D2.1 Open data user needs: seven flavours

Towards a sustainable Open Data ECOsystem D2.1 Open data user needs: seven flavours 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 opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains. D2.1 Open data user needs: seven flavours 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 D2.1 Deliverable Title Open data user needs: seven flavours Dissemination Level Public Deliverable Leader TU Delft (TUD) Submission Date 31-05-2023 Editor Ingrid Mulder, TUD Co-author(s) Davide Di Staso, María Elena López Reyes, Georgios Papageorgiou, Alejandra Celis Vargas, Liubov Pilshchikova, Caterina Santoro, Héctor Ochoa Ortiz, Umair Ahmed, Ahmad Ashraf Ahmad Shaharudin Version # Date Description (Section, page number) Author & Organisation V0.0 29-03-2023 Plenary workshop during training week on understanding users (needs) & providers (needs) from multiple perspectives. Discussion with all ESRs facilitated on a MIRO board Ingrid Mulder, TUD, Glenn Vancauwenberghe, KU Leuven, Silvia Cazacu, KU Leuven ; All ESRs parti cipated in the workshop and created a shared understanding of the various roles of users and providers in a data ecosystem V0.1 28-04-2023 First setup with contributions from ESR perspectives ESR01: Davide Di Staso, TUD; ESR06: María Elena López Reyes, AAU; ESR09: Georgios Papageorgiou, ESR10: Alejandra Celis Vargas, AAU; Liubov Pilshchikova, TUD; ESR12: Caterina Santoro, KU Leuven; ESR13: Héctor Ochoa Ortiz, UNICAM; ESR14: Umair Ahmed, UNICAM; ESR15: Ahmad Ashraf Ahmad Shaharudin, TUD V0.1a 03-05-2023 Feedback and alignment meeting to discuss structure and contribution of the deliverable Ingrid Mulder, TU Delft; Francisco J. Lopez-Pellicer, UNIZAR; Andrea Polini, UNICAM; Bastiaan van Loenen, TU Delft together with all ESR contributors V0.1b 08-05-2023 Restructure of Introduction and Conclusions ESR15: Ahmad Ashraf Ahmad Shaharudin, TUD; ESR12: Caterina Santoro, KU Leuven. D2.1 Open data user needs: seven flavours Version # Date Description (Section, page number) Author & Organisation 10-05-2023 Collaborate peer-review meeting Ingrid Mulder, TU Delft; Francisco J. Lopez-Pellicer, UNIZAR; Andrea Polini, UNICAM; Bastiaan van Loenen, TU Delft together with all ESR contributors V0.2 17-05-2023 Second draft. Processed feedback obtained from the peer review meeting on May 10, 2023 ESR01: Davide Di Staso, TUD; ESR06: María Elena López Reyes, AAU; ESR09: Georgios Papageorgiou, ESR10: Alejandra Celis Vargas, AAU; ESR11: Liubov Pilshchikova, TUD; ESR12: Caterina Santoro, KU Leuven; ESR13: Héctor Ochoa Ortiz, UNICAM; ESR14: Umair Ahmed, UNICAM; ESR15: Ahmad Ashraf Ahmad Shaharudin, TUD V0.2a 26-05-2023 Review Francisco J. Lopez-Pellicer, UNIZAR; Andrea Polini, UNICAM; Loukis Euripides, UAEGEAN V0.3 30-05-2023 Third draft. Integration of feedback received from QA peer review Ingrid Mulder, TUD V0.4 30-05-2023 Final report Ingrid Mulder, TUD V0.5 31-05-2023 Approval Bastiaan van Loenen, TUD V1.0 31-05-2023 Final editing Danitsja van Heusden, TUD D2.1 Open data user needs: seven flavours Table of Contents Abbreviations ____________________________________________________________________ 7 1. Introduction __________________________________________________________________ 8 1.1. Background ______________________________________________________________ 8 1.2. Role of this deliverable in the ODECO project ___________________________________ 8 2. Methodology ________________________________________________________________ 10 2.1. Identifying user needs _____________________________________________________ 10 2.2. Research approach _______________________________________________________ 11 3. Non-specialist users __________________________________________________________ 12 3.1. Introduction _____________________________________________________________ 12 3.2. Method ________________________________________________________________ 12 3.3. Results ________________________________________________________________ 12 3.4. Conclusions _____________________________________________________________ 14 4. Local government ____________________________________________________________ 15 4.1. Introduction _____________________________________________________________ 15 4.2. Method ________________________________________________________________ 15 4.3. Results ________________________________________________________________ 15 4.4. Conclusions _____________________________________________________________ 17 5. Data journalism ______________________________________________________________ 19 5.1. Introduction _____________________________________________________________ 19 5.2. Methods ________________________________________________________________ 19 5.3. Results ________________________________________________________________ 20 5.4. Conclusions _____________________________________________________________ 21 6. Students ___________________________________________________________________ 23 6.1. Introduction _____________________________________________________________ 23 6.2. Methods ________________________________________________________________ 23 6.3. Results ________________________________________________________________ 24 6.4. Conclusions _____________________________________________________________ 26 7. Non-governmental organisations ________________________________________________ 27 7.1. Introduction _____________________________________________________________ 27 7.2. Method ________________________________________________________________ 27 7.3. Results ________________________________________________________________ 28 7.4. Conclusions _____________________________________________________________ 29 8. Central/regional government ____________________________________________________ 30 D2.1 Open data user needs: seven flavours 8.1. Introduction _____________________________________________________________ 30 8.2. Method ________________________________________________________________ 30 8.3. Descriptive results of the literature review _____________________________________ 31 8.4. Main findings ____________________________________________________________ 31 8.5. Conclusions _____________________________________________________________ 34 9. Companies _________________________________________________________________ 35 9.1. Introduction _____________________________________________________________ 35 9.2. Method ________________________________________________________________ 36 9.3. Results ________________________________________________________________ 36 9.4. Conclusions _____________________________________________________________ 36 10. Artificial Users _______________________________________________________________ 38 10.1. Introduction ___________________________________________________________ 38 10.2. Method ______________________________________________________________ 38 10.3. Results ______________________________________________________________ 39 10.4. Conclusions ___________________________________________________________ 40 11. Open data intermediaries ______________________________________________________ 41 11.1. Introduction ___________________________________________________________ 41 11.2. Method ______________________________________________________________ 41 11.3. Results ______________________________________________________________ 41 11.4. Conclusions ___________________________________________________________ 44 12. Discussion and Conclusions ____________________________________________________ 45 12.1. Categorization of user needs _____________________________________________ 46 12.2. Limitations ____________________________________________________________ 48 12.3. Towards a research agenda ______________________________________________ 48 References _____________________________________________________________________ 50 List of figures Figure 1: Analysis framework based on Beetham’s Learning Activities Design Approach (Beetham, 2019) ..................................................................................................................................................... 24 Figure 2: BRYT Keyword Extraction ....................................................................................................... 39 Figure 3: Interconnection between user groups according to shared needs. ...................................... 45 List of tables Table 1: Several definitions of user needs ............................................................................................ 10 Table 2: First identified non-specialist user need ................................................................................. 13 Table 3: Second identified non-specialist user need ............................................................................ 14 D2.1 Open data user needs: seven flavours Table 4: Needs categories of local governments concerning the use of open data and their respective sources. ................................................................................................................................................. 16 Table 5: Needs of journalists identified in the literature ...................................................................... 21 Table 6: Needs categories of the NG(P)Os and their respective sources. ............................................ 29 Table 7: Concepts associated with the user needs of disadvantaged groups ...................................... 31 Table 8: Types of actors of open data intermediaries (Shaharudin et al., 2023) ................................. 41 Table 9: Tasks of open data intermediaries (Shaharudin et al., 2023) ................................................. 42 Table 10: Objectives of open data intermediaries (Shaharudin et al., 2023) ....................................... 42 Table 11: Challenges faced by open data intermediaries ..................................................................... 43 Table 12: Needs of open data intermediaries ...................................................................................... 43 D2.1 Open data user needs: seven flavours 7 Abbreviations AI Artificial Intelligence CARE Collective benefit, Authority to control, Responsibility, Ethics CBO Community-Based Organisations D Deliverable EC European Commission ESR Early Stage Researcher EU European Union M Milestone NGO Non-Government Organisation NPO Non-Profit Organisation OD Open Data ODECO Open Data ECOsystem WoS Web of Science 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 Spatineo SPATI Finland 12 SWECO SWECO Netherlands 13 The government lab GLAB 14 Agency for Data Supply and Infrastructure ADSI D2.1 Open data user needs: seven flavours 8 1. Introduction 1.1. Background The European Commission (EC) made a projection that by 2025, the net worth of the European Union (EU)’s data economy is predicted to be €829 billion and is going to increase significantly over the following few years (Kumpula-Natri, 2021). Open data, which is data that is made available free of charge, with an open license, and in an open, machine-readable format, is expected to generate even more value (European Commission, 2011). It can enhance the effectiveness and efficiency of public services, increase institutional accountability, boost citizen participation, accelerate scientific progress, and foster the creation of other economic and social values (Hossain et al., 2016; Janssen et al., 2012; Zhu et al., 2019). In line with this enormous potential, the EC has published the directive on open data and the re-use of public sector information (Open Data Directive) that entered into force on 16 July 2019. Despite the benefits of open data, its full potential has not yet been realised due to shortcomings in the current open data systems. Current developments in the field of open data are characterised as highly fragmented. Experts suggest that collaboration and coordination are necessary and that government efforts alone are not enough to make open data available and valuable (Harrison et al., 2012; Pollock, 2011; Zuiderwijk et al., 2014). Open data is often developed in different domains in isolation and with little involvement of potential users, resulting in approaches that significantly limit open data reusability for users. The current “one-way street” and top-down approach to open data is not ideal, as it does not consider the user needs and may lead to limited use and value generation. Therefore, a more collaborative and user-centric approach is essential for the optimal use and value of open data (Pollock, 2011; Van Loenen et al., 2021). To address this, many researchers have advocated for the “open data ecosystem” approach (Davies, 2011; Poikola et al., 2011; Pollock, 2011; van Loenen et al., 2018; van Loenen et al., 2021). While this concept is still developing, at the heart of the ecosystem metaphor is the recognition of the diversity of data users’ needs that emphasizes the importance of not only data suppliers but also users (Davies & Edwards, 2012; Pollock, 2011; van Loenen et al., 2021). To achieve this, an important step is shifting from a supplier-driven to a user-driven perspective. ODECO is working towards creating a user-driven ecosystem to better match the demand and supply of open data. Previous studies have suggested ways to improve the use of open data (Olausson, 2016; Ruijer et al., 2017; Susha et al., 2015), but more research is needed to understand the needs of different user types throughout the open data lifecycle1. Only then can appropriate governance and technical measures be implemented. 1.2. Role of this deliverable in the ODECO project In this deliverable, we aim to address the first knowledge gap of ODECO, which is understanding the diverse needs of different types of users (Task 2.1). This will help us create a more user-driven open data ecosystem. At the ODECO project’s proposal stage, seven major categories of open data users were identified: non-specialist data users, government, intermediaries & companies, journalists, students, non-government organisations (NGOs), and artificial users. Hence, the proposed title of this deliverable is “Open data user needs: seven flavours”. These seven user types are then translated into nine ESRs in the project, looking into user types, with local and regional/central governments and intermediaries and companies being divided into four separate user types. Hence, the resulting nine types of open data users are studied in the current report. 1 Open data lifecycle is “the process and practices around handling data, starting from its creation, through the provision of open data to its use by various parties” (Charalabidis et al., 2018b) D2.1 Open data user needs: seven flavours 9 The structure of this report is as follows. Developing a user-driven open data ecosystem starts with identifying the needs of users to create value from open data. Chapter 2 elaborates upon the methodology to identify user needs and presents the agreed-upon research approach. The following chapters present the user needs of the user types according to the nine different contexts: • Chapter 3: Non-specialist data users (ESR1) • Chapter 4: Local government (ESR6) • Chapter 5: Journalists (ESR9) • Chapter 6: Students (ESR10) • Chapter 7: NGOs (ESR11) • Chapter 8: Central/regional government (ESR12) • Chapter 9: Companies (ESR13) • Chapter 10: Artificial users (ESR14) • Chapter 11: Open data intermediaries (ESR15) Chapter 12 concludes with a categorisation of user needs that brings our findings together. We close the report with a research agenda helpful for our ongoing research. However, in the first place, this report is the basis for developing technological and governance measures to satisfy user needs in Task 2.2 and 2.3. D2.1 Open data user needs: seven flavours 16 Table 44: Needs categories of local governments concerning the use of open data and their respective sources. Category References Count Reliable and meaningful data (Gao & Janssen, 2022a; Golub & Lund, 2021; Milojevic-Dupont et al., 2020; Najafabadi & Cronemberger, 2022; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021a; J. Zhang et al., 2021) 8 Communication and coordination across and within open data systems (Gao & Janssen, 2020; Hlabano & Van Belle, 2019; Kassen, 2013; Meng & DiSalvo, 2018; Najafabadi & Cronemberger, 2022; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021; J. Zhang et al., 2021) 9 Legal, technical, and operational adaptability and efficiency (Cabitza et al., 2020; Cantador et al., 2020; Gao & Janssen, 2022; Golub & Lund, 2021; Hlabano & Van Belle, 2019; Jarke, 2021; Meng & DiSalvo, 2018; Najafabadi & Cronemberger, 2022; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021; J. Zhang et al., 2021) 12 Data protection, representation, and validity (Cantador et al., 2020; Gao & Janssen, 2020; Golub & Lund, 2021; Hlabano & Van Belle, 2019; Jarke, 2021; Meng & DiSalvo, 2018; Milojevic-Dupont et al., 2020; Najafabadi & Cronemberger, 2022; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021) 11 Reliable and meaningful data When local governments act as implementers of open data initiatives and are tasked to capture, publish, update, and aggregate data, they need to ensure reliable data, which means that the data being used need to be accurate, consistent, and integrated (Gao & Janssen, 2022; Milojevic-Dupont et al., 2020; Najafabadi & Cronemberger, 2022; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021; J. Zhang et al., 2021). For that to happen, one of the critical needs is having the capacity to capture data users’ input (B. Wilson & Cong, 2021; J. Zhang et al., 2021), which can also help governments to be informed about the needs and priorities of non-government users (B. Wilson & Cong, 2021). Some other consideration when publishing and updating data is the ability to keep the timeliness (Gao & Janssen, 2020), contextualise the data (J. Zhang et al., 2021), build large datasets, and enable bulk access (Runeson et al., 2021). Taking care of these aspects will enable the publication of meaningful data that can drive innovation for activities such as machine learning (Runeson et al., 2021) or research (Najafabadi & Cronemberger, 2022). Communication and coordination across and within open data systems When local governments act as initiators of open data initiatives, they need proper communication and coordination so that the initiatives have the support and engagement of local businesses and communities and align standards, business rules, and architecture. These will enable data flow and drive innovation and value creation (Hlabano & Van Belle, 2019; Kassen, 2013; Najafabadi & Cronemberger, 2022; B. Wilson & Cong, 2021). It is also necessary to have the economic resources and the appropriate business models to drive the commitment of the different participants (Meng & DiSalvo, 2018; B. Wilson & Cong, 2021). Furthermore, local governments need to coordinate with other participants to ensure robust metadata and satisfy the increase in data demand, for example, when data is used for prediction models (Gao & Janssen, 2020; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021). Additionally, accessing end-users’ feedback is crucial for evaluating and prioritizing the data availability (B. Wilson & Cong, 2021). Finally, as implementers of open data initiatives, local governments need trust and coordination mechanisms so that they can prevent distrust and competition between public and private actors (Runeson et al., 2021; Schrotter & Hürzeler, 2020; J. Zhang et al., 2021). D2.1 Open data user needs: seven flavours 17 Legal, technical, and operational adaptability and efficiency When local governments design open data initiatives, they need to increase legal, technical, and operational efficiency. Some of the considerations to achieve it are the awareness of digital literacy gaps (Jarke, 2021; Meng & DiSalvo, 2018) when designing open data solutions and having the legal and technical tools to be able to integrate different licensing frameworks (Hlabano & Van Belle, 2019; Runeson et al., 2021; Schrotter & Hürzeler, 2020; B. Wilson & Cong, 2021) and technologies (Gao & Janssen, 2022; B. Wilson & Cong, 2021; J. Zhang et al., 2021); for example, enabling the convergence of Building Information Modelling (BIM) and Geographic Information System (GIS) technologies (Runeson et al., 2021; Schrotter & Hürzeler, 2020). The technological considerations should also include upgrading tools and systems (Schrotter & Hürzeler, 2020; J. Zhang et al., 2021), which is necessary to cover the end-user demands. To have enough resources, they also need to sustain the interest of elected officials (Hlabano & Van Belle, 2019; Najafabadi & Cronemberger, 2022; B. Wilson & Cong, 2021). The operational considerations should address time efficiency by, for example, making data publishable through fewer steps (Najafabadi & Cronemberger, 2022) and having the training, specialised teams, and software so that they can automate processes (Gao & Janssen, 2022; B. Wilson & Cong, 2021; J. Zhang et al., 2021) and quickly identify insights primarily when the solutions use crowdsourcing as data collection method (Cabitza et al., 2020; Cantador et al., 2020; Golub & Lund, 2021; Hlabano & Van Belle, 2019; B. Wilson & Cong, 2021). They also need to have enough employees to properly follow up on data requirements, evaluate and prioritise data provisions, and generate evidence of the impact of open data in policymaking (Gao & Janssen, 2022; B. Wilson & Cong, 2021). Data protection, representation, and validity When local governments act as initiators of open data initiatives, they need ethical representation and personal data protection mechanisms to prevent pre-existing software biases, potential mistreatment, and misinterpretation of the data (Najafabadi & Cronemberger, 2022). Data protection of personal data and validation of data is particularly important when initiatives include AI technologies to ensure minority groups can access the benefits of open data and prevent harm to individuals, such as social exclusion (Cantador et al., 2020; Gao & Janssen, 2020; Meng & DiSalvo, 2018; Runeson et al., 2021; B. Wilson & Cong, 2021). When local governments publish data, they also need to consider different contexts and languages to ensure the representation of local communities (Golub & Lund, 2021; Jarke, 2021; Meng & DiSalvo, 2018; Milojevic-Dupont et al., 2020; Najafabadi & Cronemberger, 2022; Runeson et al., 2021; B. Wilson & Cong, 2021). The open-data solutions owned by local governments also need to consider biases in data results presentations, for which it is important to offer adaptable communication and visualisation tools (Cantador et al., 2020; Schrotter & Hürzeler, 2020). Finally, when evaluating the impact of open government initiatives, local governments want input from a diverse group of users, not only specialists such as developers, to include them in impact measures (Golub & Lund, 2021; Hlabano & Van Belle, 2019). 4.4. Conclusions The results show that the roles and needs or desires that local governments can acquire within the open data processing cycle can vary according to the goals to be achieved and the context of the initiative. With this study, the understanding of the role of local municipalities is broadened. More than acting solely as a supplier within the open data ecosystem, local governments tend to act as reusers as they are the initiators, promoters, coordinators, implementers, evaluators, and/or owners of the open data initiatives. According to the results, there are four themes that act as research directions to investigate how local government needs can be achieved: (1) reliability and meaningfulness of data, (2) communication and coordination across and within the open data systems, (3) data protection, representation, and validity, and (4) legal, technical, and operational adaptability, and efficiency. However, there is an opportunity to take more empirical approaches to create more accurate evidence and verify the results. Further research is needed to understand the links within the stages in the open data processing cycle, the roles of local governments, and the needs that arise. This study also has some limitations. The first one is related to the coverage of the study, as the scope of the literature D2.1 Open data user needs: seven flavours 18 was narrow; what that means is that the number of studies included in the review is limited and does not consider a long period limiting the validity and generalizability of the results. Finally, more descriptive methods could also help to understand better the context in which local governments act from different roles; for example, by focusing on the domains of application or describing the maturity level of the open government data ecosystem in a particular context. D2.1 Open data user needs: seven flavours 19 5. Data journalism 5.1. Introduction The importance of data in journalism is becoming increasingly prominent over the years. There have been great steps in the adoption of data-related technologies in journalism since Philip Meyer established Computer-Assisted Reporting (CAR) in the 1960s (Gray et al., 2012). Since that time, the use of data in journalism has evolved, with the contemporary term defined as data journalism (Rogers, 2008). This new trend has some significant differences from CAR. While CAR is focused on the collection and analysis of data, data journalism covers all the stages of the process, from the collection and the writing of the article to the visualisation of the data (Veglis & Bratsas, 2017). As journalism was evolving, there were also developments in the field of data. The beginning of the open data movement is considered the publication “On the Full and Open Exchange of scientific data” ( On the Full and Open Exchange of Scientific Data , 1995) when the term open data was quoted for the first time. Since then, the open data movement has grown, and governments have started opening their data to increase transparency, accountability and innovation (Publications Office of the European Union, 2022). Open data movement and data journalism have the same objectives at heart. They both aim to increase transparency and accountability. As the open data movement provides more information, journalists can use them and not only increase transparency and accountability but also prove the value of open data. That creates a synergy that should be promoted as it is beneficial to our society. Therefore, it is important to identify the needs and requirements journalists must guide the policies and the technological intonation in the field of open data to our collective advantage. To discover these needs and requirements, a research question was formulated: What are the most significant user needs and requirements that are encountered in published papers? 5.2. Methods The method used for the analysis of the literature and to identify the topics is a systematic literature review (SLR) (Okoli, 2015; Xiao & Watson, 2019). The initial results from the search for abstract and keywords in four scientific databases, IEEE Xplore, Web of Science, Science Direct, and Scopus, for user needs and requirements were not sufficient and, therefore, we broadened the research by removing the terms “User needs” and “user requirements” and we deducted them during the analysis of the papers. The keywords that were finally selected are divided into the two main focuses of the research, open data, and journalism. To identify the journalistic aspect of the publications, “Journalism”, “journalists”, and “journalist” have been used. Other keywords, such as “media” or “reporting”, were deemed unsuitable since the results were not related to journalism. For the open data, the term “Open government data” was also used along with “open data”, although it is a subset in the domain of open data. The reason for the use of it is that since the terms were quoted in the search, the databases were returning only exact matches. Therefore, during the use of the “open data” keyword, all the publications with the term “open government data” were not included. The goal of the inclusion and exclusion criteria is to ensure that only publications that are relevant to the topic of the research will be analysed. The criteria in this research are making sure that the main focus of the literature is the use of open data in journalism. For the SLR, four scientific databases were searched to discover publications that are relevant to the research questions. Based on the selected keywords, a query for each database was formed and after the use of the queries in the databases, 131 publications were extracted. The next step was to remove the duplicates that were 34 and to filter out the ones that were not in the English language. After the language filtering, we ended up with 82 publications, there were excluded eleven that were in Spanish, two in Portuguese, one in Turkish and one in French. D2.1 Open data user needs: seven flavours 20 During the screening, based on the titles and the abstracts of the papers, the remaining exclusion criteria were applied to the 82 papers we ended up with 45. From the initial 82 papers, two records were excluded for not having access to them, five for not being research papers, and 30 for not being focused on open data or journalism. The next step was to identify the papers in which it was possible the identification of user needs and requirements, and the result was 36 publications. 5.3. Results To organise the user needs that emerged from the analysis of the papers, we categorise them into four distinct steps. These steps are part of open data and data journalism life cycles. These life cycles are models describing the process of handling data from creation to consumption and are described in detail by open data (Charalabidis et al., 2018) and data journalism (Gray et al., 2012) experts. Four categories are Discoverability, Quality, Analysis and Communication. Although these categories were based on the life cycle of open data and data journalism, they were not enough to accommodate all the user needs that were discovered, so we had to add two more categories: Skills and Ethics. The skills incorporate the need for journalists to be accustomed to new technologies so they can effectively use open data sources. Ethics are about the need for assurance that the use of open data cannot harm the citizens. The research results are presented in detail below in the corresponding paragraph of each need. Table 5 presents the needs with their literature source and the number of times each need was encountered. Discoverability Most of the publications that mention the need for improved discoverability of data are technological tools that provide the functionality to explore datasets, but they can be split into two distinct categories with different needs. In the first category, they exist tools that are used for investigative journalism, these are designed to search data sets and topics (Böhm et al., 2010; Klímek et al., 2018; Paley et al., 2021). The second category is used for immediate journalism, which focuses on searching open data sources for new information as fast as possible (Gottron et al., 2015; Ocaña et al., 2021). Finally, two publications are about the difficulty journalists encounter in the discovery of open data (Bozsik et al., 2022; Martin et al., 2022). Quality There were also identified publications that mention the need for quality. There are cases where the need for better quality data is just mentioned vaguely (Porlezza & Splendore, 2019), but also more specific needs for quality are detected, like datasets that require cleaning or are missing data (Bozsik et al., 2022). Also, there are mentioned cases that do not have to do with the technical quality of the data, cases where the journalists do not trust the released data sets (Camaj et al., 2022) or they find them to be of low importance for their work (Faini & Palmirani, 2016). Analysis Interestingly, all the publications that mention the analysis of data are focused on the processing of big datasets. Of the five publications that are focused on data analysis, four advocate the use of technical tools to help journalists with their work (Andrews & Da Silva, 2013; Le Borgne et al., 2016; Sandoval-Martín & La-Rosa, 2018; Shehu et al., 2016). The fifth publication supports crowdsourcing and the use of games to make the public participate in the data analysis process (Handler & Ferrer Conill, 2016). From that finding, we identify a need for a way to handle large volumes of information. Communication The communication need is specialised since the topic of the research is journalism, and there is a great need for the users to be able to convey their findings in an interesting and understandable way for the public. In the publications, there is a great focus on visualisations and compelling storytelling. In many publications, the introduced need is to find a way to present complicated data in a form that can be easily understandable (Rind et al., 2016; Windhager et al., 2016). Another important need is for D2.1 Open data user needs: seven flavours 21 compelling storytelling. Very interesting is the case of the NHS winter crisis (Lawson, 2022), where the published articles did not have the expected impact on the public although they were accompanied by extensive data, the main problem was that they were not accommodated by interesting personal stories. So, the outcome of the communications needs is not only the adoption of technological tools but also proper storytelling techniques that can elevate the published articles. Skills The lack of digital skills of journalists and their limitation in the use of the available open data sources is a theme that is mentioned in most of the literature. Although there are some publications that cover this skill deficiency in the overall process of journalism, from the collection of data for the publication of the articles and therefore are mentioned in a separate category of needs. In the literature, there were mentioned the cases of hackathons (Boyles, 2020) and the involvement of stakeholders (Kassen, 2018a) from other professions to cover this lack of digital skills. On the other hand, there are presented cases of educational curriculums for journalists that are training them in data journalism and the use of open data (Radchenko & Sakoyan, 2016; Splendore et al., 2016). Furthermore, there are publications that cover specific topics in the skills that journalists need. There are especially mentions of the lack of data analysis skills (Baack, 2018; Gray et al., 2018; Ridgway, 2016) but also of low coding tools (Petricek, 2022) that are designed to bypass this problem. Finally, cases where the lack of digital skills is an impediment to the adoption of new technologies by journalists were encountered (Berntzen et al., 2019; Tabary et al., 2016). Ethics The ethical risk that was discovered is the possibility of de-anonymisation of the published data (Bozsik et al., 2022; Krotoski, 2012). Although the datasets used by journalists are anonymised as are all the open data datasets, the produced synthesis of them in a new dataset may lead to the possibility that individuals can be identified. Although any incident of de-anonymisation that could expose the identity of any individual has never occurred, it is a great fear for journalists. If an incident like that occurs, it can have severe consequences, especially when the data sets contain sensitive and personal data like health-related information. Therefore, there is a need for stronger anonymisation tools and techniques that journalists can use to avoid that risk. Table 55: Needs of journalists identified in the literature Category Papers Count Discoverability (Böhm et al., 2010; Bozsik et al., 2022; Gottron et al., 2015; Klímek et al., 2018; Martin et al., 2022; Ocaña et al., 2021; Paley et al., 2021) 7 Quality (Bozsik et al., 2022; Camaj et al., 2022; Porlezza & Splendore, 2019) 3 Analysis (Andrews & Da Silva, 2013; Handler & Ferrer Conill, 2016; Le Borgne et al., 2016, 2016; Sandoval-Martín & La-Rosa, 2018) 5 Communication (Araújo, 2019; Brolcháin et al., 2017; Evéquoz & Castanheiro, 2019; Gupta et al., 2016; Lawson, 2022; Rind et al., 2016; Smith, 2016; Windhager et al., 2016) 8 Skills (Baack, 2018; Berntzen et al., 2019; Boyles, 2020; Gray et al., 2018; Kassen, 2018a; Petricek, 2022; Radchenko & Sakoyan, 2016; Ridgway, 2016a; Splendore et al., 2016; Tabary et al., 2016) 10 Ethics (Bozsik et al., 2022; Krotoski, 2012) 2 5.4. Conclusions In this research, there were identified six different user need categories, Discoverability, Quality, Analysis, Communication, Skills, and Ethics. The most prominent category is Skills since it covers the whole spectrum of the data journalism process. The category with the next mentions is Communication, which is interesting since it implies that the focus for the journalists is the presentation of the story and not the data themselves. Another interesting argument on the D2.1 Open data user needs: seven flavours 22 Communication category is that journalists must acquire experience in this new type of reporting so they can combine visualisations with compelling storytelling. A special mention must be made for the Analysis since all the publications were focused on the need to be able to analyse big datasets. This is something that could be an interesting research question in the future. Additionally, we found that there was a fear that open data journalism could unwillingly expose the private information of citizens. Another interesting finding that was discovered during this research, and it is not related to the user needs, is that the lack of skills is an impediment to the adoption of new technologies and, therefore, new data sources by the journalists. This is a discovery that can be evolved into an interesting research topic since it will explain why data journalism is not so popular during a time when there is an abundance of available data sources. Finally, we should mention that this is ongoing research, and although some interesting findings have been detected, we must be cautious when drawing conclusions. The presented results are preliminary, and more analysis of the literature is needed. After that, interviews with experts in the field of journalism must be conducted. D2.1 Open data user needs: seven flavours 23 6. Students 6.1. Introduction Open Data Education is gaining relevance for straightening and training a literate community that can further benefit from Open Data (Cook et al., 2018). Therefore, the insertion of Open Data in educational systems becomes increasingly important in the Open Data movement. For example, the International Open Data Chapter suggests engaging with schools to ensure the inclusiveness of Open Data in society (International Open Data Charter, 2015). Although Open Data Education might help to stimulate bigger shifts towards Open Data ecosystems (van Loenen et al., 2021a), Open Data is normally created and released without considering its possible use for educational purposes (Coughlan, 2020). Open Data Education should consider the challenges faced by non-data experts such as students and teachers who participate in formal or hybrid (formal/informal) learning environments. For example, several researchers have highlighted that non-data expert users might face barriers associated with the complexity of handling the data and participation in the open-data process (Janssen et al., 2012). Uncovering the student’s needs might be the first step in a journey for enhancing the usage of Open Data as an educational resource. Open Data Education The novel field of Open Data Education has the potential of empowering students with digital skills and critical thinking through work directly with real facts (Celis Vargas & Magnussen, 2022). Open Data as an educational resource can contribute to create meaningful learning experiences by countering current flaws in educational systems such as the gap between classroom activities and real-life, and the lack of students and teachers’ motivation (Cook et al., 2018; Coughlan, 2020; Saddiqa, Rasmussen, et al., 2019; Wolff, Gooch, et al., 2016). Traditional educational models are criticised for their inability to develop essential skills required for civic engagement and the labour market (Elisa Raffaghelli, 2020; Saddiqa et al., 2021a). Literature in education suggests the need for promoting abilities to cope with new socio-cultural challenges and adapt to changes in technology, and a datadriven society (Atenas et al., 2015; Coughlan, 2020; Wolff, Gooch, et al., 2016). Current experiments in Open Data Education involve students engaging with Open Data in several ways. Firstly, by directly using open datasets as resources for learning subjects such as geography, history or statistics in formal learning activities (Atenas et al., 2015; Coughlan, 2020). Secondly, by engaging in courses or curriculums where Open Data is the central topic of learning (Dermentzi et al., 2022; Palova & Vejacka, 2022). Thirdly, using Open Data to understand local issues and create solutions in informal learning environments associated with local governments (Davis & Shneyer, 2020). Differences between these three approaches are related to learning objectives and educational levels. 6.2. Methods A literature review was conducted to uncover students’ needs in the field of Open Data Education. According to Grant & Booth (2009), the literature review method seeks to identify what has been accomplished, allowing for consolidation and for gaps identification. Uncovering students; needs in current literature allows building on previous work to pursue the research goal of developing tools and methods enhancing the usage of Open Data in elementary schools. Considering the Open Data and Education domains, keywords such as Open Data, Open Datasets, School, Classroom , and Educational resource were defined to conduct iterative searches in the SCOPUS database. Searches with various combinations of keywords were followed by the screening of titles and abstracts to identify relevant studies, such as articles, conference papers, and book chapters in the Open Data Education field. According to the research question: What are the student’s needs for using Open Data? initial searches considered words such as “needs” and “users”, however, the D2.1 Open data user needs: seven flavours 24 results did not provide insight from a student’s perspective. Therefore, an abductive reasoning process was adopted to form hypotheses by discovering new concepts, ideas, or explanations (Rambaree, 2018), allowing for inferring students’ needs. 6.3. Results Students are non-expert data users. Therefore, their needs for successfully using Open Data in meaningful learning activities might be related to other non-specialist user groups, such as citizens. However, specific students’ needs consider learning objectives and educational levels. Students are people who engage in a formal learning activity. According to Beetham (2019), a learning activity involves other participants, such as teachers, a learning environment including tools, and learning outcomes. Allocating learners at the centre, the following figure provides an analysis framework for uncovering the students’ needs. Figure 1: Analysis framework based on Beetham’s Learning Activities Design Approach (Beetham, 2019) Needs related to the learning environment Learning environments might involve formal or informal settings and certain tools or artifacts which determine different level of interaction. • Interception of formal and informal learning environments: On the one hand, formal education is related to primary, elementary, high school, undergraduate programmes and masters and doctorates. Open Data Education is presented as part of the curriculum first, as a specific course, or as part of regular courses such as mathematics and geography, among others. On the other hand, informal education is framed in informal educational or learning environments such as libraries, profit or non-profit, grassroots, and public organisations, and research institutions. For example, Schools of Data initiatives are mainly embodied in courses for a broad public (Dander & Macgilchrist, 2022). There is a need to integrate different levels of formality. Formal/Informal education is related to local government or community-based initiatives aimed at increasing civic participation. Their implementation strategy focuses on students. • Concrete tools for students and educators: Students and educators face barriers such as the concept of Open Data being highly abstract and the need for customised hands-on open data collection, interpretation and exploitation activities (Atenas et al., 2015; Coughlan, 2020; Saddiqa et al., 2021a). Educators using Open Data as an educational resource have been facing problems such as finding the right datasets, processing data before giving it to students, and finding sources to ensure the openness of data (Coughlan, D2.1 Open data user needs: seven flavours 25 2020). The literature shows the development of experiments on tools for countering barriers and supporting students in using Open Data. Firstly, the authors focused on identifying open datasets for school subjects such as maths, science, and geography through mining techniques, interfaces and online communities (Chicaiza et al., 2017; Saddiqa et al., 2021b; Vallejo-Figueroa et al., 2018). Secondly, the authors presented experiments with tools facilitating the use of Open Data in the classroom for the collection of own local data and data visualisation (Badioze Zaman et al., 2021; Prodromou, 2017; Saddiqa, Kirikova, et al., 2019). Needs related to other participants Most learning involves interaction with other participants. These other participants interact with the learners according to different kinds of learning, such as associative, constructive, or situated. • Connecting to actors in an Open Data Ecosystem: Students need an Open Data ecosystem which can connect them with data experts, real-world organisations, and other actors. For example, an Open Data Ecosystem might support students to connect to data and problem owners, provide teachers with proper tools to lead learning activities and help school administrators to define guidelines (Radchenko & Sakoyan, 2014; Selwyn et al., 2017). Furthermore, (Saddiqa et al., 2021a) suggest the importance of creating a community of educators and students to share their own datasets, experience and tools. Needs related to learning outcome A learning outcome might be seen as an identifiable change in the learner. It might differ according to different kinds of learning. • Engaging as active citizens in an Open Data ecosystem: Current literature elaborates on the relevance of Open Data, not just in providing information about reality but in giving input for transforming it. For example, Saddiqa, Rasmussen, et al. (2019) experimented with using local Open Data in the classroom, firstly, to help students understand real facts and, secondly, to come up with ideas to improve their communities. In a competencybased education for active citizenship, students might adopt different roles from an ecosystem perspective, such as Open Data re-users or providers. However, they should be integrated. Needs related to the learning activity A learning activity determines the interaction between learners with other people, using certain tools and resources, oriented towards a specific outcome. • Developing skills and competencies for understanding and using Open Data: The appropriate skills should allow users not only to use, modify, and share available Open Data (Conradie & Choenni, 2014; Kassen, 2013; Prieto et al., 2012; Shadbolt et al., 2012) but also to understand what kind of perspectives it opens (Van Loenen et al., 2021). For example, Open Data users usually need to be familiar with dataset formats, statistics, text processing software, programming languages or interfaces (Ridgway, 2016b). These technical abilities are often associated with Data Literacy (Van Audenhove et al., 2020; Wolff, Cavero Montaner, et al., 2016). However, the openness of Open Data might need the consideration of skills and competencies, which some authors relate to 21st century skills (Romero et al., 2015). Furthermore, Saddiqa, Kirikova, et al. (2019) and Zapata & Santana (2015) elaborate on the importance of developing skills and competencies, such as the ability to understand local and global issues and critical and scientific thinking. • Engaging in meaningful learning experiences: Open Data was used to increase engagement and motivation among students by extending teaching outside the classroom. Activities such as engaging with local settings by collecting data D2.1 Open data user needs: seven flavours 32 Data literacy, digital literacy, and digital equity Different authors refer to barriers for users and potential users of open data as being the result of the lack of data literacy (Shibuya et al., 2022; Wilson & Chakraborty, 2019; Wilson & Cong, 2021; Zhang, 2022). Open government data, including spatial data, are used more by literate users, which often include governmental staff, businesses, and journalists (Shibuya et al., 2022; Zhang, 2022), whereas other users are left behind (Wilson & Cong, 2021). Indeed, not all users possess the same data literacy, and governments should invest in building data literacy and digital equity (Wilson & Cong, 2021). Digital equity captures what is needed for actors to participate in and through open government data and is composed of both the acquisition of the necessary skills (defined as digital literacy) and technologies (Wilson & Cong, 2021). In their investigation of the effects of open data on citizens’ behaviour during the Covid-19 outbreak, Shibuya et al. (2022) conclude that information sharing through open data did not have an equal impact on all citizens. Indeed, citizens with high digital literacy were more used to making decisions based on data even before the pandemic” (p. 6). The authors, therefore, suggest that open data research focuses on understanding the needs for information of all citizens. While open data presents opportunities, a more active role of different actors is invoked to fill the data literacy gap. Zhang (2022) focuses on equitable approaches to teaching spatial literacy, considered to promote the engagement of the public and as a key to people’s empowerment. Author also discusses the implication of teaching spatial data literacy in relation to themes focused on equity. By such an approach, it is possible to link geospatial data to relevant issues and reach the public's engagement. Wilson and Chakraborty (2019) investigate civic technology as a field that deploys open data with the aim of giving visibility to problems not addressed by governments through collaboration. While many factors are described as key to the success of civic technologies initiatives, digital literacy is reckoned as a challenge for untapping the potential of these forms of collaboration. Authors also put an emphasis on transitioning to a new form of “just” planning leveraging on open data. However, this transition requires additional data skills for planners to allow them to consider open data in their activities. Overall, the different contributions to digital literacy, data literacy, and digital equity call for addressing user needs through training and technology provided to different actors (e.g., citizens or planners) by different providers (i.e., governments or libraries as data intermediaries). Digital divide Access to data is uneven and is limited by digital divide (Ghose & Appel, 2016; Lee et al., 2023). Ghose & Appel (2016) research participation in geospatial data and the role of university libraries as viable options for intermediating access to open data for less privileged groups. Indeed, the digital divide prevents users and potential users from accessing the appropriate and relevant geospatial data. The digital divide is further exacerbated by imbalances in power that lead to the development of infrastructures, actors’ collaboration, and policies in a way that does not satisfy the needs of disadvantaged and marginalised groups (Ghose & Appel, 2016). The same groups cannot reap the benefits of open data as access to relevant datasets in the geospatial domain implies, in some cases, negotiating power and skills. Lee et al. (2023) focus on smart cities and present, among others, the case of the City of Portland, in which the local government took actions to bridge the digital divide that resulted from decades of marginalisation through actions targeted primarily at marginalised areas keeping as priority community engagement to solve the most pressing problems that characterise underserved areas The D2.1 Open data user needs: seven flavours 33 “open and people-first approach to digital transformation” (p. 88) was channelled through an Open Data Resolution specifically aimed at participation and reuse from data by citizens. According to Bezuidenhout et al. (2017), the digital divide is an obsolete concept to which we should prefer “digital inequality”. The focus of digital inequality is access intended as both a social and technological issue. With the adoption of a broader definition of the digital divide, it is possible to understand and solve the variety of reasons that prevent the reuse of data, and that includes a “complex mixture of social, psychological, economic and pragmatic reasons” (Selwyn 2004: 348, as cited by Bezuidenhout et al.). Data availability Schwoerer (2022) investigates citizens’ user needs in local governments and finds out that data availability is a major issue. The information needs of citizens are different from those of other actors, and, as already pointed out by previous studies (Ojo et al. 2018), open datasets concerning specific policy areas such as health and environment are considered more relevant. Local governments are not only providers of data but also “catalysts of “hidden” demand for open data” of different user groups. Issues regarding data availability are identified also by Jarke (2019). Approaches based on co-creation through open data should deal with different citizens’ information needs. Indeed, open data that citizens consider relevant cannot be available, and therefore, data collection and data creation should come first as priorities. To this end, collaborations with different data owners should be envisaged to meet the open data availability gap. Data divide Fusi et al. (2022) investigates open government data for environmental justice and argues that governments should be responsible for filling the data divide that prevents the participation of disadvantaged groups (in this case, vulnerable population) in policymaking. The unprivileged socioeconomic background of some groups of citizens limits the opportunities for accessing data and creates phenomena of data divide. Governments need to consider differences in access and treat it as an important equity issue. Also, the focus on “technical” features of data, such as “machine readability, quantity and granularity”, widens these gaps by giving a competitive advantage in the use of open data to actors who are already empowered and skilful. Data quality According to Fernández-Ardèvol & Rosales (2022), data use and reuse need to consider the potential biases of secondary datasets, intended as datasets that were created for different purposes. Data quality assessment, indeed, does not ensure that data are bias-free and, more specifically, that data adequately reflect the existence and the characteristics of different user groups, such as minorities or social groups on which data are not collected. The definition of data quality is “socially constructed” and, therefore, it is important to acknowledge and consider that open data that meet existing standards might not satisfy user needs. Also, the issue of data quality should be seen in connection with the lack of data skills. Users might lack critical approaches towards data and, therefore, they might not be able to recognize that data are not objective and that they can embed limitations. Legal, political, and organisational barriers for data access Overby et al. (2022) investigate legal, political, and organisational barriers to data accessibility. Analysing the case of a conservation easement and land records in the United States, the Authors find out that concerns over privacy, as well as political and organisational barriers, prevent wide access and participation of citizens in environmental governance through open data. Barriers to access prevent a “just” environmental governance, and it is suggested that governments strive for equitable data access to facilitate the participation of diverse groups and satisfy societal needs. D2.1 Open data user needs: seven flavours 34 Data governance Governance through new data principles might emphasise collective ownership, control, and selfdetermination, as well as the needs of marginalised groups. One leading example is the one of the CARE (Collective benefit, Authority to control, Responsibility, Ethics) principles, proposed as a remedy for the flaws of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Walter et al., 2021). Indeed, the FAIR principles, developed in the Western tradition, do not reflect, or consider the needs of disadvantaged groups. For this reason, the RDA International Indigenous Data Sovereignty Interest Group developed the CARE principles (Walter et al., 2021). These new data principles can be a source of inspiration for developing more inclusive data governance in other contexts, such as in environmental justice and open data governance, as suggested by Fusi et al. (2022). Uneven access across territories Access to data for users might be uneven across different territories. In practice, some local governments might share more data than others. Focusing on governmental user needs at the local level in relation to the implementation of the PSI Directive, Svärd (2018) contends that financial constraints impede the adoption of open data-sharing practices. As a result, the availability of data changes across municipalities, and this might result in inequities of access. For other authors (Zuiderwijk et al., 2018), while budget constraints might play a role, differences in open data uptake at the local level can also result from uneven motivations among municipalities for adopting them. Therefore, at the central/national level, policies need to consider differences that might arise in relation to the adoption of open data policies. 8.5. Conclusions In the review of the literature on open data and social equity, we identified a variety of user needs that are connected to the disadvantaged groups that encompass: 1) Data literacy, digital literacy, and digital equity, 2) Digital divide and Digital inequality, 3) Data availability, 4) Data Divide, 5) Data Quality, 6) Legal, political, and organisational barriers for data access, 7) Data Governance, 8) Uneven access across territories. The identified user needs do not always involve governments as users of open data but rather as mediators of the demand for open data (data availability), providers of training or funding for training (data literacy, digital literacy, and digital equity) or “guarantors” of data quality and absence of biases in data. Also, the focus of the literature is on local governments, with fewer contributions about regional and central governments. Overall, the literature on open data and social equity, while not systematically discussing implications for marginalised groups in relation to the use of open data, suggests different avenues for further research. D2.1 Open data user needs: seven flavours 35 9. Companies 9.1. Introduction Open Data (OD) refers to data that can be accessed and shared without restriction. OD is revolutionizing how businesses operate in the modern digital age. Many companies are adopting OD initiatives to stimulate innovation, enhance decision-making, and uncover latent market potential (Hammell et al., 2012). As organizations become increasingly data-driven, it is essential that they comprehend and effectively align their requirements. This section examines companies’ indispensable requirements for open data. We will consider both the benefits and obstacles associated with its implementation. To make informed decisions, companies require access to high-quality, reliable open data sources (Zuiderwijk et al., 2015). The significance of data quality cannot be overstated, as inaccurate information can result in wrong strategies and wasted resources. For instance, energy companies can use open data on solar radiation and wind patterns to identify optimal locations for renewable energy projects, ensuring reliable energy generation. On the other hand, waste management companies can use open data on population density, waste generation rates, and traffic patterns to optimize waste collection routes, thereby increasing efficiency and decreasing environmental impact. Accessibility and the seamless integration of diverse datasets are essential user requirements for businesses (Väyrynen et al., 2017). Companies require user-friendly (accessible) interfaces on open data platforms that enable them to search, filter, and download data with minimal effort. In addition, interoperability between diverse datasets is essential, as organizations frequently need to combine and analyze multiple data sources to derive insightful conclusions. OD on public transit routes, schedules, and ridership can help transportation agencies and companies optimize public transportation systems, improve service coverage, and enhance the commuter experience. Businesses have diverse and specific data requirements depending on their industry, market, and goals. Therefore, companies require OD platforms that offer customization and adaptability regarding data formats, frequency, and granularity. This customization allows companies to tailor the data to their specific requirements and gain insights related to their business area. For example, businesses in the energy sector can leverage open data on energy consumption patterns to identify high-demand areas, enabling them to develop targeted energy efficiency programs and optimize grid operations. The utilization of open data about traffic flow, road conditions, and accidents can be advantageous for transportation companies in implementing efficient traffic management tactics, optimizing signal timings, and mitigating congestion. OD can help companies in their decision-making processes. They can make educated judgments with the availability of comprehensive data, which lowers uncertainty and minimizes risks (Zuiderwijk et al., 2015). For example, transportation authorities can use open data on traffic flow, road conditions, and accidents to implement effective traffic management strategies, optimizing signal timings and reducing congestion. In addition, OD fosters innovation by providing businesses with insightful information that can disclose new market opportunities, trends, and growth areas. Energy companies can use open data on energy consumption patterns to identify high-demand areas, enabling them to develop targeted energy efficiency programs and optimize grid operations. Challenges associated with the adoption of OD should also be mentioned. The massive quantity of accessible data can be overwhelming for businesses, making it difficult to identify and prioritize the most pertinent information for their purposes. Companies can overcome this obstacle, however, by focusing on specific open data sources relevant to their initiatives. There may also be data quality concerns, as open data sources may contain errors or inconsistencies. Companies can mitigate this D2.1 Open data user needs: seven flavours 36 difficulty and ensure the veracity of the information they use by thoroughly evaluating and validating their data sources. 9.2. Method The present study employed a two-fold approach comprising a comprehensive literature review and a thorough analysis of practical applications. A comprehensive literature review was conducted to investigate the impact of OD on business operations. The task necessitated the examination of academic literature, technical documents, and analytical studies pertaining to open data endeavours. The literature was meticulously selected to guarantee its pertinence and reliability, furnishing a comprehensive outlook on the ramifications of open data in various sectors. After the literature review, we examined various scenarios that showcase the pragmatic application of OD in the process of making business decisions. The instances were carefully chosen from diverse sectors, such as energy, waste management, and transportation, to demonstrate a variety of situations. The purpose of these examples is to demonstrate how enterprises can effectively employ open data to fulfil their unique needs while highlighting potential challenges that may arise. Through the integration of these two stages, a comprehensive understanding of the open data business user requirements was achieved. The methodology employed in this study incorporated theoretical and practical perspectives derived from relevant literature and real-world applications. 9.3. Results Following the literature review and the applications analysis, the following needs were identified. 1. Access to high-quality and reliable OD sources: Companies need accurate and reliable data to make good choices and avoid wrong strategies. They need trustworthy sources of OD relevant to their business area. 2. Integration and accessibility of diverse datasets: Companies must have access to user-friendly interfaces, and datasets must be interoperable between them. They require access to open data platforms that facilitate data discovery, refinement, and download. By combining and analysing data from different sources, companies can gain important insights that would not otherwise be possible. 3. Customization and adaptability of data platforms: Different industries, markets, and business goals have different and unique data needs. They need OD tools that let them change the style, frequency, and level of detail of the data. This availability lets them change the info to fit their needs and learn more about the situation. 4. Availability of data: Companies can take better decisions when they have all the needed information. OD platforms should give companies access to a wide range of data sources to get the needed information. 5. Data quality assurance: Companies need to know that their OD sources are of good quality. They need ways to check and confirm the data sources to ensure they are correct, reliable, and consistent. 6. Overwhelming amount of data: There are vast amounts of data companies can access. They need ways to find and rank the most important information for their goals. This problem can be solved by focusing on specific OD sources that are useful for their projects. 7. Data quality: OD sources may have errors or be inconsistent. Concerns about data quality need to be dealt with by companies by carefully examining and confirming their data sources. Truthful data helps ensure the decision-making process. 8. Support: For companies to use OD effectively, they need help and direction. They need access to tools, expertise, and help, to maximize the benefits OD brings to the business. 9.4. Conclusions In conclusion, open data has the potential to significantly revolutionise how organisations conduct their daily operations whilst providing a range of benefits, including improved decision-making D2.1 Open data user needs: seven flavours 37 abilities, lower costs, and the potential to stimulate innovation. The effective implementation of open data initiatives requires careful consideration of various factors such as data quality, accessibility, customisation, security, and support requirements for enterprise users. It would, in turn, promote informed decision-making, enhance operational processes, and promote a sustainable and effective future for organisations. The utilisation of open data poses certain obstacles; nevertheless, businesses that effectively overcome these challenges can attain a competitive edge. D2.1 Open data user needs: seven flavours 38 10. Artificial Users 10.1. Introduction The open data ecosystem holds significant importance in the current digital age and serves as a crucial resource for a diverse range of users (Runeson et al., 2021). In recent times, there has been a notable surge in the significance of artificial users. In the context of open data, "artificial users" refers to entities that utilize open data for consumption, analysis, learning, and informed decision-making processes (Helm et al., 2020). The category of artificial users encompasses entities such as artificial intelligence (AI), machine learning, and bots (Nikitas et al., 2020). Artificial users are integral components of various contemporary systems, including but not limited to recommendation engines, search algorithms, and data analytics tools (Kosala & Blockeel, 2000). In our technology-centric society, they play a crucial role as the foundation of numerous routine functions, ranging from tailored suggestions on digital interfaces to anticipatory analysis in domains such as finance, healthcare, and transportation, among others. Despite the extensive research conducted to understand the requirements of human users in open data ecosystems, the specific needs of artificial users have not been given equal importance. The development of synthetic users necessitates a distinctive methodology owing to their diverse needs. Accurate identification and comprehensive understanding of particular requirements are essential for enhancing the efficiency of artificial users and maximizing their potential in open data ecosystems (Janssen et al., 2020). The subsequent segment will examine the needs of artificial users in the domain of open data. The objective of this research is to offer significant perspectives and direction to experts who are involved in the management of open data and artificial intelligence. A comprehensive evaluation of the prerequisites and their probable consequences on open data systems will be executed to attain this objective. The main aim is to facilitate the establishment of open data eco-systems customized to meet synthetic users' needs, thereby enhancing their effectiveness and influence on our data-driven society (Welle Donker & van Loenen, 2017). 10.2. Method We employed a rigorous research methodology to gain a comprehensive understanding of the requisites of synthetic users in the domain of open data. The initial methodology employed encompassed a thorough review of relevant literature and an examination of established practices within the domains of artificial intelligence and open data ecosystems. This enabled us to acquire a comprehensive perspective and identify shortcomings in the current understanding of the requirements of artificial users. During the second phase of our methodology, our primary objective was to develop and implement an analytical framework to assess the distinct requirements of synthetic users from diverse viewpoints. A thorough examination was carried out on diverse categories of synthetic users, encompassing both artificial intelligence (AI) systems and software bots. Furthermore, we assessed various scenarios in which these users engage with open data. Concurrently, we conducted a study which evaluated various state of the art algorithms such as BERT, RAKE, YAKE, TEXTRANK, and CHATGPT, with a focus on their performance comparison (Campos et al., 2020; Hu et al., 2018; Qian et al., 2021; Song et al., 2023). Furthermore, a new hybrid methodology referred to as BRYT was introduced and integrated into the comparative analysis. The methodology employed in this study focuses on the extraction of pertinent metadata from datasets to assist artificial users, as illustrated in Figure 2. The algorithms' effectiveness was assessed based on their ability to pull out important keywords from the dataset's descriptions. This is a key factor in improving D2.1 Open data user needs: seven flavours 39 data access and discoverability for automated users. The efficiency of the algorithms was evaluated based on their ability to extract important keywords from the dataset's descriptions. This is a key factor in improving accessibility and discoverability for automated users. Figure 2: BRYT Keyword Extraction 10.3. Results Our findings highlighted several important requirements for artificial users pertaining to open data. Firstly, our research stressed the need for reliable and consistent data for artificial users. This is because these organizations depend on precise, thorough, pertinent, and timely data to function. Inaccurate results may be produced if the data sources are faulty, or the data formats are inconsistent. Secondly, our study demonstrated how important it is for data to be accessible and discoverable in the open data ecosystem. Artificial users should be able to explore data systems with ease and find pertinent facts in a sea of information. Thirdly, we noted the need for standardization and compatibility. This calls for data standardization since it is necessary for data from many sources to be readily combined and used together. Furthermore, it is essential for artificial users to have access to real-time or frequently updated material given the quickly changing nature of information nowadays. We also emphasized the need of strong privacy and security safeguards and the significance of upholding ethical principles while using data. Lastly, the outcomes of our suggested technique, BRYT, in terms of our keyword extraction algorithms were promising. When it came to collecting representative keywords from the dataset's descriptions, it D2.1 Open data user needs: seven flavours 40 consistently outperformed other examined approaches. This is a crucial aspect of improving data findability. 10.4. Conclusions In conclusion, this research sheds vital light on the unique needs of artificial users in the context of open data, underlining the necessity to cater to these requirements for maximizing the effectiveness of these entities. The findings reveal that factors such as data quality, accessibility, interoperability, real-time updates, and ethical considerations are of paramount importance. Furthermore, our proposed methodology, BRYT, shows promising potential in enhancing data findability, a critical need for artificial users. This research, however, is just the beginning. As the field of AI continues to evolve, it's crucial to continue exploring the needs of artificial users and refine our strategies accordingly. The focus should be on creating open data ecosystems that are tailored to the needs of artificial users, thereby fostering a digital environment where artificial and human users can coexist and thrive. D2.1 Open data user needs: seven flavours 41 11. Open data intermediaries 11.1. Introduction There are socio-technical barriers to the meaningful use of open data, such as a lack of knowledge about the data, ambiguity surrounding data licenses, and a lack of the necessary software to process data. Open data intermediaries play an important role in addressing these challenges (Davies & Edwards, 2012). They are defined as “third-party actors who provide specialized resources and capabilities to (i) enhance the supply, flow, and/or use of open data and/or (ii) strengthen the relationships among various open data stakeholders” (Shaharudin et al., 2023). Understanding the needs of open data intermediaries is necessary to ensure that they can play their role more effectively and contribute to a sustainable open data ecosystem. 11.2. Method A systematic literature review was conducted to better understand who indeed open data intermediaries are by looking into their types of actors, tasks, and objectives (Shaharudin et al., 2023)2. From the types of actors, tasks, and objectives of open data intermediaries gathered, their needs were inductively identified. Additionally, the same literature pool in (Shaharudin et al., 2023) was utilised to capture challenges faced by open data intermediaries, which are also useful in the identification of the needs. To complement the findings from the literature, an interview with a representative of an open data intermediary, Esri Nederland, which is one of the partners of ODECO, was also conducted. 11.3. Results From the literature, various types of actors of open data intermediaries were identified (Table 8). While most of them are users of open data, some of them advocate for or facilitate access to open data. They are not necessarily organizations – some of them are individuals such as entrepreneurs, individual developers, and researchers. Table 8: Types of actors of open data intermediaries (Shaharudin et al., 2023) Type of actor Sources Civil society organizations (CSOs) (Mayer-Schönberger & Zappia, 2011), (Cañares, 2014), (González-Zapata & Heeks, 2015), (Brugger, Fraefel, Riedl, Fehr, Schöeneck, et al., 2016), (Germano et al., 2016) Entrepreneurs/ businesses (Cañares, 2014), (Janssen & Zuiderwijk, 2014), (Germano et al., 2016), (Andrason & van Schalkwyk, 2017), (Glassey, 2017) Media (Cañares, 2014), (Baack, 2015b), (Brugger, Fraefel, Riedl, Fehr, Schöeneck, et al., 2016), (Meng, 2016), (Johnson & Greene, 2017) Public organizations (Janssen & Zuiderwijk, 2014), (Chan et al., 2016), (Johnson & Greene, 2017), (Robinson & Mather, 2017), (Kim, 2018) Researchers (Meng, 2016), (Johnson & Greene, 2017b), (Park & Gil-Garcia, 2017), (Corbett et al., 2018), (Kim, 2018) Multi-partner (Hielkema & Hongisto, 2013), (Meijer & Potjer, 2018) Open data intermediaries do a wide range of tasks (Table 9) at various stages of the open data lifecycle, deploying various types (Shaharudin et al., 2023). Typically, multiple tasks are needed for them to serve their functions. Most of the tasks entail active processing of open data, such as collecting, augmenting, contextualizing, visualizing data, and developing products and services with open data. However, some tasks do not necessarily require them to actively process open data, for example, building data capacity, facilitating stakeholders’ interactions, and channelling feedback. 2This systematic literature review has been published as a research article in a peer-reviewed journal whose objective is to propose a common definition of open data intermediaries. D2.1 Open data user needs: seven flavours 48 Governance and coordination The literature on Governments suggests that governance through new data principles might emphasise collective ownership, control, and self-determination, as well as the needs of marginalised groups. One leading example is one of the CARE (Collective benefit, Authority to control, Responsibility, Ethics) principles, proposed as a remedy for the flaws of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. New data principles can be a source of inspiration for developing more inclusive data governance in other contexts, such as in environmental justice and open data governance. Furthermore, there is a need for coordination. Local governments, as implementers of open data initiatives, need trust and coordination mechanisms so that they can prevent distrust and competition between public and private actors. Communication Communication is referenced as a need with different meanings. NGOs need communication channels to connect with other actors, such as open data providers, users, and other intermediaries who can help satisfy their needs. For Journalists , communication is directed towards their audience (the public), and, as such, they need to be able to present complicated data in a form that can be easily understandable through visualization and, most importantly, storytelling. When local governments act as the initiators of open data initiatives, they need proper communication and coordination so that open data initiatives have the support and engagement of local businesses and communities and align standards, business rules, and architecture. Communication is paramount to enable data flow and drive innovation and value-creation. Even though categories of needs were identified as transversal to user groups, a deeper knowledge of different contexts of use can lead to more specific needs. For example, “the need for concrete tools for students and educators”, “the need for meaningful learning experiences” ( students ), “the need for connecting to Open Data Ecosystems ( students )”, “domain-specific knowledge support” and “domainspecific data standards”, as in the case of open data intermediaries . 12.2. Limitations It is important to review some limitations of the report. The most important limitation of the study lies in the fact that literature on open data do not always directly engage with the concept of user needs. Therefore, challenges faced by users are often derived from the tasks, impediments, barriers, and struggles faced by different user types in approaching open data. The report is also limited by the novelty of the field. While the number of studies on open data is exponentially growing, the literature is still fragmented, with research gaps concerning the different user types analysed in this report. Another limitation of the study is the lack of conceptual clarity in the literature. The roles of open data users are not always interpreted in the same way by the literature, and, therefore, what constitutes, for instance, an open data intermediary is not always clear due to a lack of common definitions. Despite its limitations, the report adds to our understanding of the needs of different types of users who have been neglected by previous research and contributes to setting out a research agenda to fill existing gaps in the literature. 12.3. Towards a research agenda Based on our analysis, we can delineate a research agenda. First, empirical research is needed to both corroborate the results of the literature review and provide new insights into the needs of open data users for which previous research is fragmented, scarce or unfocused, such as NGOs, non-specialist users, and journalists. Second, further research is also needed regarding different stages of the open data lifecycle, considering how the user’s needs emerge in a different way based on the multiple roles that different users experience (e.g., governments both sharing and using data). Finally, and most importantly, additional research is needed to understand how to address the user needs from both a technological and a governance perspective. As seen in the report, while some user needs are D2.1 Open data user needs: seven flavours 49 expected to be solved by technological solutions, such as improving findability, others are deeply intertwined with governance issues, as in the case of improving literacy, channelling funding, and setting new regulations. To this end, future ODECO studies (deliverable D2.2 and deliverable D2.3) will build on this report and focus on technological and governance measures to satisfy user needs. 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