiano Flores, C. (2021). Towards value-creating and sustainable open data ecosystems: A comparative case study and a research agenda. JeDEM - eJournal of eDemocracy and Open Government , 13 (2), 1–27. https://doi.org/10.29379/jedem.v13i2.644 Van Loenen, B., Zuiderwijk, A., Vancauwenberghe, G., Lopez-Pellicer, F. J., Mulder, I., Alexopoulos, C., Magnussen, R., Saddiqa, M., Dulong De Rosnay, M., Crompvoets, J., Polini, A., Re, B., & Flores, C. C. (2021). Towards value-creating and sustainable open data ecosystems: A comparative case study and a research agenda. eJournal of eDemocracy and Open Government , 13 (2), 1–27. https://doi.org/10.29379/jedem.v13i2.644 van Schalkwyk, F., Willmers, M., & McNaughton, M. (2016). Viscous Open Data: The Roles of Intermediaries in an Open Data Ecosystem. Information Technology for Development , 22 , 68–83. https://doi.org/10.1080/02681102.2015.1081868 Verhulst, S., Young, A., Zahuranec, A., Calderon, A., Gee, M., & Aaronson, S. A. (2020). The Emergence of a Third Wave of Open Data: How To Accelerate the Re-Use of Data for Public Interest Purposes While Ensuring Data Rights and Community Flourishing. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3937638 Weber, M., Fu, D., Anthony, Q., Oren, Y., Adams, S., Alexandrov, A., Lyu, X., Nguyen, H., Yao, X., Adams, V., Athiwaratkun, B., Chalamala, R., Chen, K., Ryabinin, M., Dao, T., Liang, P., Ré, C., Rish, I., & Zhang, C. (2024). RedPajama: An Open Dataset for Training Large Language Models (arXiv:2411.12372). arXiv. https://doi.org/10.48550/arXiv.2411.12372 Weerakkody, V., Janssen, M., & Dwivedi, Y. K. (2017). Transparency and Open Data Policies . Weerakkody, V., Kapoor, K., Balta, M. E., Irani, Z., & Dwivedi, Y. K. (2017). Factors influencing user acceptance of public sector big open data. Production Planning & Control , 28 (11–12), 891–905. https://doi.org/10.1080/09537287.2017.1336802 Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803932 Wiener, M., Sommer, F. T., Ives, Z. G., Poldrack, R. A., & Litt, B. (2016). Enabling an Open Data Ecosystem for the Neurosciences. Neuron , 92 (3), 617–621. https://doi.org/10.1016/j.neuron.2016.10.037 Yuan, Y. (2019). Economic and Social Impact of Open Data . Zuiderwijk, A., & Janssen, M. (2014a). Open data policies, their implementation and impact: A framework for comparison. In Government Information Quarterly (Vol. 31, Issue 1, pp. 17– 29). https://doi.org/10.1016/j.giq.2013.04.003 Zuiderwijk, A., & Janssen, M. (2014b). The negative effects of open government data— Investigating the dark side of open data. Proceedings of the 15th Annual International Conference on Digital Government Research , 147–152. https://doi.org/10.1145/2612733.2612761 Zuiderwijk, A., Janssen, M., & Davis, C. (2014). Innovation with open data: Essential elements of open data ecosystems. Information Polity , 19 (1–2), 17–33. https://doi.org/10.3233/IP140329 Zuiderwijk, A., Janssen, M., van de Kaa, G., & Poulis, K. (2016). The wicked problem of commercial value creation in open data ecosystems: Policy guidelines for governments. In Information Polity (Vol. 21, Issue 3, pp. 223–236). https://doi.org/10.3233/IP-160391 D5.3 Strategies towards sustainable open data ecosystems 66 Zuiderwijk, A., Volten, C., Kroesen, M., & Gill, M. (2018). Motivation Perspectives on Opening up Municipality Data: Does Municipality Size Matter? In Information (Vol. 9, Issue 11, pp. 1– 26). https://doi.org/10.3390/info9110267 D5.3 Strategies towards sustainable open data ecosystems 67 Annex 1 - Open Data Ecosystem Strategies Table 4: Strategies for user driven open data ecosystems Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation Coordinating functions should be discharged by public administrations to bring together open data providers and open data users to openly discuss their needs. Public administrations should routinely consult with open data users and providers from the citizenry, the private sector and the public sector to understand evolving needs of these stakeholders and undertake open data initiatives that respond to these needs. Public administrations should also provide funding to collaborative open data initiatives, which can then serve a coordinating function. At the same time, coordinating functions can also lead to open data ecosystems in unexpected ways. For instance, the UK mapping agency – Ordnance Survey – did create large geographic datasets, but many open data users were dissatisfied that these datasets were not freely distributed. This led to the birth of OpenStreetMap (see https://wiki.openstreetmap.org/wiki/History_of_OpenStreetMap) – an open platform for crowd-source geospatial data, which could be considered an example of a sustainable ODE. Future research can explore mechanisms for non-governmental actors to discharge coordinating functions. Governance Coordinating functions to be discharged by governmental actors. With the right incentives, such functions can be discharged by nongovernmental actors as well Partnerships should be formed between public administrations / international organisations and other civic open data user groups to codevelop open data initiatives, which can prompt contributions from other actors. This is another tool for collaborations between public bodies and other civic actor groups. These partnerships enable other actors, like non-specialised users, to contribute to open data initiatives. These partnerships can be formal. In many cases however, NGOs initiate projects and build partnerships with government bodies on their own, which could take the form of more informal partnerships. In such cases, there should be greater willingness on the part of government bodies to engage in such partnerships with NGOs. The partnership (whether formal or informal) should also include a feedback loop, where citizen-contributed data (as in the case of CityLAB Berlin) and NGO-created data (as in the case of Femicides in Europe) are integrated into open government data initiatives. Governance, Market, Incentives Willingness on part of public administrations to engage in formal and informal partnerships with other civic actor groups A strong interorganisational culture oriented towards effecting social and economic impact of open data in a participative manner is also useful. Organisations should have a strong internal culture of contributing to as well as realising value from ODEs. This can then enable inter-organisational cultures, as well as potentially foster partnerships. These organisational cultures can also serve as motivations for actor groups to engage in open data initiatives. Such cultures can be cultivated using formal institutions like legal frameworks, together with informal approaches to creating shared understandings among the various open data actors. Culture Cultivating strong organisational cultures oriented towards open data Enhancing accessibility of metadata Metadata must remain accessible despite the unavailability of datasets, facilitating the ongoing utilization of historical context for research purposes. Ensures that datasets remain significant and 1. Implement metadata repositories 2. Provide documentation for metadata usability D5.3 Strategies towards sustainable open data ecosystems 68 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation valuable throughout time, hence contributing to the sustainability of the ecosystem. Better interfaces and feedback mechanisms for open data provision Important to design and develop the necessary technical mechanisms including new interfaces and user interaction methods that enhance the way NGD holders are creating and sharing open data that is available, usable and valuable to a wide range of stakeholders, fostering a more inclusive and effective ODE. Data provision, Enabling Environment, Infrastructure Enhance the foster improved open data platforms' interface and feedback mechanisms by adopting User driven design, inclusion of diverse user groups in society and active feedback loops while making use of new technologies wherever this is possible; e.g., Natural Language Processing, Virtual and Augmented Reality, AI, and Chatbots. User driven Feedback mechanisms Feedback mechanisms, including dataset rating systems, dataset comments, and direct user feedback through discussion forum, are essential for enhancing data quality and ensuring user-friendly portals. Integrates user contributions so that it can improve overall dataset relevance and usability. Establish effective feedback mechanisms. Identification of gaps between the needs of user groups and the current features of open data platforms with respect to user interfaces user-interfaces are basic part of the FAIR principles when it comes to findability of open data. The Findability aspect of FAIR Data Provision is the primary focus. This principle of data discovery is acknowledged as the fundamental premise of data use. Data and metadata should be easy to find for both humans and computers. open data infrastructure The data should be findable to wide range of users with diverse searching options. FAIR Principles open data portal must be up to par of FAIR data principles for all users. It supports, FAIR principles for specialist and non-specialist open data stakeholders open data infrastructure The FAIR Principles should be applied to achieve the User driven ODE. Smooth integration of open data from diverse domains Open data publishers require a smooth data integration process to combine data from diverse sources to make a collective decision. In this way, several issues with the open data portals such as data interoperability, data transformation, data harmonization and integration of tools and technologies can be achieved. open data infrastructure ODE should facilitate the integration of open data from cross-domains (either government, nongovernment, NGOs). D5.3 Strategies towards sustainable open data ecosystems 69 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation User needs quality measures and proper profiling of the data By providing proper data profile and quality measures of the open data can enhance its useability. Driving Force: Data quality Follow some kind of quality standards and data profiling techniques to achieve the user-needs regarding the data quality and usability. Data consumers capacity building visualization skills, training in data analytics Value need to build my skillset in the field of data visualisation and build capacity regarding data issues (fragmentation, aggregated data, unreliable data, and sensitive data). Multi-format data access provide the end-users with open data in multi-formats such as open formats, APIs, bulk downloads to tackle diverse needs open data infrastructure Use open standards, open formats available to engage the users with diverse needs. Some users require data in bulk, other requires APIs, and other requires Linked open data Unified view for open data publication process Open Data providers require a unified view of publishing dashboard. For instance, several government levels participate in open data publications, regional, decentralized, and municipal level. They should have access to unified portal to publish their datasets open data infrastructure Develop a standardized open data portal interface to make the data publication process easier. Anonymization of the open dataset before publishing must be the norm Open Data providers must have access to tools to increase the anonymity of open data before making it public. open data infrastructure Use/develop tools to anonymize the open data Making available integrated datasets from diverse fields for users with diverse needs is needed The necessity for integration of data and accessibility feasible for user groups with varying needs (e.g., visual impairment, dyslexia, and others) open data infrastructure Introduce tools for unified view of data and physically impaired user groups. Portability of infrastructure in the open data domain The portability of infrastructure will help transfer the knowledge of open data infrastructure to another organization open data infrastructure Seek guidance and infrastructure from the already developed portals from other countries, areas, regions to develop your one. Community Portal for interaction The data portal should provide a place where users can connect with other users in a community, based on similar interests. Enabling Environment Open Data community portal development helps in identification of issues with data, the possible use of data, future improvements. Technical openness of the datasets By opening the datasets, it technically means to follow the dataset standards, formats, and other protocols to enhance the data integration across different systems and to facilitate the Data Provision Adopting standards, requirements, and guidelines to open data technically. D5.3 Strategies towards sustainable open data ecosystems 70 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation better accessibility of the open data. Technical openness contributes to the technical interoperability of open data. Allowing the user to upload their dataset A user can upload an updated version of the existing dataset. In this way, other users can use cleaned, modified, augmented, or transformed datasets for specific tasks. This feature also contributes to the open data interoperability. Data Provision developing a pipeline to engage the other open data providers as well. Boundary-making in relation to ODEs To define the boundaries of an ODE, actors should be aware of the socio-technical conditions where their interactions with other actors take place. These conditions refer on one hand to social components such as historical, geographical aspects, social and cultural norms, organization norms and community affiliations, as well as practices, traditions, personal motivations and values. On the other hand, the technical aspects include soft and hard data infrastructures, interoperability practices, standards, laws and regulations. Ecosystem mapping can be done using tools from the discipline of design thinking and theoretical principles from the discipline of information visualization and communication. ODE Awareness is needed of the sociotechnical conditions in which interactions between actors take place. Supporting communities of ODEs To support the formation of communities around the use of open data, actors of the ODE should be knowledgeable of the purposes and practices that can be affected by open data. Shared purposes typically revolve around public and/ or local concerns, therefore they directly affect citizens, local communities or digital communities. Communities of practice typically form when experts, practitioners and academics explore societal problems by developing knowledge, tools, practices that address those problems. Research done in the field of participatory design focuses on empowering communities of shared purpose, while disciplines such as data science and engineering, engineering design, computer science typically form communities of practice around open data. Enabling environment Actors should be knowledgeable of the purposes and practices that can be affected through open data. Encouraging participation and shared decision-making To encourage polycentricity through participation and collaborative decision making in the ODE, actors with more power such as institutions, organizations, communities that represent the status quo should ensure that those typically with less power such as citizens, students, research participants as well as less represented and disadvantaged groups are being actively encouraged to communicate their feedback, needs and concerns, as a first step. Moreover, they should be empowered to actively contribute to the creation of strategies and plans, practices and assessments, products and services. The discipline of (critical) data studies provide tools, approaches and theoretical concepts that challenge existing power structures and propose more just and equitable alternatives. Governance Powerful actors and organizations should ensure that less powered, less represented and disadvantaged actors and organizations are encouraged to provide feedback and even actively contribute. Considering appropriate legal mechanisms To encourage the use of open licenses – including open data licenses for databases, Creative Commons licenses for content, and open source software licenses for software code and other software artefacts. Licenses have been central to the creation and continuation of knowledge, information and data commons. Where the data in question does not relate to any personal or sensitive information, broad licenses should be used that impose little to no Enabling Environment (Regulations) Use of open licenses should be encouraged D5.3 Strategies towards sustainable open data ecosystems 71 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation restriction on reuse. Further, governments should, to the extent possible and subject to security concerns, procure open-source infrastructures for open data technologies. Here, the open licenses also serve to instil a culture of communing. Designing an ecology of interoperable projects To focus on interoperability and data portability and have a broad understanding of these concepts. In particular, efforts for interoperability should encompass technical interoperability (through, for example, semantic and syntactic interoperability of open data systems/portals and through standardized formats for (open) data, as also noted in the empirical data collected) as well as generative interoperability (through adoption of policies aimed at nurturing public spaces for decision-making in relation to open data needs and challenges). Support should be provided to regulatory measures aimed at broad interoperability and portability, through advocacy and political action. open data infrastructure Focus should be put on - different types of - interoperability and data portability Ensuring sustainability of ODEs In terms of economic sustainability of ODEs, advocacy for availability of public funds can be accompanied with insights from economic and business models of digital commons/information commons/data commons projects, from collaborative peer production. Contributions to social sustainability can be ensured through the adoption of critically situated approaches to participation from critical data studies. Enabling environment To ensure sustainability of ODEs, different types of funding models (public funding, private funding, etc.) should be considered. The availability of open data platform based on federated architecture. To some extent, a federated architecture could address the current shortcomings where open data across different domains and/or jurisdictions are segmented due to multiple open data providers. The federated architecture means that users can easily integrate open data from multiple domains and/or jurisdictions based on interoperable data standards, but the maintenance of the data still falls under the responsibility of the original provider. This would be valuable to address complex global and local challenges requiring a multidisciplinary and multiscale data-based approach. Intermediaries Open data intermediaries could consider providing an open data platform based on federated architecture. Soliciting various needs of existing and potential open data actors Open data intermediaries can play a role to understand and gather the needs of existing and potential open data actors with respect to the supply, processing, and reuse of open data. Certain needs may not be immediately apparent but can be uncovered by understanding the challenges that these actors face. These needs may also not be directly visible unless there are avenues for these needs to be communicated by different actors. Open data intermediaries can provide such avenues. Intermediaries Open data intermediaries could play an active role in identifying and understanding various needs of open data actors. They can then help to address these needs or communicate these needs to those who may be able to address (such as data providers). D5.3 Strategies towards sustainable open data ecosystems 72 Table 5: Strategies for inclusive open data ecosystems Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation There should be continued focus on publication of open government datasets by public administration. To realise value out of open data, it is important that high-quality open datasets are easily available. Accordingly, public administrations should continue to release open datasets, based on legal mandates as well as on a voluntary basis. To ensure that public administrations prioritize release of open data, it may be important to evaluate different enforcement as well as incentive structures. Enabling Environment (Regulations) Continued implementation of legal mandates for open government data. Shared open infrastructures for publishing and use of data and information are important, including for non-governmental data holders Shared open infrastructures for accessing, publishing and using open data are important, as not all actors may have the resources necessary to develop such infrastructures from scratch. Infrastructure Public financing and maintenance of shared open infrastructures, like the European Open Science Cloud Facilitating multi-format data access for different users Providing datasets in multiple formats enhances accessibility for NGOs, businesses, and researchers with distinct technical skills It encourages inclusiveness ens uring the accessibility of data is in accordance with the needs of users. 1. Enable diverse data formats and API options 2. Build accessible user interfaces to enhance usability for non-technical users. Addressing barriers to local community access to open data Communities require localized, interpretable datasets accompanied by metadata to utilize data effectively for decision-making and advancements in society. Promotes inclusivity by enabling access and utility of open data at a community level. 1. Provide tools for local data interpretation 2. Create community-specific manuals and localized data insights platforms Open Data contribution from nongovernment actors The need to escape the narrow limits of only governmental bodies being active contributors to the ODE; rather, include other stakeholders (non-governmental data groups) as potential contributors. Enabling Environment Analyse the barriers, motivations, and technical requirements for nongovernment data holder groups to share their data and propose technical mechanisms to enable/facilitate this purpose. Alignment of interests The framework must align organizational, societal, and individual interests to foster collaboration and shared responsibility Collaboration, Values Create shared goals and values among diverse stakeholders within an ODE. This ensures cohesive collaboration and maximizes the ecosystemʼs ability to deliver value D5.3 Strategies towards sustainable open data ecosystems 73 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation across its users, contributors, and beneficiaries. Community-centric design Students' motivations highlight the importance of focusing on real-world relevance and fostering their contributions. Infrastructure Community-centric design prioritizes the needs, preferences and involvement of communities in the ODE. Lowering barriers to data access Ensuring the inclusivity of open data portals involves the provision of multilingual support, user-friendly interfaces, and accessibility for non-technical users. Promotes balanced participation and equitable opportunity by expanding the ecosystem to underrepresente d groups. Simplify interfaces with user-friendly designs and incorporate multilingual support and accessibility features. Governance mechanisms to support bottom-up initiatives Open data governance mechanisms should also support communities and local environment to interact and benefit of ODEs not just as users of data. Local partnerships and networks might be supported by new governance mechanisms with a bottom-up perspective Enabling Environment, Data Usage, Infrastructure Scoping at local problems and building networks of actors and open dataflow among them Interdisciplinary collaboration Collaboration between domain-experts and data experts. Domain-experts can contribute with contextual knowledge and priorities that need to be addressed with open data. Data experts can contribute their skills for data analysis (cleaning, visualization, etc.). Enabling Environment Apply strategies to incentivize and facilitate the collaboration of domain experts and data experts Open data event design Open data events (such as hackathons, mapathons, etc.) can contribute to increase the inclusiveness of ODEs if designed appropriately (group formation methods, agenda, activities, etc.) Enabling Environment Open data events should be designed to be accessible by nonspecialist users. Institutionalization of open data events Public administration bodies can organize and financially support open data events, which provide networking and learning opportunities, especially for non-specialist users. Enabling Environment The public administration should institutionalize and support open data events Comprehensive dataset coverage Availability of a wide variety of thematic datasets such as from different domains such as from science and transport and so on Data Provision Adopting thematic classification of datasets from EDP or other classification such as SDGs CARE principles account for power dynamics in ODEs. Legal avenues for redistributing value need to recognise and account for the politics of open data production and re-use. Normatively, this can be achieved through an orientation towards ‘data justiceʼ. (Taylor, 2017) In terms of practical implementation, legal frameworks for open data initiatives could refer to CARE principles in addition to the FAIR principles for data quality management. (Di Staso et al., 2023). ODE In terms of practical implementation, legal frameworks for open data initiatives could refer to CARE principles in addition to the FAIR principles for data quality D5.3 Strategies towards sustainable open data ecosystems 80 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation The financial value created or captured by actors depend on the roles they play in the ODE, as they can play multiple roles at the same time. 1/6 Actors in an ODE (e.g., government agencies, companies, non-profit organisations, and citizens) are not wedded to any particular role. In other words, they can play multiple roles at the same time or different roles in different contexts. ODE Commitments to provide continuous and adequate funding for maintaining and developing open data infrastructure and support ought to be institutionalized, for example, through supranational, national, or local laws. Conversely, open data has to be considered as an infrastructure (like roads, clean water, etc.) and be accounted for in the tax policy design that, at the same time, does not disincentivize companies from continue using open data. The financial value created or captured by actors depend on the roles they play in the ODE, as they can play multiple roles at the same time. 2/6 Actors in an ODE (e.g., government agencies, companies, non-profit organisations, and citizens) are not wedded to any particular role. In other words, they can play multiple roles at the same time or different roles in different contexts and these roles influence the types of financial value they can create or capture. ODE Some restructuring, streamlining, and coordination of open data management and provision may be necessary within and across government agencies to save (transaction) costs as open data providers further. At the same time, it may also be worth coordinating with other agencies that are not necessarily open data providers to implement contractual obligations with third-party vendors that collect data through their governmentfunded projects/undertakings to give such data to the government free of charge (including augmented data on top of initially government open data), so that (some of) the data can then be provided as open data. This saves the government from (re)collecting the data. D5.3 Strategies towards sustainable open data ecosystems 81 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation The financial value created or captured by actors depend on the roles they play in the ODE, as they can play multiple roles at the same time. 3/6 Actors in an ODE (e.g., government agencies, companies, non-profit organisations, and citizens) are not wedded to any particular role . In other words, they can play multiple roles at the same time or different roles in different contexts and these roles influence the types of financial value they can create or capture. ODE One possible way to recuperate some of the costs of providing open data is for government agencies to also provide value-added services based on open data at fees. This involves revisiting, clarifying, and potentially amending existing laws related to government-market roles and competition. The financial value created or captured by actors depend on the roles they play in the ODE, as they can play multiple roles at the same time. 4/6 Actors in an ODE (e.g., government agencies, companies, non-profit organisations, and citizens) are not wedded to any particular role . In other words, they can play multiple roles at the same time or different roles in different contexts and these roles influence the types of financial value they can create or capture. ODE Government agencies should be obligated to use open data whenever it is available and to inform open data providers of any errors in the data. This saves costs for them as open data users as well as to open data providers in ensuring data quality. The financial value created or captured by actors depend on the roles they play in the ODE, as they can play multiple roles at the same time. 5/6 Actors in an ODE (e.g., government agencies, companies, non-profit organisations, and citizens) are not wedded to any particular role. In other words, they can play multiple roles at the same time or different roles in different contexts and these roles influence the types of financial value they can create or capture. ODE Tax incentives may be worth considering enticing companies and citizens to contribute to open data initiatives as funders or data providers. The case of the Wikimedia Foundation and OpenStreetMap Foundation being granted taxdeductible status in some countries, where companies and citizens who donate to these organizations can claim tax deductions, is a good example to consider for other open data initiatives. The financial value created or captured by actors depend on the roles they play in the ODE, as they can play multiple roles at the same time. 6/6 Actors in an ODE (e.g., government agencies, companies, non-profit organisations, and citizens) are not wedded to any particular role. In other words, they can play multiple roles at the same time or different roles in different contexts and these roles influence the types of financial value they can create or capture. ODE Governments should recognize that they are no longer the only open data providers. Hence, they should consider supporting citizengenerated or crowdsourced open data projects, such as D5.3 Strategies towards sustainable open data ecosystems 82 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation OpenStreetMap, by financially supporting or contributing their own data to these projects. At the same time, governments can also leverage open data from these projects as one of their data sources. This may lead to more cost-sharing in open data initiatives within the ecosystem. In ODEs, value is seen as multifaceted and dependent on the dynamics of user engagement, circularity, inclusivity, and skill-based development within open data systems The complexity of ‘valuingʼ open data arises from the various stages in the open data usage process, which range from collection and storage to analysis and sharing. Each stage presents unique requirements and risks that need to be managed to maintain the data's utility and integrity while ensuring responsible data practices that consider privacy, equity, and accountability (S. G. Verhulst, 2021). In the ODEs proposal, the concept of value is approached from a broader, ecosystem-oriented perspective rather than merely traditional use-value. ODE A purpose-driven approach, rather than a User driven one, can enhance ODEs to better reflect the understanding of social value. Purpose-directed models establish clear goals, usually concentrating on encouraging responsible data usage while advancing societal benefits, such as improved healthcare and environmental sustainability. Various open data intermediation value propositions addressing various real-world challenges and providing innovative solutions Greater awareness and inspiration on the potential value-added services based that can be offered based on open data should be promoted across various sectors/domains to encourage the emergence of more open data intermediaries that help address real-world challenges. Intermediaries Open data advocates (such as open data NGOs, researchers, and public agencies) could engage with domain-specific industry players (such as mobility, agriculture, energy sectors) to showcase the potential use of open data and exemplary value-added services. Advisory, consultancy or support services to existing or potential open data actors Some existing or potential open data actors may require additional various forms of support and knowledge to supply, process, or reuse open data. Intermediaries Open data intermediaries (within or outside public sector) should consider introducing functions to provide technical and non-technical supports to other open data actors in supplying, processing, and reusing open data. Initiating engagement and interaction among open data actors Some potential collaborations between open data actors may happen with the help of open data intermediaries that bring different actors together or initiate possible connection or engagement. Intermediaries Beyond holding events such as hackathons or conferences that bring various actors in one place to D5.3 Strategies towards sustainable open data ecosystems 83 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation connect, open data intermediaries may also consider initiating or leading collaborative initiatives that harness the resources and expertise of multiple open data actors. The availability of open-source software for open data supply, processing, and reuse Certain open data actors may face financial constraints to purchase or subscribe proprietary software to supply, process, or reuse open data. Hence, the availability of open-source software may alleviate such constraints. Intermediaries The development of open-source software should be supported, especially by providing funds for open-source software development and maintenance. Sustainable business models for open data intermediaries Open data intermediaries require sustainable business models to ensure its longevity and performance. Intermediaries Incubator or similar programs to support the design and development of open data intermediaries' business models could be initiated to help existing and future open data intermediaries. These programs could be catered not only to for-profit but also nonprofit open data intermediaries, as the former also requires sustainable business models. Fiscal incentives in exchange for contributions to open data initiatives Open data initiatives carried out by non-profit entities such as Wikimedia and OpenStreetMap require sustained funding. The same goes to open-source software initiatives that enable the processing and use of open data. Citizens and companies could be given fiscal incentives (such as tax discounts) in exchange for their contribution to these open data/open-source initiatives, either in monetary or data/content form. Resources Governments should consider offering financial incentives to citizens and companies that support open data or open-source projects. Enabler: Availability of training in data skills and literacy The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From the barrier "Lack of data skills and literacy", the enabler "Availability of training in data skills and literacy" was identified. Incentives By making training in data skills and literacy available, data contribution can be enabled/leveraged Enabler: Availability of appropriate technical tools The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From the motivation "To improve technical skills or internal data processes" and the barrier "Lack of technical tools", the enabler "Availability of appropriate technical tools" was identified. Incentives By making appropriate technical tools available, data contribution can be enabled/leveraged Enabler: Alignment of private value and interests with open data sharing The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From Incentives By aligning private value and interests with open data sharing, D5.3 Strategies towards sustainable open data ecosystems 84 Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation the motivation "Private value" and the barrier "Misaligned goals and interests", the enabler "Alignment of private value and interests with open data sharing" was identified. data contribution can be enabled/leveraged Enabler: Availability of resources (financial, time, people/workforce) The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From the barrier "Lack of resources", the enabler "Availability of resources" was identified. Incentives By making resources (financial, time, people/workforce) available, data contribution can be enabled/leveraged Enabler: Existence of data-sharing communities The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From the motivations "Supporting other stakeholders", "Supporting communities" and "Belonging", the enabler "Existence of data-sharing communities" was identified. Incentives By making data-sharing communities exist, data contribution can be enabled/leveraged Enabler: Awareness about the social impact of open data sharing The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From the motivation "Creating social impact" and the barrier "Lack of awareness", the enabler "Awareness about the social impact of open data sharing" was identified. Incentives By making stakeholders aware about the social impact of open data sharing, data contribution can be enabled/leveraged Enabler: Presence of engagement or enjoyment activities The motivations and barriers of non-government actors to become active contributors to the ODE were found. From these motivations and barriers, 7 key enablers were identified. From the motivation "Engagement / enjoyment", the enabler "Presence of engagement or enjoyment activities" was identified. Incentives By having engagement or enjoyment activities, data contribution can be enabled/leveraged D5.3 Strategies towards sustainable open data ecosystems 85 Table 7: Strategies for skill-based open data ecosystems Key element Description of key-element Connection with ecosystem framework Proposed strategy/recommendation Providing assistance to data intermediaries to improve the quality of data The use of methods/approaches that increase data quality, such as validation and augmentation, is crucial in order to make open datasets better and to ensure that they are accessible to all different kinds of users simultaneously. Intermediaries are given the capacity to increase the utility of data while also developing trust and reliability within the ecosystem. "1. Create and distribute data augmentation tools adaptable to different dataset types Developing low-code tools for educators and data intermediaries Low-code platforms enable teachers and intermediaries to process, analyse, and extract insights without advanced technical expertise, empowering broader participation in the ODE. 2. The tools should be capable of supporting a wide variety of data types and intended uses."