Open Science programs are structured educational, research, and technical projects that support the application of Open Science practices in universities. Despite growing interest in monitoring Open Science practices, two key gaps remain: limited research on how the organizational design of Open Science is implemented in universities, and an overemphasis on output-based indicators that overlook processual and organizational aspects. To address these gaps, this study develops a responsive framework that universities can adapt to monitor the progress and ‘health’ of their Open Science programs. The framework, developed through a case study involving eleven semi-structured interviews and three participant observations, was rigorously validated during two focus groups involving thirteen Open Science experts. We describe the study’s results using the responsive evaluation method, which is suitable in complex social systems and is recognized as a learning process by the evaluation’s stakeholders. The proposed framework comprises seven key themes: Open Science as an ecosystem, Epistemic Cultures, Governance, Technical Infrastructure, Funding, Open Science as a Process, and Reflexivity. The developed framework advances theory by framing Open Science as an institutional practice embedded in organizational structures, and it supports practice by providing universities and policymakers with a more comprehensive tool for assessing and managing Open Science initiatives going beyond output-based indicators.

Research on the potential use of ChatGPT for anonymizing texts in government organizations is scarce. This study examines the possibilities, risks, and ethical implications for government organizations to use ChatGPT in the anonymization of personal data in text documents. It adopts a case-study research approach, including informal conversations, formal interviews, literature review, document analysis, and experiments. The experiments using three types of texts demonstrate ChatGPT’s proficiency in anonymizing diverse textual content. Furthermore, the study provides an overview of significant risks and ethical considerations pertinent to ChatGPT’s use for text anonymization within government organizations, related to themes such as privacy, responsibility, transparency, bias, human intervention, and sustainability. The current form of ChatGPT stores and forwards inputs to OpenAI and potentially other parties, posing an unacceptable risk when anonymizing texts containing personal data. We discuss several potential solutions to address these risks and ethical issues. This study contributes to the scarce scientific literature on the potential value of employing ChatGPT for text anonymization in government settings. It also offers practical insights for civil servants coping with the challenges of personal data anonymization, emphasizing the need for the cautious consideration of risks and ethical implications in the integration of AI technologies.

Machine learning algorithms show promise in assisting clinical decision-making; however, only a few have been successfully implemented in practice. To bridge this gap, it is essential to analyse the clinicians’ perspective on the compatibility of Artificial Intelligence-based clinical decision support systems (AI-CDSSs) with their clinical tasks. We therefore conducted a literature review of 21 empirical qualitative studies that examined the interaction between health professionals and AI-CDSSs. We synthesised the research through the lens of the Task-Technology Fit (TTF) model, analysing task, technology and individual characteristics of AI-CDSS applications, to identify design elements that are (mis)aligned with clinicians’ needs. Three key findings emerged from our analysis. First, clinicians often expressed scepticism about the clinical judgements of AI-CDSSs, particularly questioning the system’s ability to compete with clinical expertise in the absence of contextual information. Users valued AI primarily for specific strengths, such as identifying trends in patient trajectories, consolidating large datasets and pattern recognition, and comparing similar patient cases, but were hesitant to rely on it for clinical decisions. Second, actionability emerged as a desired characteristic of AI-CDSSs. For instance, clinicians particularly appreciated features of AI-CDSSs that enabled them to explore how different clinical actions might influence outcomes, as well as Explainable AI for identifying modifiable variables that impacted prediction scores, allowing them to take informed action. Third, we identified various ways AI-CDSSs could be used in clinical practice, including for patient prioritisation, patient monitoring, care acceleration, risk communication and workflow efficiency. In essence, AI-CDSSs functioned either as an alert system, preventing oversights, or as a tool for more informed decision making. Our analysis challenges the assumption that AI-CDSSs add little value when clinicians disregard its predictions, as it frequently prompts them to critically reassess their judgments through additional testing, consultation with colleagues, and other actions. Overall, our findings underscore the importance of an in-depth understanding of how AI-CDSSs are used in clinical practice. To optimise for effectiveness, the design of AI-CDSSs should prioritise supporting clinicians’ cognitive processes and information needs. This approach ensures that we move beyond the hype, focusing on the responsible integration of AI-CDSSs, and ultimately enhancing patient care.

While numerous platforms have been developed to support climate action, structured evaluations of their design remain limited. This paper presents a novel assessment framework for evaluating climate change mitigation and adaptation policy platforms. The framework includes 43 criteria that are structured around nine design requirement categories, including transparency, ease of use, interactivity, and accessibility. It is applied to ten policy platforms developed under the EU Horizon 2020 programme. Results show that while most of the examined platforms perform strongly in transparency, communication of complex information, and education, they consistently underperform in areas such as active maintenance, security, and accessibility. These findings highlight key areas for improvement by platform developers and funders. In parallel, they demonstrate the framework's flexibility and value as both an evaluation tool and a design guide for future platforms.

This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what factors are expected based on systems theory but are missing. Based on the hypothesis that ML systems are best addressed with a systems theory lens, we argue that the missing factors deserve greater attention in ongoing efforts to address ML systems safety. These factors include the explication of safety goals and constraints, the inclusion of systemic factors in system design, the development of safety control structures, and the tendency of ML systems to migrate towards higher risk. Our observations support the hypothesis that ML systems can be best regarded through a systems theory lens. Therefore, we conclude that system safety concepts can be useful aids for policymakers who aim to improve ML system safety.

There is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems. The aim of the study is twofold: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems. Moreover, this study develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations that differ in terms of (a) components and relationships, (b) stakeholders, (c) actors and their roles, (d) data types, (e) processes and activities, and (f) data lifecycle phases. Finally, three avenues for a future research agenda are proposed. This study is relevant for practitioners suggesting what elements of public data ecosystems have the most potential to generate value and should thus be part of public data ecosystems. As a scientific contribution, this study integrates conceptual knowledge about the elements of public data ecosystems, the evolution of these ecosystems, defines a future research agenda, and thereby moves towards defining public data ecosystems of the new generation.

The global initiative supporting open government data (OGD) has witnessed significant strides in the last decade. This study delves into the prospective integration of Artificial Intelligence (AI) with Hippolyta, a framework meticulously crafted to amplify the interpretability of government data. The aim is to scrutinize the viability of this integration, conducting a technical investigation in the realms of open government data and artificial intelligence. In contributing to the expansive field of OGD, this research focuses on elucidating the interpretability of data originating from governmental sources. Through an exploration of the technical feasibility surrounding the fusion of AI with Hippolyta, we aim to pave the path for advancements, fostering heightened interpretability and overarching enhancements in the understanding of government data.

To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.

Despite an increasing interest in the strategies to promote open data use in recent years, there has been a substantial lack of empirical and theoretical analysis of the governance modes that favored different types of open data initiatives. To address this gap, this study asks: How do governance modes support open data sharing in open government data platforms? To answer this question, we assess the coherence of the open data governance contexts of France and Ireland when sharing data on open government data platforms during the Covid-19 crisis. The study uses a multi-method approach involving both interviews with experts, identified through purposive sampling, and secondary sources for triangulation purposes. Overall, the governance context supported open data sharing in France and Ireland. Both cases are characterized by a strong central coordination with a solid trust relationship and clear legal frameworks. France, more than Ireland, relied on a market governance mode, and Ireland scored higher in networked governance due to the creation of social capital. The results provide new insights on how to combine governance modes that support open government data initiatives through coordination, collaboration with the private sector, and involvement of different actors. Practitioners can use our insights as examples of governance strategies that are fit for events that need a timely open data response.

In the data economy, data sovereignty is often conceptualized as data providers’ ability to control their shared data. While control is essential, the current literature overlooks how this facet interrelates with other sovereignty facets and contextual conditions. Drawing from social contract theory and insights from 31 expert interviews, we propose a data sovereignty conceptual framework encompassing protection, participation, and provision facets. The protection facets establish data sharing foundations by emphasizing baseline rights, such as data ownership. Building on this foundation, the participation facet, through responsibility divisions, steers the provision facets. Provision comprises facets such as control, security, and compliance mechanisms, thus ensuring that foundational rights are preserved during and after data sharing. Contextual conditions (data type, organizational size, and business data sharing setting) determine the level of difficulty in realizing sovereignty facets. For instance, if personal data is shared, privacy becomes a relevant protection facet, leading to challenges of ownership between data providers and data subjects, compliance demands, and control enforcement. Our novel conceptualization paves the way for coherent and comprehensive theory development concerning data sovereignty as a complex, multi-faceted construct.

Open government data initiatives have been rising quickly in recent times. They are encouraged by a wish to democratize data access and knowledge production and enhance cities socially and economically. The hardship of interpreting data can be considered an obstacle to using open government data and more prominent citizen engagement. Technology is crucial to enhance data interpretability and the practical construction of an open government. Nevertheless, the literature needed an instrument to support open government data's interpretability. In this work, our primary goal is to present the definition, implementation, and evaluation of a framework named Hippolyta, which is qualified to help citizens to interpret open government data. Hippolyta first identifies the citizen's necessities using a semantic enrichment module. After this step, the framework conducts the data collection through the same data retrieval module. Finally, Hippolyta creates a graphic visualization through a data visualization module. This study is relevant since it furnishes comprehensive insights into what the open data interpretability concept is composed of and which framework modules can sustain open data interpretation.

The complex societal problems that we face today require unprecedented collaboration and evidence-based decisions. These collaboration processes are further propelled by the datafication of virtually all spheres of public life. To benefit from this, the data needs to be made available to allow for data analytics. Thus, data sharing becomes a crucial aspect of cross-sector collaborations that aim to create and capture value from information. Compared to collaborations where data sharing is not the main goal, data sharing partnerships face a number of novel challenges, such as mitigating data risks, complying with data protection legislation, and ensuring responsible data use. Navigating these waters and achieving data sharing can be challenging for both governments and businesses, as well as other actors. How do organizations from different sectors manage to achieve data sharing for addressing societal challenges? To address this research question, we apply a framework of three models of cross sector social partnerships developed in the field of organization studies to structure the analysis of six cases. Our analysis suggests that to a certain extent the partnership model determines the types of drivers and challenges to sharing data in a partnership. Leveraging the drivers and anticipating these challenges can help organizations be more aware of key terms of the collaboration and the mechanisms that can be used to succeed in their partnership goals.

The landscape of platform ecosystems is becoming increasingly complex, with new types of platforms emerging that glue together otherwise fragmented ecosystems. One recent case is metaplatforms that can contribute to the European Data Economy by interconnecting data marketplaces; however, meta-platforms may intensify data sovereignty concerns: the inability of data providers to own and control the exchanged data. While smart contracts and certification can generally enhance data sovereignty, it is unknown whether data providers perceive these control mechanisms as valuable in the complex meta-platform setting. This study aims to evaluate the perceived efficacy of the control mechanisms to ensure data sovereignty in meta-platforms. The findings from a survey study (n=93) indicate that respondents perceive high data sovereignty. One potential explanation is that smart contracts can potentially enable providers to maintain ownership and control over their exchanged data; meanwhile, certification may signal metaplatforms’ responsibility to deliver secure data exchange infrastructure and assist providers in adhering to relevant regulations. This study contributes to advancing design knowledge for meta-platforms, showcasing that meta-platforms can be designed in a way to resolve fragmentation without neglecting data sovereignty principles.

Investigating meta-platforms has been a continuing concern within information system literature due to the increasingly complex constellations of platforms in ecologies of ecosystems. A meta-platform is a platform built on top of two or more platforms, hence connecting their respective ecosystems. One promising case to benefit from meta-platforms is data marketplaces: a particular type of platform that facilitates responsible (personal and non-personal) data sharing among companies. Given that business models for meta-platforms are largely unexplored in this emerging case, how they can create value for data marketplaces remain speculative. As a starting point toward business model investigations, this paper explores value creation of a meta-platform in the case of data marketplaces. We interviewed fourteen data-sharing consultants and six meta-platform experts. We identify three potential value creation archetypes of a meta-platform. The discovery aggregator archetype emphasizes searching and dispatching value, while the brokerage one focuses on promoting and supporting value. Finally, the one-stop-shop archetype creates value by standardizing, regulating, sharing, and experimenting. This study is among the first that explore value creation archetypes for a meta-platform, thus identifying core value as a base for further business model investigations.

Meta-platforms have received considerable Information Systems scholarly attention in recent years. Meta-platforms enable platform-to-platform openness and are especially beneficial to amplifying network effects in highly-specialized markets. A promising emerging context for applying meta-platforms is data marketplaces—a special type of digital platform designed for business data sharing that is vastly fragmented. However, data providers have sovereignty concerns: the risk of losing control over the data that they share through meta-platforms. This research aims to explore antecedents and consequences of data sovereignty concerns in meta-platforms for data marketplaces. Based on interviews with fifteen potential data providers and five data marketplace experts, we identify data sovereignty antecedents, such as (potentially) less trustworthy data marketplace participants, unclear use cases, and data provenance difficulties. Data sovereignty concerns have many consequences, including knowledge spillovers to competitors and reputational damage. This study is among the first that empirically develops a pre-conceptualization for data sovereignty in this novel context, thus laying the groundwork for designing future data marketplace meta-platform solutions.

Open Government Data (OGD) is a fundamental source for sustainability-oriented and data-driven innovation by citizens, companies, and other actors. However, many government agencies are reluctant to openly share their data with the public. While the resistance of public organizations to openly share government data has been investigated in previous research, most of these studies are focused on the reuse of open government data by companies and citizens. There is a paucity of research applying theoretical models to study the provision of OGD, and more specifically, the resistance of public organizations to make government data publicly available. We argue that Innovation Resistance Theory (IRT), which considers both functional and psychological factors, can be used to study OGD barriers, where OGD is seen as a source of innovation. This study aims to develop an OGD-adapted IRT model to empirically identify predictors affecting public agencies' resistance to openly sharing government data. Based on a review of the literature on both IRT research and barriers associated with open data sharing by public agencies, we develop an initial version of the model. In our future research, we plan to conduct exploratory interviews in multiple countries to refine the model. Ultimately, we will validate the refined model to study the resistance of public authorities to openly sharing government data in a quantitative study.

Non-profit organisations (NPOs) are one type of open data intermediaries connecting different actors in the open data ecosystem. They perform a number of activities, from requesting the government to open up the data to application development. Such activities can have an effect on open data usability barriers that other actors in the open data ecosystem encounter. The objective of this study is to systematically review the literature on the influence of NPOs' activities on the usability barriers for open data users in the open data ecosystem. The authors identified and analysed fourteen relevant papers. This study shows that NPOs conduct various activities that relate to different intermediary roles in the open data ecosystem, which in turn can affect certain usability barriers. Moreover, NPOs may perform different activities depending on the type of open data they work with. However, the connection between the activities and open data usability barriers for open data users cannot be clearly established from the selected articles, as most of them do not focus on establishing such a link. This review highlights a literature gap in relation to NPOs' activities and their effects on open data usability.