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.

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.

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.

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.

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.

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.