D4.1 Motivations of non-government actors and data holders to become active contributors to the open data ecosystem

Towards a Sustainable Open Data ECOsystem D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 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. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 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 D4.1 Deliverable Title Motivations of non-government actors to become active contributors to the Open Data ecosystem Dissemination Level Public Deliverable Leader Università degli Studi di Camerino (UNICAM) Submission Date 28-06-2024 Author(s) Barbara Re – UNICAM, Héctor Ochoa Ortiz – UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft, Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft Document history Version # Date Description (Section, page number) Author & Organisation V0.1 13-02-2024 First draft table of content Barbara Re, Héctor Ochoa Ortiz - UNICAM V0.1a 22-02-2024 Review of the table of content Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft, Héctor Ochoa Ortiz - UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft V0.2 01-03-2024 First draft of introduction and motivation for each stakeholder Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft, Héctor Ochoa Ortiz - UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft V0.2a 09-03-2024 Revision of introduction and motivation for each stakeholder Barbara Re – UNICAM V0.3 11-03-2024 Finish the motivations for each stakeholder, first draft of barriers for each stakeholder Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft, Héctor Ochoa Ortiz - UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem Version # Date Description (Section, page number) Author & Organisation V0.3a 20-03-2024 Revision of V0.3 Barbara Re – UNICAM V0.4 21-03-2024 Finish barriers for each stakeholder Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft, Héctor Ochoa Ortiz - UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft V0.4a 27-03-2024 Revision of V0.4 Barbara Re – UNICAM V0.5 28-03-2024 Conclusions for each stakeholder Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft, Héctor Ochoa Ortiz - UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft V0.6 03-05-2024 Shared introduction and conclusion, revision of V0.5. End of Draft 1 Héctor Ochoa Ortiz, Barbara Re - UNICAM V0.6a 08-05-2024 Revision of V0.6 Joep Crompvoets - KUL V0.6b 19-05-2024 Revision of V0.6 Anneke Zuiderwijk-van Eijk – TU Delft V0.7 24-05-2024 Fixes on the introduction shared parts, and conclusion Héctor Ochoa Ortiz - UNICAM, Ahmad Ashraf Ahmad Shaharudin – TU Delft, Davide Di Staso - TU Delft, Giorgos Papageorgiou – Farosnet S.A., Alejandra Celis Vargas - AAU, Liubov Pilshchikova - TU Delft V0.7a 27-05-2024 Revision of V0.7 Joep Crompvoets - KUL V0.7b 28-05-2024 Revision of V0.7 Anneke Zuiderwijk-van Eijk – TU Delft V0.8 06-06-2024 Fix of the final version Héctor Ochoa Ortiz, Barbara Re - UNICAM V0.9 25-06-2024 Final revision Barbara Re - UNICAM V0.10 25-06-2024 Approval Bastiaan van Loenen, TUD V1.0 28-06-2024 Final editing Danitsja van Heusden, TUD D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem Table of Contents Abbreviations ..................................................................................................................................................................... 6 1 Introduction ............................................................................................................................................................... 7 1.1 Problem definition ............................................................................................................................................. 7 1.2 Role of this deliverable in the ODECO project ........................................................................................ 7 1.3 Structure ................................................................................................................................................................. 8 2 Non-specialist citizens ........................................................................................................................................... 9 2.1 Introduction .......................................................................................................................................................... 9 2.2 Methodology..................................................................................................................................................... 10 2.3 Results .................................................................................................................................................................. 11 2.3.1 Motivation .................................................................................................................................................... 11 2.3.2 Barriers ........................................................................................................................................................... 12 2.4 Conclusion .......................................................................................................................................................... 13 3 Data journalists...................................................................................................................................................... 15 3.1 Introduction ....................................................................................................................................................... 15 3.2 Method ................................................................................................................................................................ 15 3.3 Results .................................................................................................................................................................. 16 3.3.1 Motivation .................................................................................................................................................... 16 3.3.2 Barriers ........................................................................................................................................................... 16 3.4 Conclusion .......................................................................................................................................................... 17 4 Elementary school students ............................................................................................................................. 18 4.1 Introduction ....................................................................................................................................................... 18 4.2 Method ................................................................................................................................................................ 18 4.3 Results .................................................................................................................................................................. 20 4.3.1 Motivation .................................................................................................................................................... 20 4.3.2 Barriers ........................................................................................................................................................... 21 4.4 Conclusion .......................................................................................................................................................... 22 5 Non-governmental organisations ................................................................................................................. 23 5.1 Introduction ....................................................................................................................................................... 23 5.2 Method ................................................................................................................................................................ 23 5.3 Results .................................................................................................................................................................. 24 5.3.1 Motivations .................................................................................................................................................. 24 5.3.2 Barriers ........................................................................................................................................................... 25 5.4 Conclusion .......................................................................................................................................................... 26 6 Commercial organisations ................................................................................................................................ 27 6.1 Introduction ....................................................................................................................................................... 27 6.2 Method ................................................................................................................................................................ 27 6.3 Results .................................................................................................................................................................. 28 D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 6.3.1 Motivation .................................................................................................................................................... 28 6.3.2 Barriers ........................................................................................................................................................... 29 6.4 Conclusion .......................................................................................................................................................... 29 7 Open Data intermediaries ................................................................................................................................. 30 7.1 Introduction ....................................................................................................................................................... 30 7.2 Method ................................................................................................................................................................ 30 7.3 Results .................................................................................................................................................................. 31 7.3.1 Motivation .................................................................................................................................................... 31 7.3.2 Barriers ........................................................................................................................................................... 32 7.4 Conclusion .......................................................................................................................................................... 33 8 Discussion ................................................................................................................................................................ 34 8.1 Common motivations and barriers ........................................................................................................... 34 8.1.1 Common motivations .............................................................................................................................. 34 8.1.2 Common barriers ....................................................................................................................................... 35 8.2 Enablers ............................................................................................................................................................... 38 9 Conclusion ............................................................................................................................................................... 39 9.1 Summary of the results and further actions ......................................................................................... 39 9.2 Limitations .......................................................................................................................................................... 39 10 References .................................................................................................................................................................. 41 List of Figures Figure 1: Data collection timeline ............................................................................................................................10 Figure 2: Participantʼs knowledge at the beginning of the open data game jam. The numbers inside the stacked bars represent the number of participants who picked that answer. ................................11 Figure 3: Motivations for attending the open data game jam recorded in the survey ......................12 Figure 4: Methodological flow ..................................................................................................................................20 Figure 5: Artistʼs impression of the chosen methodology. Drawing by Iulian Thomas .......................28 List of Tables Table 1: Overview of the data collection methods ............................................................................................10 Table 2 : Motivations for non-expert citizens to attend the game jam ....................................................12 Table 3: Barriers to non-expert citizensʼ contribution to open data ecosystems ..................................13 Table 4: Methods and participants ..........................................................................................................................19 Table 5: Sources of the qualitative data .................................................................................................................24 Table 6: Motivations for NPOs to contribute open data .................................................................................25 Table 7: Mentioned barriers........................................................................................................................................26 Table 8: Sources of the case study data .................................................................................................................28 Table 9: Motivations for commercial organisations to contribute open data ........................................28 Table 10: Barriers of commercial organizations to contribute Open Data ...............................................29 Table 11: Information on the interviews conducted .........................................................................................30 Table 12: Motivations for open data intermediaries to contribute open data .......................................32 Table 13: Barriers for open data intermediaries to contribute open data ...............................................33 Table 14: Common motivations to contributing open data ..........................................................................35 Table 15: Common barriers to contributing open data ...................................................................................36 Table 16: Enablers and related motivations and barriers ................................................................................38 D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 6 Abbreviations D Deliverable ESR Early Stage Researcher MS Milestone NGD Non-Government Data OD Open Data ODECO Open Data ECOsystem 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 OCI United Kingdom D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 7 1 Introduction 1.1 Problem definition Task 4.1 explores the motivations of non-government actors to become active contributors to the Open Data ecosystem by releasing their data into the open data ecosystem. Governmental actors have historically been the dominant providers of open data. In many cases, such responsibility is engraved in policy documents, from non-binding strategic plans to binding laws and regulations such as the EU Open Data Directive. On the other hand, while there have been some initiatives by non-governmental actors to publish their data as open data, it is still limited and largely remains to be desired (van Loenen et al., 2018). Non-governmental actors are outside the public/governmental sector, such as companies, civil society organisations, and the media. Exploring the motivations for non-governmental actors to contribute to open data is thus essential to developing sustainable Open Data Ecosystems that incorporate both government and non-government open data. Aligned with the ODECOʼs Description of Action, this deliverable aims to answer the following three research questions: • RQ1: What are the motivations of non-governmental data holders to contribute to the open data ecosystem? • RQ2: What barriers do non-governmental data holders face when contributing to open data ecosystem? • RQ3: How can the motivations and barriers to sharing non-governmental data become enablers? Motivations are defined as “the need or reason for doing something” or the “willingness to do something” (Cambridge Dictionary, 2024). On the other hand, barriers are “anything used or acting to block someone from going somewhere or from doing something, or to block something from happening” (Cambridge Dictionary, 2024). In this deliverable, barriers refer to things that hinder actors from contributing to open data ecosystem, negatively affecting the open data community creation. They thus should also be studied in conjunction with motivations to share their data as open data. Their negative influence varies depending on the stakeholders, but they have some typical characteristics that must be considered in the study. Enablers are “something or someone that makes it possible for a particular thing to happen or be done” (Cambridge Dictionary, 2024). In this deliverable, enablers refer to situations in the ecosystem that facilitate or enhance the contribution of open data by non-governmental actors by leveraging the motivations and barriers for them to do so. Motivations and barriers of the following non-government stakeholders were identified: non-specialist users, data journalists, students, NGOs, commercial organisations, and open data intermediaries. For each stakeholder group, we outline the methods employed and the types of data sources used, including primary (interviews, questionnaires, focus groups, etc.) or secondary (literature review, use case analysis, etc.). Where relevant, we broadened our analysis of stakeholdersʼ motivations and barriers to contribute to open data ecosystems. For example, in the section on non-specialist citizens, we also by considered knowledge (rather than data) contributions. 1.2 Role of this deliverable in the ODECO project The ODECO deliverable 4.1 is part of Working Package 4, “From an Exclusive to an Inclusive Open Data Ecosystem”. In D4.1, we seek to understand the motivations of non-government actors to become active contributors to the open data ecosystem sharing their own data as open data. The relation to the other deliverables in WP4 is as follows: D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 8 • D4.2 explores technical strategies to steer the behavior of non-government data holders towards open data. It will report on technological ways to promote the inclusion of nongovernment data holders in the open data ecosystem. • D4.3 explores a governance strategy to steer the behavior of non-government data holders towards open data. It will report on steering mechanisms and approaches for activating nongovernment data holders in the open data ecosystem. This deliverable also marks the completion of the milestone MS6 “Joint research deliverables” after successfully delivering the first research deliverables (D2.1, D3.1, and D4.1). D4.1 complements Deliverable 3.3, "Closing the cycle: Promoting open data usersʼ contribution from a governance perspective". D3.3 explores ways to sustainably establish the contribution of open government data users to open data ecosystems by identifying motivations to do it. D4.1 differs from D3.3 as D4.1 is centred on having an inclusive ecosystem where non-government actors share their data, while D3.3 is centred on closing the cycle and users of open government data contributing back to the ecosystem. 1.3 Structure This report is structured as follows: Chapters 2 to 7 present the motivations and barriers of each stakeholder type to contribute to the Open Data Ecosystem. Each chapter briefly defines the given stakeholder, followed by the methodology, results, and conclusion. Chapter 8 serves as the discussion and conclusion for the whole document. Shared motivations and barriers between the stakeholders are discussed, and questions are proposed for future deliverables in WP4 (D4.2 and D4.3). D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 9 2 Non-specialist citizens 2.1 Introduction In the context of open data ecosystems, non-specialist citizens are interested in accessing open data or can benefit from open data while lacking the specialised skills needed to analyse datasets. To engage non-specialist citizens in sharing their data, we developed a new approach, an “Open data game jam”, which is an event similar to an open data hackathon, with the critical difference that participants make a video game rather than an application. In this section, we explain why we chose to develop this novel approach, starting with an explanation of the opportunities and challenges of open data hackathons. In recent years, open data hackathons have emerged as a promising approach to engage citizens (both specialist and non-specialist) in the reuse of open data. Open data hackathons typically last 1-3 days, during which participants use open data to develop new solutions. Like other hackathons, they are “accelerated design processes” (Falk, 2022) demanding rapid results. While solutions are rarely developed beyond the event, prototyping fosters a deeper understanding of the problem and potential solutions. Organisers range from government bodies to local activists and NGOs, each with various motivations, such as: (1) promoting data reuse, (2) building a community around open data (Jaskiewicz et al., 2019), (3) supporting Nonprofit Organisations (NPOs) (Hou & Wang, 2017), (4) addressing specific societal challenges (Lodato & DiSalvo, 2016), (5) creating new business models around open data (Kitsios & Kamariotou, 2018). Open data hackathons often attract data experts with strong analysis skills and non-experts with contextual knowledge and lived experiences. Non-specialist citizens can contribute to making sense of open datasets through their “thick data” (Wang, 2016), which is the qualitative context needed to interpret quantitative data, such as ethnographic observations and lived experiences. We will refer to this type of data as “knowledge” that non-specialist citizens possess, and that they can contribute to open data ecosystems. We focus on the context of open data hackathons and similar “accelerated design processes” (Falk, 2022) with open data because of their capacity to increase communitiesʼ ability to work with open data (Jaskiewicz et al., 2019). However, (open data) hackathons have been criticised for taking a “solutionist” (Morozov, 2013) approach to social issues, meaning that they focus on technology rather than social issues. At an (open data) hackathon, issues tend to be oversimplified, and solutions exclusively rely on technology rather than social change. This critique does not apply to all hackathons. Additionally, the problem of solutionism is not necessarily about the event itself but how it is framed and studied. Nonetheless, there is a need to move the focus of hackathons from technology to social issues. To refocus open data hackathons on social issues, we changed the “invitation” (Lindström and Ståhl, 2014) to participants from using technology to create solutions to using technology to articulate (describe) the issues (Lodato and DiSalvo, 2016). Our understanding of the “invitation” to participants is similar to Lindström and Ståhlʼs definition (2014, p. 329): the invitation included an “area of curiosity” (social issues) and “a proposition of how to engage with it” (making a video game). Our new approach is an open data game jam, an event where participants collectively produce a video game about a social issue of their choosing. Most of our participants had never made a video game before but still possessed sufficient digital skills to craft a prototype in a few hours. We introduced participants to a beginner-friendly game engine which relies on visual coding and offered technical help throughout the event. Participants could pick an issue they directly experienced and contribute their thick data (lived experience). Additionally, we invited participants to brainstorm and include available open data about the issue in their games. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 16 reflection to fine-tune our approach and methodologies. By keeping a journal and conducting regular reflection sessions, strategies were continuously assessed and adapted to better support and engage journalists in effectively utilising open data within the newsroom. 3.3 Results 3.3.1 Motivation The primary function that the journalist serves in the open data ecosystem is that of the communicator. To utilise open data in their work, they must analyse, visualise, and, in several cases, reach out to experts, as specialised understanding is required to extract meaningful insights from the data in many instances. Finally, they also have to construct compelling storytelling around their findings that will be pleasing and engaging for their audience and allow people with no expert knowledge to understand the root of the problem without delving into it themselves. Through this process, two main motivations for journalists to contribute to the open data ecosystem become apparent: the desire to enhance public understanding and the drive to foster transparency and accountability. Transparency and accountability The main reason that journalists have to get involved and contribute to the open data ecosystem is to promote a culture of transparency and accountability in society. This finding became prevalent in the interviews and the action research as their primary motivation. In all cases, the focus of using open data in journalistic activities was to display social issues and expose the deep roots of the problems using infographics and data. Communicating complex data to a wider audience cultivates a more informed and engaged citizenry. Their contribution to the open data ecosystem empowers citizens to advocate for transparency and better governance. Enhanced credibility Another reason journalists want to use open data in their work is to boost their credibility. By supporting their opinions with verifiable data, journalists can transform their articles from mere opinion pieces into well-substantiated analyses, thereby enhancing their trustworthiness and authority in the eyes of the public. This requires journalists to include references to their data sets and highlight their methodology of analysis so that their work is reproducible by the audience. 3.3.2 Barriers During the interviews and the action research, several barriers were observed that prevented journalists from becoming active contributors to the open data ecosystem. Open data are not the only source of information During the interviews, it was clarified that journalists use more than just open data. Although they explicitly mentioned they use data from official sources, these are not only published on open data portals but often include data acquired by requesting them from other European or governmental agencies. Lack of skills to use and analyze open data It became clear from all the interviews and the action research that journalists must possess the skills to find, analyse, and use open data. In all cases, experts from other fields (data analysts, visual artists) were required. Although this is an easily bypassed barrier, it creates other problems; it increases the complexity of using open data as more people have to collaborate and coordinate. It also increases the cost of the published articles as management has to add more people to the payroll. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 17 Lack of interest The lack of interest in open data became evident during the action research. For seven months, we were actively working in the newsroom with the chief editor and producing articles; the other journalists were interested in getting involved with what we were doing. This lack of interest must be explored further. Limited time This was mentioned in interviews but was also encountered in the action research. Journalists' main activity is to present the news. Still, as analysing data and compiling comprehensive infographics is a time-consuming process, it is a frequent phenomenon that when an article with results extracted from data analysis is prepared, other news is more relevant to the public. Therefore, the impact of the article is reduced. Not willing to share their data This was encountered during the action research and the interviews: the journalists were not keen to share the datasets they had compiled as they considered them an asset for the media organisation. When confronted about their willingness to share their datasets openly, they replied that monetary compensation would be required. The main reasoning behind this stance is their concern that their competitors could utilise the datasets they have compiled through extensive research and effort, and sharing them without compensation would eliminate their organisation's strategic advantage. 3.4 Conclusion The introduction of open data as a primary source for data journalists has the potential to fuel them with abundant useful information but also provides the incentives to make them active contributors in the open data ecosystem. The main motivation of journalists to engage with the open data ecosystem is aligned with the core values of the open data movement on transparency and accountability, highlighting a promising entanglement. However, the barriers detected have more to do with the managerial aspects of media organisations and the need for journalists to acquire more skills and resources. Although journalists intend to use and, by extension, contribute to the open data ecosystem, they must embrace a cultural shift toward a more collaborative and open practice paradigm. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 18 4 Elementary school students 4.1 Introduction Students can be defined as individuals actively engaged in a learning process in formal or hybrid (formal/informal) educational environments, ranging from basic to higher education. In the open data context, they have been seen as part of the large percentage of citizens without technical backgrounds, often referred to as non-specialists, non-data experts or lay audiences (Boyles, 2020; Concilio & Mulder, 2018). Young students in basic school education have been revealed as significant actors in open data and data literacy initiatives (Celis Vargas et al., 2023). Building a larger open data literate community is essential for fostering citizens able to participate and benefit from open data. Although the open data field has recognised students as a strategic user group to promote the skills and competencies necessary for increasing citizen participation and ensuring the long-term sustainability of open data ecosystems, they have been participating as users of open data rather than active contributors in open data ecosystems. The current study explores this user group as non-government actors, acknowledging that while they may engage in private or public educational institutions, they interact autonomously within educational systems. Current open data initiatives in education seek to equip students with the essential skills needed for the current fast-changing and data-driven society (Cook et al., 2018) , often called 21st-century skills (Romero et al., 2015). The potential of using open data has mainly been related to the connection of classroom activities to real facts and, secondly, to increasing teachers' and students' motivation (Coughlan, 2020). Open data learning activities have ranged from using OD in regular school subjects such as chemistry and geography (Pence et al., 2015), engaging with local problems and data in undergraduate courses about open data (Palova & Vejacka, 2022), and extracurricular activities such as public hackathons (Davis & Shneyer, 2020). According to a previous systematic mapping review (Celis Vargas et al., 2023), in current initiatives in elementary school, learning goals are often related to increasing awareness about open data and developing criticality. For example, Badioze Zaman et al. (2021) focus on increasing open data readiness by using in classroom pet robots and IoT, and Saddiqa et al. (2019) have related data literacy in schools with the ability to identify which types of data are needed for solving a problem and the ability to use visualisation technologies for exploring and presenting the data in greater detail and understandable way. Research Pellegrino & Antelmi (2023) has shown that open data initiatives at the school level primarily focus on using open datasets or data exploitation rather than on their production. Although elementary school students create their data using open government data in a few learning activities, their data is not currently open or shared outside the classroom. The current study aims to uncover the studentsʼ motivations and barriers to potentially sharing their data in open data ecosystems. 4.2 Method This section examines the method that supports the understanding of the motivations and barriers students face when contributing to sharing their data in the open data ecosystem. Considering the novelty of the topic, a systematic literature review and exploratory empirical studies were conducted. Firstly, the systematic literature review helped to understand the barriers to sharing data, considering the current use and awareness of open data in schools. For example, from the literature, it was possible to identify that the concept of open data is still highly abstract for students and teachers; therefore, asking directly about their motivation was not considered. Grant & Booth (2009) Three main steps were conducted: defining the scope, identifying the D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 19 articles through iterative searches and categorising them according to students' motivations and barriers to sharing their data in open data ecosystems. Secondly, two exploratory empirical studies were conducted in formal and informal learning environments to better understand elementary school students' latent or implicit motivations. One study in a formal educational environment included 39 students aged 15 to 16 and 5 teachers in a Danish school. In an informal learning environment, the study included 40 students aged 14 to 18 engaged in an active citizenship initiative organised by a Danish non-profit organisation. Sanders & Stappers (2012) Different qualitative methods were used to explore implicit or explicit motivations, such as individual and focus group interviews, observations, workshops and an open questionnaire. Table 4 summarises the applied methods and participants involved in the two studies. Table 4: Methods and participants Formal educational environment: Conducted in a Danish school during a week Method Participants Description Workshop and survey 39 school students aged 1516 years old in 9th grade. The workshop was developed as an OD learning activity including two parts. The first part proposed an individual data exploration and second part focused on group work to create a Data story with visualisations. At the end of the workshop, students answered a brief survey. Duration: 2h Focus group interview 15 school students (3 groups of 5 students) aged 1516 years old in 9th grade Informal interviews were conducted with a group of students after the workshop. Duration: 20 min Semi-structured interviews 5 elementary school teachers Semi-structured interview. Duration: 60 minutes Informal educational environment with focus of active citizenship Method Participants Description Nonparticipant observation 50 children aged 14-18 years old from different nationalities Non-participant observation during the co-creation workshops conducted by the partner organisation CoC Playful Minds during the Children's General Assembly CGA 2022. Duration: one week Sessions were recorded and transcribed for analysis, and observations were recorded in a diary. The data collected was analysed together following a thematic network analysis approach (Attride-Stirling, 2001). Firstly, potential students' motivations were coded, keeping the participants worded as much as possible. Secondly, categories were made to show the different motivations of students and barriers to sharing their data in open data ecosystems. Finally, global themes were identified to create a map of student's motivations. Figure 4 visualises the methodological flow, including the different samples and methods. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 20 Figure 4: Methodological flow 4.3 Results Considering the novelty of open data integration in elementary school, student data contribution has been identified as a potential rather than a current activity. The literature or the empirical study did not explicitly mention motivations and barriers for opening data created by elementary school students. An inductive back-and-forth analytic process helped uncover them in connection to the students' context and learning goals. 4.3.1 Motivation Five main motivations create an overview of the potential incentives behind potentially opening non-government data produced by students. Students' motivations were found to be associated with (i) being active citizens, (ii) raising awareness of local issues around students' context and daily life, (iii) helping the community around the school, considering students as important actors in local ecosystems, (iv) seeing what students learn in schools as useful in the real world, and (v) making school activities more relevant, interesting, and fun. (i) Being active citizens. Celis Vargas et al. (2023) have identified that open data learning activities, especially, seek the development of competencies for active citizenship address activities for the collection of their own data. In those cases, students have been involved in creating simple spreadsheets and collecting more complex data using tools such as sensors, games, or mobile applications (Badioze Zaman et al., 2021; Chicaiza et al., 2017; Saddiqa, Larsen, et al., 2019; Saddiqa et al., 2021b; Vallejo-Figueroa et al., 2018). The motivation is actively participating as citizens to create a better world. For example, during the focus interviews, students wondered about their school projects: "How is this going to create a better world?". (ii) Raising awareness of local issues around students' context and daily life. Considering students as experts in their local experience, they can create and share local datasets addressing aspects of their environment and daily life experiences. From their perspective, they want to raise Danish non-profit organiza�on: Informal learning environment Sample: 50 students aged 14 to 16. Non-par�cipant observa�ons along a week Danish school: formal learning environment Sample: 39 students in 9th grade aged 15 to 16 and 5 teachers. Interviews with teachers Genera�ve session: Workshop, survey and focus group interview. Literature review: Mapping Open Data ini�a�ves in educa�on (Celis Vargas et al. 2023) Empirical study: Defining learning designs for Open Data competencies. (Celis Vargas et al. 2024 in review) Thema�c Network analysis to map the student's mo�va�ons. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 21 awareness and provide a contextual understanding of local issues. Their motivation behind this is raising their voice, being heard, and making "Children's voice as important as others". (iii) Helping the community around the school, considering students as important actors in local ecosystems. Students are motivated by helping the community around them, implicitly to feel belonging and build their identity and place. Creating and sharing data has been identified as an opportunity to build networks in their local communities by addressing problems from other actors and contributing to solving them with data. "The school could be part of the local community by creating better data", and "I think local problems could be more fun because students can do something". (iv) Seeing what students learn in schools as useful in the real world. It was relevant for young pupils in elementary school to see what they do in school being used in the real world. It increases the authenticity of their learning experience. "If I'm sharing it, and it could be used afterwards, students will be more proud and more engaged to make it right because they know that it's likely to be used for something meaningful afterwards”. (v) Making school activities more relevant, interesting, and fun. Creating and sharing their data might increase their motivation for learning by fulfilling their intrinsic motivation for making something relevant, being heard, and connecting to their communities. Overall, students are motivated by active learning experiences where they can experiment and learn by themselves. 4.3.2 Barriers Five barriers to opening the data produced by students in learning activities are identified, as well as the main elements in learning designs, such as the characteristics of the learners and other actors involved, like teachers, and the learning environment, including tools. These challenges were identified through a literature review and empirical study. The main barriers found are (i) the lack of technical skills from teachers and significant training, (ii) updating classroom technology, (iii) the concept of open data being highly abstract, (iii) low awareness about what open data is, and (iv) the risk of disclosing personal data from young pupils. (i) Teachers need more technical skills and significant training. Teachers have an essential role in educational design. Considering different pedagogical approaches, teachers lead or facilitate learning activities and propose the main tools and resources. Several studies have pointed out the need for more technical skills for managing data, and digital skills are a primary barrier to achieving the potential of open data as an educational resource. (ii) Updating classroom technology. Depending on the specific context, tools, platforms, and methods for adapting classrooms to fast-changing technology could change simultaneously. Nevertheless, investment, skills, and administration are factors to consider. The most traditional educational systems are characterised by slow adaptation and low technology insertion. (iii) The concept of open data is highly abstract. Several authors have stressed the challenge that understanding and using open data presents for students due to its high level of abstraction (Atenas et al., 2015; Coughlan, 2020; Saddiqa et al., 2021a). For example, Saddiqa et al. (2021a) I Wolff et al. (2016) have suggested contextualising the data for better understanding, using open data from students' municipalities. Furthermore, the need for customised hands-on open data collection, interpretation and exploitation tools and methods has been made explicit to overcome this barrier. However, developing tools and methods simultaneously entangles new challenges for the usually steady educational systems. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 22 (iv) Low awareness about what open data is. During the empirical study, students and teachers referred to open data as any information found on the Internet. For example, teachers claimed to use open data for their teaching. Still, when asked more in-depth about their sources and process for managing the OD, it was explicit that they understood open data as any available information on the Internet. On the other hand, it is a completely new term for the pupils. (v) Risk of disclosing personal data from pupils. Ethical data management is essential in the user context of elementary school students since children are usually a vulnerable group. Due to low awareness of data management, schools, teachers, and parents are at risk of violating GDPR. 4.4 Conclusion Motivations change according to the participants' awareness. Teachers' primary motivation was learning about new tools and making studentsʼ learning activities more authentic using real facts. Open data learning designs consider that opening or sharing their data might increase authenticity and motivation in elementary school students. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 23 5 Non-governmental organisations 5.1 Introduction Non-Governmental Organisations (NGOs), also interchangeably called Non-Profit Organisations (NPOs) in this section, take up an intermediary role in the open data ecosystem, where they bridge the gap between open data providers and users (Gonzalez-Zapata and Heeks, 2015). NPOs are unique as intermediaries because there are specific user communities they are focusing on to address a social issue (Enaholo, 2017) while also not seeking to gain any profits from it (Salamon and Anheier, 1992). Historically, NPOs pushed for data openness, developed the open data research field, and resolved the practicalities of open data use (Enaholo, 2017). There are many ways in which NGOs contribute data back to the open data ecosystem. For example, they create tools and applications to aggregate or enhance the data, making it more accessible and understandable for the users. Moreover, they can produce or collect additional open data to enhance their use and re-share it with the users. NPOs can also request the data they or their users need from the data providers and republish it as open. The motivation to contribute back in various ways that NPOs have may come from the focus and aims they have. The aim is to provide information and services that the community needs, which the government does not provide, and motivate NPOs to aggregate existing data and collect and publish the available data as open (Ricker et al., 2020). If NPOs aim to improve overall openness and transparency, it can push them to aim for various projects and have open data and open source on that principle (Baack, 2015). However, NGOs may face barriers that prevent them from contributing (Chattapadhyay, 2014). Thus, in the rest of this section, we discuss the motivations, i.e. enablers of the NGOs to contribute data back to the open data ecosystem, as well as possible barriers to sharing the data. 5.2 Method This section examines the method that supports understanding the motivations and barriers of NPOs/NGOs to contribute to sharing their data in the open data ecosystem. The case study approach was used to collect the data and investigate the motivations of the NPOs/NGOs to become active data contributors to the open data ecosystem. The selection criteria for the case studies were: 1. Non-profit organizations should have different missions/focuses/aims. 2. Each case should have more than one type of open data activity. 3. The cases work on different levels, i.e., municipal/regional/national. 4. The cases involve organizations and people willing and ready to cooperate in the research and share information required to conduct this research. The three cases we have focused on are NPOs: Open Knowledge Belgium, Open Knowledge Foundation Germany and CityLAB Berlin. We conducted eight semi-structured interviews with three NPO employees from each organisation, both online and in person. We interviewed employees who work on open data-related projects within the NPO. Additionally, we collected information from public web pages describing the open data projects. A summary of the data sources is presented in Table 5. We used an inductive approach to analyse the qualitative data due to the studyʼs exploratory nature. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 24 Table 5: Sources of the qualitative data Method Participants Description Semi-structured interviews Employees of three NPOs. Eight interviews in total. Online and in-person interviews were conducted individually with available employees who work on the projects related to and using open data. Duration: 1h Period of the data collection: September 2022 to December 2023 Information from the relevant websites Twelve webpages related to the NPOsʼ open data projects The descriptions of the relevant open data projects were collected. 5.3 Results In general, our results support the literature's findings on how open data is contributed to the open data ecosystem by NGOs and what their motivations can be to do it (Enaholo, 2017; Ricker et al., 2020). NGOs create applications that aggregate different data sources or enhance the available data for such tools. This improved data is then available as open data for any user to download, often with an overall project being open source. NGOs also take the role of the data demander and advisor and build relations with data providers who might need to be more willing or capable of opening their data. Thus, NPOs get closed-off data from the providers and republish it as open data. Moreover, NPOs can collect the needed data for their projects and provide it to any other user as open. The motivations behind the NPOsʼ publishing the available data as open vary; some may be more prominent than others for the organisation. Overall, we found five reasons that can motivate NGOs to contribute open data back to the open data ecosystem. We also found two barriers to these motivations that can stop NGOs from sharing their data as open. 5.3.1 Motivations Firstly, opening the data can serve demonstrative purposes. Creating a project with open data enhanced or reused and made available can encourage other stakeholders or make them more aware of its availability. NPOs that target community needs are motivated by showing the power of open data and engaging relevant stakeholders, such as other potential data providers, to open relevant data. Secondly, NPOs are motivated by the help they can provide with the local and global societal issues that align with their organisational goals. Many aim to educate by sharing open data with communities, encouraging the reuse of open data, or contributing the data to the cause. Open government data might be available on topics and issues that become relevant, but this data needs to be utilised. By finding ways to enhance the data and publish it as open, the NPO can highlight it and help those affected by the issue. For example, as energy consumption became more relevant due to rising prices, CityLab Berlin created a tool showing the energy usage of public buildings with the data used in it being available as open. Providing open data to the targeted communities can help them educate themselves on the issue. Although they might need tools and the help of an intermediary to interpret the data, the NPOs can overcome the findability and accessibility barriers that the community may face. Those communities are then more engaged in solving their local issues. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 25 Thirdly, the positive feedback from the community motivates NPOs to pursue data sharing and continue their projects. Moreover, the community can give feedback on data issues and suggestions when they are interested and involved. Such feedback is welcomed by NGOs and motivates them to have projects that are open source and open data. Fourthly, some NPOs have transparency and openness of data and knowledge as their main goals. Thus, they are motivated by their organisational goals and the employees' personal beliefs to open up the data. If the data is not openly available and aggregated elsewhere on important issues, NPOs can be prompted to aggregate and collect the data themselves from closed sources. As the data is published, the NPOs can open the previously closed data. For example, CityLab Berlin has a platform to help find local services for mental health help, for which the data has been collected from various closed or partially open sources, and through the project, it is now available as open. Fifthly, NPOs distinguish themselves from for-profit organisations. They want their data to be reused widely by and benefit a variety of communities, NGOs, and governmental organisations, if applicable. Thus, NPOs can be motivated by the opportunities for other stakeholders that they would create. That is most likely achieved if the data is open. To conclude, we found five motivations of NGOs to open their data: (1) showing the power of open data, which would increase other stakeholdersʼ awareness of available open datasets; (2) utilising available open data to help with the local and global societal issues; (3) receiving positive feedback from the community, which can act as an external motivator for NGOs to push forward with open data project; (4) following organisational goals and the personal beliefs of the employees in openness and transparency; and (5) creating opportunities for other stakeholders to reuse NGOsʼ data for their benefit. Table 6: Motivations for NPOs to contribute open data Motivation Description Show the power of open data Creating a project with enhanced or reused open data can engage other stakeholders or make them more aware of the open data available Help with the local and global societal issues There is open government data that might be available on societal issues, but not utilised. By finding ways to enhance the data and publish it as open, the NPO can highlight it and help those affected by the issue Receive positive feedback from the community The community can give feedback on the issues with the data and give suggestions when they are interested and involved which motivates NGOs to continue the project Follow organisational goals and the personal beliefs of the employees in openness and transparency Some NPOs have transparency and openness of data and knowledge as their main goals and employees join NPOs because they share the vision Create opportunities for other stakeholders NGOs want their data to be reused widely by and benefit a variety of communities, NGOs, private and governmental organisations 5.3.2 Barriers NGOs face barriers that can demotivate them from sharing their data, as open data is closely related to their resources (Salamon and Anheier, 1992). As NGOs are non-profit organisations, funding their projects can be a pressing topic, especially for smaller-sized organisations. Thus, the first barrier is the need for more financial resources, i.e., funding for the projects that would let them publish their data. NGOs might need help to continue existing projects and stop providing D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 32 Table 12: Motivations for open data intermediaries to contribute open data Motivation Description Support the visibility of their organizations By providing open data, open data intermediaries can increase the visibility of their organization; hence, it is a form of marketing for their products and services. Support other partners within their networks Some open data intermediaries provide open data to support the business or operation of their partners, which they would also get the benefit from. This may further strengthen their position within their network. The desire to contribute to society Some open data intermediaries are driven by philanthropic or altruistic motivations to contribute open data that could benefit society. The availability of open data platforms to share open data Open Data intermediaries could be more motivated to release open data if there are open data platforms that could facilitate them to do so. This is because not all open data intermediaries have the capability and resources to develop and maintain their own data platforms. 7.3.2 Barriers Open Data intermediaries may be reluctant to share open data because of their business interests. For example, some of the datasets in the Living Atlas platform curated by Esri are not available as open data. Instead, they may only be used by Esriʼs customers on its software, ArcGIS. One of the reasons for this is to attract more customers to subscribe to the ArcGIS software, hence generating income for Esri. To some extent, this is understandable since Esri has pre-processed and curated those datasets, and such tasks require technical and human resources, including harmonising and validating them, ensuring they are reliable and usable by end users. Furthermore, current legislation around open data only compels a few intermediaries to contribute back to open data. An interviewee noted that, at present, the law mostly only requires the public sector to provide open data and not the other sectors. Thus, open data intermediaries outside the public sector are not legally responsible for delivering open data. The interviewee suggests that, in the absence of such a law, (public or private) funders can encourage open data intermediaries to provide open data by including such requirements in the contracts. Moving forward, policymakers should consider expanding the law requiring specific private and civil organisations to provide open data. Additionally, open data providers may also offer certain types of open data under the share-alike license, necessitating open data intermediaries to share the value-added data as open data as well. Additionally, not all open data intermediaries already have their data platform; hence, sharing open data would require additional infrastructure and investment in human resources. Once developed, maintaining the infrastructure will also incur recurring costs. As one of the interviewees pointed out, most open data platforms are currently limited to government data and do not facilitate open data contributions by non-government sectors. In summary, the barriers that prevent open data intermediaries from contributing to open data were gathered as presented in Table 13. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 33 Table 13: Barriers for open data intermediaries to contribute open data Motivation Description Protecting business interests Some open data intermediaries are hesitant to release (some of) their data as open data because they want to protect their business interests. No compelling legislation There are limited or no legal requirements that compel open data intermediaries to provide open data. Additional costs to develop and maintain open data platforms Developing and maintaining data platforms to release open data would incur additional costs to open data intermediaries. 7.4 Conclusion The study aims to investigate the motivations and barriers for open data intermediaries to contribute to open data. We uncovered four motivations and three barriers through interviews with open data intermediaries, providers, and users. The (potential) motivations are to support the visibility of their organisations, to support other partners within their networks, the desire to contribute to society, and the availability of open data platforms for them to share open data. The barriers to open data contribution are protecting their business interests, the lack of compelling legislation, and the additional costs of developing and maintaining open data platforms. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 34 8 Discussion In this Discussion section, we grouped similar motivations and barriers to contributing to open data by different non-government actors to identify the common motivations and barriers. Based on those common motivations and barriers, we also identified the enablers for them to contribute to open data. 8.1 Common motivations and barriers During Training Week 4 in April 2024 at KU Leuven, Belgium, a workshop was organised to identify common motivations and barriers for non-government actors to share open data. The workshop consisted of setting up an online whiteboard where the motivations and barriers for each actor, as written in previous sections, were written down. During the workshop, participants clustered into groups after an in-depth discussion on each of the motivations and barriers, going from the domain - or stakeholder-specific level to a more generic one. Each participant presented the motivation related to the stakeholder they represent, and then all the others commented until a shared agreement was reached. The main goal of the discussion was to identify the common motivations and barriers to defining shared trends. This work continued online a week after the workshop in a video call between the authors of this deliverable. Although these common motivations and barriers cluster individual non-government actors' motivations and barriers, they are not expected for all actors. Common obstacles and clusters might reflect sub-groups of actors with similar characteristics or contexts. 8.1.1 Common motivations Seven common motivations were found in the workshop, which are explained in detail below. Table 14 summarises the general picture and shows the stakeholders who mentioned each motivation. For many non-government actors studied, some motivations related to their benefit were identified. Such motivations include supporting other partners within an organisation network for their benefit, the private value of contributing, the feeling of belonging, and enjoyment. Aligning the different stakeholdersʼ benefit motivations is critical to achieving the goal of data sharing. Own benefit motivations are supported by the desire to create an impact, mentioned in the clusters of supporting other partners, helping the community, and creating social impact. Finally, the availability of and desire to improve technical skills and solutions are mentioned as motivations and potential motivations. This means that creating the correct technical environment can be an enabler for data sharing and including possible stakeholders in the process of improving the technical environment. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 35 Table 14: Common motivations to contributing open data Common motivations Nonspecialist actors Data journalists Elementary school students Non - governmental organisations Commercial organisations Open data intermediaries Supporting other stakeholders within an organisation network for their own benefit X X X Supporting and creating communities that would benefit from open data X X Private value X X X Belonging X X Creating social impact X X X X Engagement / enjoyment X X To improve technical skills or internal data processes X X 8.1.2 Common barriers Six common barriers were found in the workshop, which are explained in detail below. The general picture is summarised in Table 15, which stakeholders who mentioned each barrier marked. Regarding shared barriers, lack of resources is the most mentioned barrier, with 5 out of the 6 studied non-governmental actors mentioning it. Other considerably mentioned barriers are misaligned goals and interests, and a lack of technical tools is the most cited barrier, with 4 out of the 6 studied non-governmental actors saying them. The lack of technical tools in the barrier cluster pairs with the technical motivations described by some actors. Technical steering mechanisms are, therefore, shown to be crucial in motivating and creating barrier-free open data ecosystems where stakeholders can share their data. Deliverable D4.2 will delve in-depth into answering the questions these mechanisms pose. Regarding the misaligned goals and interests, creating spaces aligned with the domains and topics the non-governmental actors are interested in is important in creating a low-barrier environment and showing them the potential benefits of participating, following the principle shown in the shared motivations. D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 36 Two out of the six case studies stated the lack of governance mechanisms cluster. Stakeholders mentioned issues with a lack of compelling legislation and privacy concerns. Appropriate governance is, therefore, regarded as important in lowering the barriers and enabling data sharing in the open data ecosystem. Deliverable D4.3 will dive into answering the questions this cluster creates. Finally, three and one out of six of the case studies found a lack of data skills and literacy and a lack of awareness, respectively. Improved training in open data topics can help overcome these barriers. Table 15: Common barriers to contributing open data Common barriers Non - specialist actors Data journalists Elementary school students Non - governmental organisations Commercial actors Open data intermediaries Lack of data skills and literacy X X X Lack of governance mechanisms X X Lack of awareness about the value of open data X Lack of technical tools X X X X Misaligned goals and interests X X X X Lack of resources X X X X X D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 37 D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 38 8.2 Enablers This subsection answers RQ3: “How to turn the motivations and barriers to the sharing of nongovernmental data as open data into enablers?”. Once the motivations and barriers have been defined, situations that allow these motivations and barriers to be leveraged have been identified. The final list includes seven enablers, each relating to one or several motivations or barriers. They are described in Table 16, based on a reflection on the work done in the workshop. Table 16: Enablers and related motivations and barriers Enabler Related motivation Related barrier Availability of training in data skills and literacy Lack of data skills and literacy Availability of appropiate technical tools To improve technical skills or internal data processes Lack of technical tools Alignment of private value and interests with open data sharing Private value Misaligned goals and interests Availability of resources (financial, time, people/workforce) Lack of resources Existence of data-sharing communities Supporting other stakeholders, supporting communities, belonging Awareness about the social impact of open data sharing Creating social impact Lack of awareness Presence of engagement or enjoyment activities Engagement / enjoyment D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 39 9 Conclusion 9.1 Summary of the results and further actions This deliverable addressed the following three research questions: • RQ1: What are the motivations of non-governmental data holders to contribute to the open data ecosystem? • RQ2: What barriers do non-governmental data holders face when contributing to open data ecosystem? • RQ3: How can the motivations and barriers to sharing non-governmental data as open data become enablers? Seven common motivations for non-government data holders to contribute their data as open data were identified (RQ1): “Supporting other stakeholders within an organisation network for their benefit”, “Supporting and creating communities that would benefit from open data”, “Private value“, “Belonging”, “Creating social impact”, “Engagement/enjoyment”, and “To improve technical skills or internal data processes”. Six common barriers were also identified (RQ2): “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.” Finally, motivations and barriers were turned into seven common enablers (RQ3): “Availability of training in data skills and literacy”, “Availability of appropriate technical tools”, “Alignment of private value and interests with open data sharing”, “Availability of resources (financial, time, people/workforce)”, “Existence of data-sharing communities”, “Awareness about the social impact of open data sharing”, and “Presence of engagement or enjoyment activities”. Further deliverables in WP4 will build on D4.1, exploring technical (D4.2) and governance (D4.3) strategies to steer the behaviour of non-government data holders towards open data. 9.2 Limitations One of the main limitations of the study we conducted is that the analysis is based mainly on case studies and related interviews that are limited in number and heterogeneous. A common drawback of case studies is that they are often criticized for being subjective, biased, or lacking in rigour (Idowu, 2016). Because they focus on particular domains, generalising results takes a lot of work. Bias can be another issue with case studies, as it can step into various stages by selecting non-representative cases or by interpreting the results in a way that favours a specific view, potentially skewing results. Furthermore, small sample sizes hinder representativeness, and replication can be problematic due to the unique nature of each case. Additionally, narrow scopes may overlook crucial contextual elements. Lastly, the subjective nature of qualitative data interpretation adds another layer of complexity. These limitations underscore the need for careful consideration and critical evaluation of conclusions drawn from case studies. What we observe and report in this deliverable is based on the researcher's interpretation and selection of data, which personal views, assumptions, or preferences can influence. A case-control study could have improved the systematic observation, improving the quality and completeness of the results, but it was out of scope. Despite the limitations of the performed research, we still hold that the research results provide welcome new preliminary insights in the motivations, barriers and enablers of non-governmental D4.1 Motivations of non-government actors to become active contributors to the Open Data ecosystem 40 data holders to contribute open data to the open data ecosystem. 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