To assess the effectiveness of the ‘Weet wat je eet’ (‘Know what you eat’) school-based nutrition education programme on behavioural determinants and behaviour among students aged 12–15 years. A quasi-experimental study design was used, collecting data at baseline and after implementing the programme in both an intervention and control group (in total 611 students) across the Netherlands. Students from eighteen Dutch secondary education schools completed two consecutive questionnaires, assessing knowledge, self-efficacy, attitude, subjective norm, intention, and behaviours related to healthy, safe, and sustainable nutrition. Multilevel regression analyses were conducted corrected for gender, grade, education level, and school location. The intervention group showed a significant higher increase in self-efficacy, attitude, intention to drink water (all three P < 0.01), and a significant higher decrease in the consumption of sugary drinks, snacks, and meat (all P < 0.05) than the control group. Both the groups scored significantly higher on knowledge during the post-test (both P < 0.05), although the intervention group not significantly higher than the control group (P = 0.14). No significant effects were observed for subjective norm, intention, and fruit, vegetable, and whole grain bread consumption. The results of this study showed positive effects of the ‘Weet wat je eet’ school-based nutrition education programme on self-efficacy and attitude towards healthy, safe and sustainable nutrition, intention to drink more water, and various healthy eating behaviours among secondary school students. Further research is necessary to assess the long-term sustainability of these results.

This analysis provides a critical account of AI governance in the modern “smart city” through a feminist lens. Evaluating the case of Sidewalk Labs’ Quayside project—a smart city development that was to be implemented in Toronto, Canada—it is argued that public–private partnerships can create harmful impacts when corporate actors seek to establish new “rules of the game” regarding data regulation. While the Quayside project was eventually abandoned in 2020, it demonstrates key observations for the state of urban algorithmic governance both within Canada and internationally. Articulating the need for a revitalised and participatory smart city governance programme prioritizes meaningful engagement in the forms of transparency and accountability measures. Taking a feminist lens, it argues for a two-pronged approach to governance: integrating collective engagement from the outset in the design process and ensuring the civilian data protection through a robust yet localized rights-based privacy regulation strategy. Engaging with feminist theories of intersectionality in relation to technology and data collection, this framework articulates the need to understand the broader histories of social marginalization when implementing governance strategies regarding artificial intelligence in cities.

In our digitalized modern society where cyber-physical systems and internet-of-things (IoT) devices are increasingly commonplace, it is paramount that we are able to assure the cybersecurity of the systems that we rely on. As a fundamental policy, we join the advocates of multilayered cybersecurity measures, where resilience is built into IoT systems by relying on multiple defensive techniques. While existing legislation such as the General Data Protection Regulation (GDPR) also takes this stance, the technical implementation of these measures is left open. This invites research into the landscape of multilayered defensive measures, and within this problem space, we focus on two defensive measures: obfuscation and diversification. In this study, through a literature review, we situate these measures within the broader IoT cybersecurity landscape and show how they operate with other security measures built on the network and within IoT devices themselves. Our findings highlight that obfuscation and diversification show promise in contributing to a cost-effective robust cybersecurity ecosystem in today’s diverse cyber threat landscape.

Usage data on research outputs such as books and journals is well established in the scholarly community. Yet, as research impact is derived from a broader set of scholarly outputs, such as data, code, and multimedia, more holistic usage and impact metrics could inform national innovation and research policy. While usage data reporting standards, such as Project COUNTER, provide the basis for shared statistics reporting practice, mandated access to publicly funded research has increased the demand for impact metrics and analytics. In this context, stakeholders are exploring how to scaffold and strengthen shared infrastructure to better support the trusted, multistakeholder exchange of usage data across a variety of outputs. In April 2023, a workshop on Exploring National Infrastructure for Public Access and Impact Reporting supported by the United States (US) National Science Foundation (NSF) explored these issues. This paper contextualizes the resources shared and recommendations generated in the workshop.

The promised merits of data-driven innovation in general and algorithmic systems in particular hardly need enumeration. However, as decision-making tasks are increasingly delegated to algorithmic systems, this raises questions about accountability. These pressing questions of algorithmic accountability, particularly with regard to data-driven innovation in the public sector, deserve ample scholarly attention. Therefore, this paper brings together perspectives from governance studies and critical algorithm studies to assess how algorithmic accountability succeeds or falls short in practice and analyses the Dutch System Risk Indication (SyRI) as an empirical case. Dissecting a concrete case teases out to which degree archetypical accountability practices and processes function in relation to algorithmic decision-making processes, and which new questions concerning algorithmic accountability emerge therein. The case is approached through the analysis of “scavenged” material. It was found that while these archetypical accountability processes and practices can be incredibly productive in dealing with algorithmic systems they are simultaneously at risk. The current accountability configurations hinge predominantly on the ex ante sensitivity and responsiveness of the political fora. When these prove insufficient, mitigation in medias res/ex post is very difficult for other actants. In part, this is not a new phenomenon, but it is amplified in relation to algorithmic systems. Different fora ask different kinds of medium-specific questions to the actor, from different perspectives with varying power relations. These algorithm-specific considerations relate to the decision-making around an algorithmic system, their functionality, and their deployment. Strengthening ex ante political accountability fora to these algorithm-specific considerations could help mitigate this.

Financial inclusion depends on providing adjusted services for citizens with disclosed vulnerabilities. At the same time, the financial industry needs to adhere to a strict regulatory framework, which is often in conflict with the desire for inclusive, adaptive, and privacy-preserving services. In this article we study how this tension impacts the deployment of privacy-sensitive technologies aimed at financial inclusion. We conduct a qualitative study with banking experts to understand their perspectives on service development for financial inclusion. We build and demonstrate a prototype solution based on open source decentralized identifiers and verifiable credentials software and report on feedback from the banking experts on this system. The technology is promising thanks to its selective disclosure of vulnerabilities to the full control of the individual. This supports GDPR requirements, but at the same time, there is a clear tension between introducing these technologies and fulfilling other regulatory requirements, particularly with respect to “Know Your Customer.” We consider the policy implications stemming from these tensions and provide guidelines for the further design of related technologies.

Open innovation programmes related to data and artificial intelligence have interested European policy-makers as a means of supporting startups and small and medium-sized enterprises to succeed in the digital economy. We discuss the objectives behind the typical service offerings of such programmes and propose a case for exploring how they align with the motivations of individual companies who are targeted by these initiatives. Using a qualitative analysis of 50 startup applications from the Data Market Services Accelerator programme, we find that applicants wrote most frequently about fundraising, acceleration and data skills. A smaller number of startups expressed interest in services related to standardization or legal guidance on General Data Protection Regulation and intellectual property rights, which are some of the ongoing priority areas for the European Commission. We discuss how the value propositions of these less desired offerings can be amplified by appealing the existing business motivations of data-driven startups.

Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.

In our data-driven society, personal data affecting individuals as data subjects are increasingly being collected and processed by sizeable and international companies. While data protection laws and privacy technologies attempt to limit the impact of data breaches and privacy scandals, they rely on individuals having a detailed understanding of the available recourse, resulting in the responsibilization of data protection. Existing data stewardship frameworks incorporate data-protection-by-design principles but may not include data subjects in the data protection process itself, relying on supplementary legal doctrines to better enforce data protection regulations. To better protect individual autonomy over personal data, this paper proposes a data protection-focused data commons to encourage co-creation of data protection solutions and rebalance power between data subjects and data controllers. We conduct interviews with commons experts to identify the institutional barriers to creating a commons and challenges of incorporating data protection principles into a commons, encouraging participatory innovation in data governance. We find that working with stakeholders of different backgrounds can support a commons’ implementation by openly recognizing data protection limitations in laws, technologies, and policies when applied independently. We propose requirements for deploying a data protection-focused data commons by applying our findings and data protection principles such as purpose limitation and exercising data subject rights to the Institutional Analysis and Development (IAD) framework. Finally, we map the IAD framework into a commons checklist for policy-makers to accommodate co-creation and participation for all stakeholders, balancing the data protection of data subjects with opportunities for seeking value from personal data.

With the constant flow of data across jurisdictions, issues regarding conflicting laws and the protection of rights arise. This article considers the EU–US data transfer relationship in the aftermath of the decision in Data Protection Commissioner v Facebook Ireland and Maximillian Schrems where the Court of Justice of the European Union (CJEU) invalidated an EU–US data transfer agreement for the second time in just five years. This judgment continues the line of cases emphasising the high value the Court places on securing EU personal data in accordance with EU data protection standards and fundamental rights. This article assesses the implications of the ruling for the vulnerable EU–US data transfer relationship.

Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Data trusts have been conceived as a mechanism to enable the sharing of data across entities where other formats, such as open data or commercial agreements, are not appropriate, and make data sharing both easier and more scalable. By our definition, a data trust is a legal, technical, and organizational structure for enabling the sharing of data for a variety of purposes. The concept of the “data trust” requires further disambiguation from other facilitating structures such as data collaboratives. Irrespective of the terminology used, attempting to create trust in order to facilitate data sharing, and create benefit to individuals, groups of individuals, or society at large, requires at a minimum a process-based mechanism, that is, a workflow that should have a trustworthiness-by-design approach at its core. Data protection by design should be a key component of such an approach.

This paper explains how the concept of personal data should be delimited. Certainty on this matter is crucial, as it determines the material scope of the data protection obligations. The primary boundary delimiting the scope of personal data is the requirement that personal data ‘relate to’ an individual. The courts of the UK and the EU have sought to delineate this boundary, but there are serious difficulties in the present approaches that have emerged thus far. Two possible ways forward are suggested, taking into account the implications of the direct application of the GDPR in the UK.

In this article Susan Doe reports from the perspective of the law firm sector on the progress towards the introduction of the General Data Protection Regulation that became automatically ‘live’ on 25 May 2018. She provides an introduction to the Regulation, highlights some practicalities for law firms when considering compliance with GDPR and offers a ‘to do’ list with reference to the record of data processing, training needs, security, and contracts and documentation. She also provides advice on what should be considered especially in respect of client demands.

Against a backdrop of increasing data security and privacy concerns, current data protection law will soon be overhauled by the General Data Protection Regulation (GDPR). Previous research has indicated a lack of data protection management in libraries, however, it has been nine years since the latest study. This article by Josephine Bailey aims to provide an updated review of the extent of data protection management in UK library and information services and gauge preparation for the incoming GDPR.

This article, written by Sahar Bhaimia, presents an overview of the General Data Protection Regulation (EU) (2016/679) (GDPR) which will apply automatically across the EU on 25 May 2018. The GDPR is an update and reform of existing EU data protection law, first established by the Data Protection Directive (1995/46/EC). The article is for knowledge managers and information services professionals who may be asked to take on responsibility for GDPR, and focuses on the UK. It covers the fundamentals of EU data protection law, highlights key changes brought about by the GDPR, and provides practical tips and suggestions for knowledge managers.