Sustainable open data ecosystems through data quality, governance, and infrastructure: Unlocking social, political and economic value

OPEN LETTER Sustainable open data ecosystems through data quality, governance, and infrastructure: Unlocking social, political and economic value [version 1; peer review: 2 approved] Ramya Chandrasekhar , Melanie Dulong de Rosnay Centre Internet et Société, CNRS, Paris, 75017, France First published: 23 Sep 2025, 5:295 https://doi.org/10.12688/openreseurope.21212.1 Latest published: 23 Sep 2025, 5:295 https://doi.org/10.12688/openreseurope.21212.1 v1 Abstract Open data are crucial for scientific knowledge production, transparency and accountability, as well as innovation. The European Union has implemented various policies and regulatory frameworks for open government data and open scientific data, as well as for data sharing and re-use of non-government data. However, the mere availability of open data does not ensure its reuse and distributional benefit to society, and its production can meet sustainability challenges. Working with open data requires data skills, access to data infrastructures, and regulatory guidance to address privacy, confidentiality and intellectual property requirements. Further, critical scholarship has cautioned against the de facto valorisation of open data, and urges focus on the socio-technical and political aspects of production, dissemination and use of open data beyond mere economic value. This open letter is building upon findings of an interdisciplinary Marie Curie Action Innovative Training Network focussed on ‘Open Data ECOsystems’ (ODECO). It claims that in a datadriven economy and a datafied society, more attention needs to be paid to the conditions within which open data is produced, disseminated and used, and by whom. Accordingly, this open letter provides a set of actionable recommendations for both practitioners and policymakers, to support sustainability as well as economic and social value in open data initiatives, through proposals in areas including data quality, governance, participation and infrastructure. Plain Language summary How can we ensure open data benefits everyone? Open data plays a key role in science, transparency, accountability, and innovation. However, simply making data available as open datasets does not guarantee it will be reused or benefit society equally. Effective use of Open Peer Review Approval Status 1 2 version 1 23 Sep 2025 view view Mohsan Ali , University of the Aegean, Samos, Greece 1. Arwid Lund , Södertörn University, Stockholm, Sweden 2. Any reports and responses or comments on the article can be found at the end of the article. Open Research Europe  Page 1 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 Corresponding author: Ramya Chandrasekhar ([email protected]) Author roles: Chandrasekhar R: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing; Dulong de Rosnay M: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests: No competing interests were disclosed. Grant information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Chandrasekhar R and Dulong de Rosnay M. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite this article: Chandrasekhar R and Dulong de Rosnay M. Sustainable open data ecosystems through data quality, governance, and infrastructure: Unlocking social, political and economic value [version 1; peer review: 2 approved] Open Research Europe 2025, 5:295 https://doi.org/10.12688/openreseurope.21212.1 First published: 23 Sep 2025, 5:295 https://doi.org/10.12688/openreseurope.21212.1 open data requires skills, infrastructure, and clear rules to handle privacy, confidentiality, and intellectual property. Further, the use of open data must result in economic value as well as social value; but often only economic value generation is valourised. Drawing on research from the ODECO project, this open letter calls for more focus on how and by whom open data is produced, shared, and used. It provides a set of actionable recommendations for both practitioners and policymakers, to support sustainability as well as economic and social value in open data initiatives, through proposals in areas including data quality, governance, participation and infrastructure. Keywords Open data, open data governance, participation, open data infrastructure, social value, economic value, principles, recommendations This article is included in the Marie-SklodowskaCurie Actions (MSCA) gateway. This article is included in the Horizon 2020 gateway. Open Research Europe  Page 2 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 Disclaimer The views expressed in this article are those of the author(s). Publication in Open Research Europe does not imply endorsement of the European Commission. Introduction Open data is crucial for scientific knowledge production, transparency and accountability, as well as innovation (Davies et al., 2019; Kitchin, 2014; Ruijer et al., 2017; Van Loenen et al., 2018; Zuiderwijk et al., 2014). Open data is typically understood to include datasets and creative content that are made freely available by creators or stewards, with little to no technical or legal restrictions on their reuse (Open Knowledge Foundation, n.d.). Open data encompasses open government data, open access artefacts resulting from the open science movement, as well as open data generated and/or released by non-government actors such as citizens, non-profit organisations, and commercial actors. Open data is often conceived of as an inherent public good funded by government that is both non-excludable and non-rivalrous, and therefore its access can be unrestricted (see for e.g., (Jetzek et al., 2013; Open Data Charter, 2015). But, there are several barriers to both the production of open data as well as its downstream use (Barry & Bannister, 2014; Conradie & Choenni, 2014; Janssen et al., 2012; Nikiforova et al., 2024; Toots et al., 2017; Zuiderwijk et al., 2012a). On the production side of open government data for instance, there are often opaque decisions made by public administrations on what types of information are selected to be released as open data, as well as inconsistent use of data standards and formats which limit interoperability, i.e. the seamless circulation of data between systems thanks to norms and protocols (Denis & Goëta, 2014; Goëta & Davies, 2016). When it comes to production of non-government open data, it is difficult to incentivise non-government actors such as companies to voluntarily and freely release open datasets, while ensuring due protection of proprietary information as well as personal data, since those cannot be released without an open licence, which is key to ensure legal access and reuse (Enders et al., 2021; Enders et al., 2022). Research programmes such as Horizon Europe have long experimented with mandates to stimulate the release of results as open data to benefit to others (European Research Executive Agency, n.d.). On the use side, lack of public access to internet connectivity, computing infrastructure and the need for data skills mean that the distributional impact of open data is not equal (Bezuidenhout et al., 2017; Lansana et al., 2020; Zuiderwijk & Janssen, 2014a). Further, the mere existence of ‘data heaps’ does not mean that its use is equal, nor always beneficial for society (Gurstein, 2011). Intellectual property and personal data protection issues arise on both the production and the use side (Dalla Corte, 2018; Giannopoulou, 2018; Scassa, 2019). There are also sustainability challenges with open data initiatives, particularly with regard to maintenance of open data resources and infrastructure (Dodds & Wells, 2019) as also with regard to extractive use of open data by reusers who give little to value back to the ecosystem, similar to the free-rider problem in public goods (Bezuidenhout & Chakauya, 2018; Sharma, 2022). The ODECO (Towards a sustainable Open Data ECOsystem) consortium is a Marie Curie Action Innovative Training Networks gathering 15 early career researchers, 20 academics from 8 universities and research institutions, and 18 public and private partner organisations.1 From 2021 to 2025, the members of ODECO conducted interdisciplinary research and participated in routine training activities on the topic of ‘sustainable open data ecosystems.’ ODECO responds to a specific research gap – how to maximise value generation from open data as well as ensure sustainability of open data initiatives, by adopting an ecosystemic perspective. The background paper for ODECO hypothesised four pillars necessary for value-creating and sustainable open data ecosystems – moving from producer-driven to user-driven, exclusive to inclusive, linear to circular, and best-effort to skill-based open data initiatives (Van Loenen et al., 2021). The project outputs of ODECO contain valuable insights on technical, social, economic, legal, governance and design aspects of open data ecosystems. In this open letter, we summarise key findings of ODECO as they relate to user needs and governance of open data ecosystems (including technical as well as non-technical strategies). We further synthesis and present 9 recommendations, which convey actions necessary to improve open data ecosystems, which can be undertaken and funded by policymakers and actors themselves. Similarly to MacFeely et al. (2025) data principles, we present these recommendations as the basis for a normative contribution on the governance and sustainability of open data. An early version of these recommendations was prepared for and presented at the final conference of the ODECO Consortium, held in Athens from May 14 to 16, 2025. This open letter is structured in three parts. First, we discuss the state-of-the-art on open data research, initiatives and policy, with a focus on the European Union (EU). Second, we introduce the ODECO project, and outline the methodology by which we extracted practical recommendations for sustainable open data ecosystems from the ODECO project outputs. Finally, we present a set of 9 recommendations meant to support the future of open data, by imagining, building and maintaining open data ecosystems that are financially, socially and ecologically sustainable, equitable, and empower all stakeholders. Accordingly, these recommendations are relevant for researchers, data infrastructure builders, funders and policymakers interested in open data as well as data reuse and more broadly, data science, public policy and research policy. Open Data policy and regulation in the EU From open government data While the word “data” itself is polysemic, “open data” is equally if not more polysemic in nature. The open data movement 1 They are listed at the end of this article. Page 3 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 has been related to open government (where data held by public bodies is made publicly and freely accessible to ensure transparency and return on taxpayers’ money), open source (where datasets made available for access and reuse in repositories under permissive open licenses which emerged for free and open source software) and open science (where research is sought to be made publicly available and reproducible through open access databases, platforms and infrastructure). As a result, open data is a “malleable” concept that acquires different meanings in different contexts of democracy and governance (Gray, 2014) and became a fundamental pillar for science and evidence-based and participatory policy. Open data is commonly understood as digital creative or knowledge works and datasets made freely accessible for use and reuse, with little to no legal, economic or technical restrictions (Open Knowledge Foundation, n.d.). Institutionalised approaches to open data emerge from open government as well as from open science. Open government data was originally made available on request, by virtue of freedom of information laws, where users had to “pull” information out of public administrations (Whittington et al., 2015). However, with the digitalisation of public sector information and the diffusion of the open source, open science, and open government movements, legal frameworks as well as institutionalised practices for publication and dissemination of open datasets and open content by public administrations became more widespread, supplementing the “pull” model with the “push” model (Id). Non-government actors also became active participants in open data initiatives, by voluntarily publishing non-government data as open datasets, offering services for other open data initiatives, as well as using open data for different purposes, with the metaphor of “spill” often used to describe the release of open data from such non-government actors (Id). Requiring the release of public sector information as open datasets combined with open licenses that invert the logic of copyright to enable wider use and reuse of such information constitute the foundational legal instruments for open data (Giannopoulou, 2018), which were not without legal and institutional applicability challenges (Dulong de Rosnay & Janssen, 2014). Further, the open science movement contributed technical standards for improving the provenance, findability and usability of open datasets. A crucial contribution is the FAIR principles – which propose four characteristics for scientific data – Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016). In the EU, the first Directive encouraging public administrations and public institutions in members states to publish more open government datasets was released in 2003, and subsequently modified in 2013 to encourage the release of such datasets in machine-readable formats (Valli Buttow & Weerts, 2022). In 2019, this legal framework was replaced with the Open Data Directive, which requires member states to make certain categories of public sector information open by default, on which only marginal fees can be levied for “reproduction, provision and dissemination of documents as well as for anonymisation of personal data and measures taken to protect commercially confidential information”. The Open Data Directive also introduced the concept of ‘high-value datasets’ - six thematic categories of datasets which are to be made freely available by public administrations on national open data portals and under open licenses, in machine-readable format, and accessible through both application programming interfaces (APIs) and bulk downloads. These six thematic categories are geospatial, earth observation and environment, meteorological, companies and company ownership, and mobility. Article 10 of the Open Data Directive also requires member states to support open access policies for publicly-funded research data, and the use of the FAIR principles for such data. Most recently in 2023, a new regulation known as the Data Governance Act was also implemented, which enables conditional access to public sector information which cannot be released openly, for instance due to personal data protection, commercial secrecy or statistical confidentiality. In the EU, public policies and regulatory frameworks for open data are motivated by a desire to unlock primarily economic value from open data (European Commission, 1989; European Commission et al., 2015; Publications Office of the European Union, 2020). But the value of open data is multi-faceted, and includes social as well as economic value (López Reyes & Magnussen, 2022; Shaharudin et al., 2024; Zuiderwijk & Janssen, 2014b). This includes the creation of new open datasets (often by combining or adding to existing open datasets), identifying social issues of concern, creating new technological infrastructures for open data, offering educational and awareness activities related to open data, and new data-driven products and services (Molina, 2022). Exclusive focus on economic value generation from open data can foreclose policy focus and allocation of resources for realising other types of value from open data, as well as development of additional infrastructures and capabilities for the realisation of such other types of value (Broomfield, 2023; Davies, 2019). To critical open data studies Following Science and Technology Studies recognising technical artifacts are embedding values and are influenced by social factors, a growing body of critical scholarship calls attention to other aspects of open data, that challenge its neutrality. While open government often reduces the production of open data as merely the digital formatting and release of already existing public sector information, researchers of critical data studies and Science and Technology Studies reveal the “data work” that goes into the production and release of open government data, and the ways in which internal decisions about what data to release and in what format influence what types of value are generated from such open data (Denis & Goëta, 2014; Goëta & Davies, 2016). And on the use side, there is also growing scholarship questioning the promise of universality of open data. There are various socio-technical barriers to the use of open data, ranging from data literacy skills to access, motivation and access to computational infrastructures (Zuiderwijk et al., 2012b). Further, the focus on economic value generation has resulted in a disproportionate Page 4 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 use of open data by market actors to create commercial solutions to societal problems, and to profit from openness while giving little to no value back to the maintenance or sustainability of open resources or nourishment of communities who contribute labour to such resources (Bates, 2012; Lund & Zukerfeld, 2020; Tkacz, 2012). Open data initiatives and infrastructures also face many challenges ranging from lack of financial support to limited participation of non-government data holders, which impact their sustainability. But some open data initiatives also serve as useful examples of peer production (as in the case of citizen science initiatives) as well as knowledge commons (as in the case of Wikipedia and Science Commons), where open data are a form of digital commons – shared informational resources that community-maintained and are crucial for the realisation of digital rights (on digital commons, see Dulong de Rosnay & Stalder, 2020, on data commons and digital democracy, see Senabre Hidalgo et al., 2024). Widespread machine reuse of openly licensed datasets and content for the purpose of AI training is also raising new problematics. Openly licensed creative works are often used to train proprietary generative AI models, by employing web crawling tactics. But many creative workers as well as researchers are opposed to such machine reuse, because of the lack of accountability as well as autonomy, privacy and economic risks of generative AI models. This is resulting in a move towards closure, either where more restrictive licenses are applied to such creative works or where website owners register strict opt-outs from commercial text and data mining which could result in a siloisation of the open web (Chandrasekhar, 2025; Hardinges et al., 2025). On the other hand, machine reuse of open data has also resulted in the articulation of new expectations of attribution, reciprocity and sustainability by data creating communities and data stewards, to ensure sustainability of these communities and the open resources (Id). To make sense of these new developments however, there is a need to move beyond a binary approach to open data that assumes equal distributional impact of open data, and think more carefully about who creates open data, how, and who benefits (Okorie & Marivate, 2024; Santoro et al., 2025). Findings on open data users The starting point of the ODECO project was to move away from a linear approach to open data, and articulate ecosystemic approaches. According to Zuiderwijk et al, “an open data ecosystem is characterized by multiple interdependent socio-technical levels, dimensions, actors (including data providers, infomediaries and users), elements and components” (Zuiderwijk et al., 2014). Jetzek further adds that such an open data ecosystem is “circular in nature, building upon a complicated network of value that is generated by different participants that are creating valuable information as well as products and services” (Jetzek, 2017). In the ODECO project background paper, van Loenen et al. go further, to argue that an open data ecosystem should be “a cyclical, sustainable, demanddriven environment oriented around agents that are mutually interdependent in the creation and delivery of value from open data” (Van Loenen et al., 2021). Between May 2023 and March 2025, the ODECO project published 11 project reports, each exploring different aspects of open data ecosystems, as set out in Table 1 below. The ODECO project focussed on 9 actors central to open data ecosystems: local government, regional/central government, non-specialist data users, journalists, students, non-profit organisations, companies, artificial users, and open data intermediaries. As an interdisciplinary research and training initiative, the ODECO project explored technical, legal, design, governance, social, economic and skill-based aspects of open data. ODECO Findings on users’ needs, contributions and motivations In the first project report, the ODECO consortium identified a list of user needs, as relating to the 9 actors that constitute the core stakeholders of open data ecosystems (Di Staso et al., 2023). This project report identified 9 buckets of user needs: availability, accessibility, and findability of open data; improved data quality; reliable data infrastructures; adequate funding; literacy; data ethics; licensing and privacy regulations; governance principles; and communication and coordination frameworks. These users’ needs were identified as being crucial to the transition from linear producer-driven models of open data, to circular user-driven models of open data, by recognising the centrality of various actors to open data initiatives as well as recognising the multiple roles discharged by these actors. In the next two project reports, the ODECO consortium identified certain technical and governance aspects of these open data user needs (Aziz et al., 2023; Cazacu et al., 2024). From a technical perspective, the ODECO consortium focussed on everyday user stories of different types of users navigating open data portals, and proposed strategies for addressing these user needs through implementation of the FAIR principles (Aziz et al., 2023). From a governance perspective, some members of the ODECO consortium proposed commonsbased principles to ensure open access as well as sustainability of open data initiatives (Cazacu et al., 2024). In particular, governance of open data ecosystems should entail focus on creating communities of practice as well as communities of shared purpose, as well as encouraging shared decision-making by the different actor groups. In the next 3 project reports, the ODECO consortium focussed on open government data, exploring different types of values generated from open government data and contributions by users back to open data ecosystems (Ktistakis et al., 2023), as well as technical and governance aspects of open government data (Magnussen et al., 2024; Polini et al., 2024). The ODECO consortium identified various types of contributions made by users of open government data – which ranges from creation of more open datasets, as well as other non-data contributions such as technological infrastructures for data storage and analysis, data flow automations, educational Page 5 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 Table 1. ODECO project reports, prepared by authors. ODECO Deliverable Title Date of Publication URL 2.1 Open data user needs: seven flavours 31-05-2023 https://odeco-research.eu/wp-content/uploads/2023/06/ ODECO-D2.1-Open-data-user-needs-seven-flavours_Final. pdf 2.2 User needs from a technical perspective 30-09-2023 https://odeco-research.eu/wp-content/uploads/2023/10/ ODECO-D2.2-User-needs-from-a-technical-perspective_ Final.pdf 2.3 User needs from a governance perspective 28-02-2024 https://odeco-research.eu/wp-content/uploads/2024/03/ ODECO-D2.3-User-needs-from-a-governance-perspective_ Final.pdf 3.1 Closing the cycle: Understanding potential contributions of open government data users to the open data ecosystem 29-11-2023 https://odeco-research.eu/wp-content/uploads/2023/12/ ODECO-D3.1-Closing-the-cycle-Understanding-potentialcontributions-of-open-government-data-users-to-theopen-data-ecosystem_Final.pdf 3.2 Closing the cycle: Promoting open data users’ contribution from a technical perspective 11-04-2024 https://odeco-research.eu/wp-content/uploads/2024/09/ ODECO-D3.2-Closing-the-cycle_Promoting-open-datausers-contribution-from-a-technical-perspective_final.pdf 3.3 Closing the cycle: Promoting open data users’ contributions from a governance perspective 03-05-2024 https://odeco-research.eu/wp-content/uploads/2024/09/ ODECO-D3.3-Closing-the-cycle_Promoting-open-datausers-contribution-from-a-governance-perspective_Final. pdf 4.1 Motivations of nongovernment actors to become active contributors to the Open Data ecosystem 28-06-2024 https://odeco-research.eu/wp-content/uploads/2024/09/ ODECO-D4.1-Motivations-of-non-government-actors-tobecome-active-contributors-to-the-open-data-ecosystem_ Final.pdf 4.2 An approach to steer the behaviour of nongovernment data holders towards open data through a technical strategy 22-10-2024 https://odeco-research.eu/wp-content/uploads/2024/12/ ODECO-D4.2-An-approach-to-steer-the-behaviour-of-nongovernment-data-holders-towards-open-data-through-atechnical-strategy_Final.pdf 4.3 An approach to steer the behaviour of nongovernment data holders towards open data through a governance strategy 30-09-2024 https://odeco-research.eu/wp-content/uploads/2024/12/ ODECO-D4.3-An-approach-to-steer-the-behaviour-of-nongovernment-data-holders-towards-open-data-through-agovernance-strategy_Final.pdf 5.1 Models of allocating roles, tasks and responsibilities in open data ecosystems 28-11-2024 https://odeco-research.eu/wp-content/uploads/2024/12/ ODECO-D5.1-Models-of-allocating-roles-tasks-andresponsibilities-in-open-data-ecosystems_Final.pdf 5.2 Strategies to balance and distribute value in open data ecosystems 13-02-2025 https://odeco-research.eu/wp-content/uploads/2025/02/ ODECO-D5.2-Strategies-to-balance-and-distribute-valuein-open-data-ecosystems_Final.pdf services, consultancy services, organisational services, communication products, and collaboration spaces (Ktistakis et al., 2023). Based on these diverse contributions, the ODECO consortium identified different types of value created by users of open government data – which includes knowledge enrichment, informed decision-making by citizens, collaboration between multiple actors for a shared purpose, transparency and accountability of public administrations, and improve internal efficiency of a user (Id). Having identified these different types of user contributions, the ODECO consortium also identified motivations for users to engage in such contributions, which must be accounted for in governance frameworks for sustainable open data ecosystems Page 6 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 (Magnussen et al., 2024). Most users contribute to open data ecosystems due to a mix of intrinsic and extrinsic motivations (Id). Extrinsic motivations often arise from legal obligations or profit-driven motivations. Further, local governments are motivated to contribute open datasets as well as other non-data contributions to improve their communities – by improving community-participation in decision-making as well as co-created innovation (Id). Thereafter, the ODECO consortium focussed on open data portals as one of the most important mediums by which public administrations make open government data available, and proposed design and technical strategies to account for user needs in the functioning of open data portals (Polini et al., 2024). The ODECO consortium proposed a design pipeline by which user feedback can be obtained and integrated into open datasets (Id). The ODECO consortium also proposed additional features that should be included in open data portals – such as hackathons and competitions to incentivise use of open data, categorisation of open datasets based on temporally-relevant thematic categories (such as open datasets relating to the climate crises, and open datasets relating to the covid-19 pandemic), and suggestions for data analysis tools and/or integration of these tools into the open data portals (Id). Artificial Intelligence (AI) can also be relied on, particularly for real-time data searching, data discovery, and metadata generation (Id). ODECO findings on contributions by non-government actors to open data ecosystems The ODECO consortium also produced 3 reports on nongovernment open data, focussing in particular on motivations and challenges for non-government actors to contribute their data as open data. The non-government actors include companies as well as other data holders such as journalists, non-profit organisations and non-technical individuals. The ODECO consortium identified several motivations, ranging from the desire to create and support communities of practice, the desire to support organisational networks through sharing and reuse of data, create private value, create social impact, improve the contributor’s internal skills or data processes, for personal enjoyment, and because of a sense of belonging in an open data community (Re et al., 2024). The barriers to open data contributions by non-government actors are lack of data skills and literacy, lack of governance mechanisms, lack of awareness about the value of open data, lack of technical tools, misaligned goals and interests, and lack of resources (Id). The ODECO consortium also identified technical strategies, to increase contributions of open data by non-government data holders. In particular, the ODECO consortium identified three challenges for which technical solutions may be appropriate – at the data creation stage to improve dataset and metadata quality and interoperability, at the data sharing stage to improve privacy and licensing challenges, and at the feedback stage to create a vibrant community involving both data holders and data users (Alexopoulos et al., 2024). Further, an ideathon (as described in Alexopoulos et al., 2024) was conducted at a training week organised by the ODECO consortium in September 2024, where participants create prototypes of technical solutions that could respond to these challenges, such as the use of AI for metadata generation, workflow management to improve adherence to interoperability standards, and questionnairebased tools for identification and selection of appropriate licenses. ODECO findings on collaboration and redistribution The ODECO consortium also studied models of collaboration between various open data actors, to collectively generate value from open data (Chandrasekhar et al., 2024). By analysing four-case studies of collaboration between 2 or more open data actors, the ODECO consortium identified key institutional factors relevant for such collaborations – which include partnerships between government and non-government actors for open data initiatives, coordinating roles discharged by public administrations to regularly involve non-government actors in open data initiatives, the creation and maintenance of strong interorganisational culture around open data, and investment in secure public data infrastructures for data storage, sharing and analysis (Id). Finally, the ODECO consortium also proposed some strategies to redistribute value in open data ecosystems, to ensure that both financial and social value can be generated from open data (Cazacu et al., 2025a). In terms of financial value, strategies proposed to mitigate the imbalanced distribution of financial value include tax incentives for open data contributions, the provision of shared infrastructures, and enabling value-added services by government agencies to offset costs. These initiatives point toward the need for redistributive mechanisms that counterbalance the financial dominance of well-resourced actors and foster a more equitable ecosystem. In terms of social value, institutionalising both the FAIR principles for data quality as well as other principles for addressing power inequalities are important, as well as the use of legal frameworks to enable more open sharing of data that is of public interest but are currently enclosed by private service providers, such as mobility data. Specifically, researchers from the indigenous data sovereignty movement have criticised the FAIR principles for focussing exclusively on techno-legal aspects of data quality, and ignoring the social, historical and material contexts within which open data is produced and used. This has resulted in the articulation of the CARE principles as a companion to the FAIR principles for open data and open science, where the focus is also on Collective Benefit, Authority to Control, Responsibility and Ethics (Carroll et al., 2020; Carroll et al., 2021). Extracting recommendations for enabling and improving open data ecosystems in the long term From the ODECO project reports described above, we propose a synthesis of recommendations for enabling and improving open data ecosystems. We conducted a narrative review (Greenhalgh et al., 2018) of the ODECO project reports described above, to identify all recommendations, suggestions and proposals contained in these reports. We then organised these into 9 thematic categories, as illustrated in Table 2 below, and further clustered them under 3 topics applicable to different Page 7 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 Table 2. ODECO Recommendations for Sustainable Open Data Ecosystems, prepared by authors. Data quality 1. Discoverability Multiple access modalities; machine-readable formats 2. Metadata Comprehensive metadata; use of AI for metadata generation 3. Engagement and participation A priori, ongoing and a posteriori strategies to involve all actors Wide data re-use 4. Beyond open government data Legal instruments, financial incentives and governance strategies for open data from non-government actors 5. Data literacy Leverage education sector for data skills and data literacy 6. Mind the gap(s) Power dynamics in terms of who and what is “missed out” in open data initiatives Infrastructures for open data 7. Usability User experience matters 8. Interoperability Uptake of interoperability standards is crucial 9. Public administrations’ investments Funding, infrastructures and coordinating function of public administrations stages of open data ecosystems lifecycle, data production and access, data reuse, and the long-term perspective with required infrastructural investment. Each recommendation is also supplemented with a logo. In doing so, we illustrate the diversity of policy and infrastructural actions required to create and sustain open data ecosystems, beyond purely technical or techno-optimistic solutions, and pay attention to the sociotechnical and political aspects of open data production and use as well as to all facets of the necessary investments and strategies. In the section below, we expand on each of these recommendations for sustainable open data ecosystems. These recommendations can be broadly categorised into three buckets – data quality, wide data re-use, and infrastructures for open data. These buckets represent the overall problem sought to be addressed. Each recommendation within these buckets outlines the different solutions (technical, governance, policymaking) proposed in the surveyed ODECO project reports. Data quality Despite global uptake of principles such as the FAIR principles, findability and interoperability of open datasets as well as data elements within these datasets remains weak. Accordingly, the ODECO project put forth three recommendations to improve data quality in open data initiatives – to boost discoverability, improve metadata, and focus on ongoing engagement and participation. 1. Discoverability Open data initiatives should create multiple access modalities, including open data portals, open APIs and direct downloads (Ali et al., 2022). Policymakers should also encourage the adoption of technical openness in data publication by advocating for machine-readable formats (Dulong de Rosnay, 2008). To enhance the accessibility of open data for non-technical users as well users in low-resource settings, governments and open data researchers should also support the development and distribution of low-code tools, as well as low-tech and Page 8 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 mid-tech data analysis systems (Kostakis et al., 2023; Philippe, 2020). Figure 1 serves as a logo for this recommendation on discoverability. Figure 1. Photo by Dyon Joël, representing a logo for discoverability. Dedicated to the public domain. Source: https:// commons.wikimedia.org/wiki/File:Toutes_directions.JPG. 2. Metadata Enhancing metadata quality boosts data discoverability (Chokki et al., 2022; Nogueras-Iso et al., 2021). Policymakers should continue to advocate for metadata standards, ensuring datasets include comprehensive descriptions, provenance, and structured classifications (Brewster et al., 2020; Maratsi et al., 2024b). To this extent, emerging technologies such as artificial intelligence can be leveraged to generate core metadata automatically, thereby reducing the burden on open data providers (Ahmed, 2023). Figure 2 serves as a logo for this recommendation on metadata. Figure 2. Image by cea, representing a logo for metadata. Licensed under CC BY 2.0. Source: https://commons.wikimedia.org/ wiki/File:Metadata_is_a_love_note_to_the_future_(8071729256)_ (cropped).jpg. 3. Ongoing engagement and participation Open data initiatives should be attuned to stakeholders’ needs through feedback loops, as illustrated in Figure 3. A priori, open data initiatives should undertake ecosystem mapping to identify different stakeholders and their needs (Nthubu et al., 2022). They may do so using tools from the discipline of design thinking (Sanders & Stappers, 2008) and theoretical principles from the discipline of information visualisation and communication (Bohman, 2015). This can engender both technical data openness as well as social equity in open data initiatives. On an ongoing basis, open data initiatives should create robust feedback loops (Alexopoulos et al., 2014; Bisztray et al., 2021; Herrera-Murillo et al., 2022; Nikiforova, 2020). This can include digital design strategies to create participative interfaces as in the case of the French open data portal, which contains a discussion section under each dataset where users can flag errors and propose edits to the datasets. Open data initiatives could also adopt participative design strategies, to create spaces for community discussion and deliberation on data re-use (Verhulst et al., 2024). Other examples include open data game jams i.e. the use of serious games to enable collectivisation around open data (Di Staso et al., 2024), data physicalisation i.e. the use of physical artefacts to represent data and visualise data and value flows (Cazacu et al., 2025b), data sprints (Venturini et al., 2018) and gamebased classroom learning pedagogies (Vargas et al., 2024b). A posteriori, open data initiatives should also undertake evaluations. This can include automated validation tools, periodic audits, quality dashboards, automated interoperability assessment frameworks, and collaborative stakeholder engagement to maintain high data quality standards and to assess whether open data initiatives are discharging their original stated objectives (Alexopoulos et al., 2024). Figure 3. Diagram by SilverStar, representing a logo for ongoing engagement and participation. Licensed under CC BYSA 3.0. Source: https://commons.wikimedia.org/wiki/File:Feedbackloop-general.svg. Wide data re-use The ODECO project also proposed recommendations to ensure wide data re-use. The GovLab identified four phases in the political journey of the open data movement (Chafetz et al., 2024; Verhulst et al., 2020). In the first phase, citizens could obtain conditional access to public sector information on request, pursuant to “freedom of information” regulations (Birkinshaw, 2006). The second phase sought to make government datasets ‘open by default’ by law. This focus on ‘open by default’ has had a significant impact on open government. For instance, the EU Data Portal (the open government data portal at the EU-level) now hosts more than 1.5 million open datasets. The third wave of open data was focussed on the issue of impactful re-use, focussing on incentives, barriers are infrastructures for data re-use. In this regard, the third wave illustrated the need to move beyond the creation of ‘data heaps’ in the public domain, and to think more critically about the conditions in which data is created, used and re-used. The (more speculative) fourth wave of data investigates how to make open data ‘AI-ready’, by focusing more on issues of data provenance and AI-related re-use. The open data movement is currently between the third and fourth wave. On the one hand, large amounts of data that is in the public interest, such as mobility data, are generated and Page 9 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 Nogueras-Iso J, Lacasta J, Ureña-Cámara MA, et al.: Quality of metadata in open data portals. IEEE Access. 2021; 9: 60364–60382. Publisher Full Text Nthubu B, Perez D, Richards D, et al.: Navigating complexity through co-design: visualising, understanding and activating entrepreneurial ecosystems. The Design Journal. 2022; 25(5): 730–751. 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Electronic Journal of E-Government. 2012b; 10(2): 156–172. Reference Source Zuiderwijk A, Janssen M, Davis C: Innovation with open data: essential elements of open data ecosystems. Information Polity. 2014; 19(1–2): 17–33. Publisher Full Text Page 16 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 Open Peer Review Current Peer Review Status: Version 1 Reviewer Report16 October 2025 https://doi.org/10.21956/openreseurope.22945.r61293 © 2025 Lund A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Arwid Lund Södertörn University, Stockholm, Sweden This open letter is a report that summarizes key findings of the research project ODECO (Open Data ECOsystems), a consortium within Marie Curie Action Innovative Training Networks. The project ran between 2021 – 2025 and developed a broad perspective on OD valuation including economic value generation andsocial valuation within a theoretical ecosystemic framework, focused on sustainability. The report is clearly structured with a state-of-art section on current research, a section on the project and its methodology, and a final part with recommendations for building sustainable OD ecosystems that empowers all stakeholders. The rationale for the report is clearly stated and the groundwork for the suggested recommendations are well-documented, factually correct and written in an accessible language. The report's recommendations are described in detail and clustered in three themes: data production and access, data reuse and the long-term perspective with a focus on infrastructural issues. The report highlights and develops a very much needed critical perspective on the character of theopenness of OD. Who provides the OD, for whom and for what purpose? Raising for example the issue of how to incentivize non-government actors to release open datasets. On the critical side, I would like to problematise the theoretical ecosystemic perspective. It works well to give a picture of the interconnectedness of all stakeholders. Stakeholders that are listed as “local government, regional/central government, non-specialist data users, journalists, students, non-profit organisations, companies, artificial users, and open data intermediaries”. The weakness I perceive is connected to how the ecosystem metaphor affects our understanding of the interconnectedness. The idea of an ecosystem assumes some kind of equilibrium and sustainability already from the start, and like the network metaphor it somewhat obscures systematic imbalances and power relations, for example from a critical political economic perspective (note for example the position of companies in the list above). The critique that results from the report is limited to proposing complementary forms of social valuation in relation to the structural and systematic economic exploitation and enclosure of OD that is driven by private nongovernmental actors. On the positive side, the alternative visions of OD ecosystems are richly elaborated, especially by highlighting the importance of government support to commons-based projects. The report's Open Research Europe  Page 17 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 recommendations invite all stakeholders to take constructive part in discussions. Is the rationale for the Open Letter provided in sufficient detail? (Please consider whether existing challenges in the field are outlined clearly and whether the purpose of the letter is explained) Yes Does the article adequately reference differing views and opinions? Yes Are all factual statements correct, and are statements and arguments made adequately supported by citations? Yes Is the Open Letter written in accessible language? (Please consider whether all subjectspecific terms, concepts and abbreviations are explained) Yes Where applicable, are recommendations and next steps explained clearly for others to follow? (Please consider whether others in the research community would be able to implement guidelines or recommendations and/or constructively engage in the debate) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Open Science, Open Data, Open Governmental Data, Open Access, Open Source, Commons-Based Peer-Production, Digital Platforms I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer Report14 October 2025 https://doi.org/10.21956/openreseurope.22945.r61289 © 2025 Ali M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Mohsan Ali University of the Aegean, Samos, Greece The report discusses the open data ecosystem perspectives and, based on the research group activities over the years, extracted recommendations for enabling and improving OD ecosystems in the long term. This letter is equally important for the research purpose and for the policy makers and other user groups in the open data multidisciplinary domain. Open Research Europe  Page 18 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025 In its current form, it is well organized and presents detailed information regarding open data ecosystem specificities. Thanks Is the rationale for the Open Letter provided in sufficient detail? (Please consider whether existing challenges in the field are outlined clearly and whether the purpose of the letter is explained) Yes Does the article adequately reference differing views and opinions? Yes Are all factual statements correct, and are statements and arguments made adequately supported by citations? Yes Is the Open Letter written in accessible language? (Please consider whether all subjectspecific terms, concepts and abbreviations are explained) Yes Where applicable, are recommendations and next steps explained clearly for others to follow? (Please consider whether others in the research community would be able to implement guidelines or recommendations and/or constructively engage in the debate) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Open Data Technical Interoperability I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Open Research Europe  Page 19 of 19 Open Research Europe 2025, 5:295 Last updated: 16 OCT 2025