While the public sector traditionally lags behind business in innovation, significant changes are anticipated with the broad diffusion of so-called disruptive technologies. The use of such technologies in public service, along with possible benefits, need to be well researched, and challenges be carefully discussed, analysed and evaluated. This paper applies scenario-based science and technology roadmapping to identify research and training needs in the implementation of disruptive technologies in public service. 70 experts reviewed 13 future scenarios and derived a number of research and training needs regarding internet of things, artificial intelligence, virtual and augmented reality, big data and other disruptive technologies. The identified needs serve as a starting point for a broader and more informed discussion about how such new (disruptive) technologies can be successfully deployed in the public sector - leveraging the benefits of these technologies while at the same time constraining the drawbacks affiliated with them.

A unified framework outlining the procedures and methods necessary to adhere to data management standards is essential for effective Data Governance. Data Governance Frameworks serve as a guide to secure and trustworthy data exchange between different (legal) entities. Their specific implications differ based on the field they are incorporated in. Thus, to extract their full potential, they need to be suitably designed. However, there is a lack of knowledge in the literature concerning the assets and components of Data Governance Frameworks tailored for data intermediaries, including Data Trusts. Moreover, there is a limited understanding of what crucial components the frameworks consist of and how they can be managed to maximize frameworks’ performance. The level of effectiveness of such frameworks needs to be measured by analyzing the performance of certain assets. These relations between the components and the assets are yet to be thoroughly explored and precisely defined. Establishing a strong link between the components and assets gives an overview to practitioners on how the intended assets can be fulfilled properly with the corresponding component. Therefore, practitioners can tackle the respective management components to achieve the intended trustworthiness in the data trust ecosystem. This study conducts a systematic analysis of existing Data Governance Frameworks, identifying the crucial assets as well as components, and provides a mapping between them. The findings set a base that enables the development of a comprehensive Data Governance Framework tailored specifically for Data Trusts.

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.