Indonesia’s rapid digital transformation generates massive economic opportunities while posing new regulatory challenges. This study examines how Indonesia’s digital economy is governed through key regulatory domains: electronic commerce, data governance, and digital taxation, and assesses their impact on inclusive growth. The research utilizes an integrated theoretical lens, Institutional Theory and Digital Divide Theory, to analyze how formal rules and on-the-ground access to technology together shape outcomes. The research employs a multimethod approach, to understand how different regulatory models influence market participation, business compliance, and digital inclusion. The findings reveal significant regional disparities in internet access and digital business adoption, with rural and remote areas lagging behind urban centers. Micro, small, and medium-sized enterprises face disproportionate compliance burdens under current regulations, a gap partly addressed through cooperative initiatives like the Indonesia–Japan Track 1.5 Public–Private Partnership. A key insight is the complex impact of data localization policies: while strengthening digital sovereignty and user privacy, strict localization requirements can raise operational costs and deter foreign investment. The paper concludes with policy recommendations for improving regulatory implementation, expanding digital infrastructure and literacy, and fostering international cooperation to ensure Indonesia’s digital economy regulations promote equitable growth across its diverse archipelago.

Twin registries worldwide increasingly function as large-scale research infrastructures, enabling standardized phenotyping across the lifespan, integration of biological and environmental data streams, and international cross-cohort collaborative research and replications. This development is also taking place in Germany. The GERman Twin Registry Under Development (GERTRUD; www.gertrud.info) was established in 2022 as the first nationwide research platform for recruiting twins and higher order multiples of all ages within Germany to support the large-scale genetically informative psychological, sociological, health, and neuroscience twin research at national and international levels. GERTRUD is being developed as a modular infrastructure that supports classical and extended twin family designs, combining annual core survey waves with optional embedded modules for intensive phenotyping (e.g., neuroimaging, smartphone-based assessments), biosampling, and linkage of participants’ residential context to external geographic datasets via geospatial information systems (GIS). To operate within Germany’s stringent data protection landscape, GERTRUD implements project-specific pseudonymisation, role-based access control, and contract-governed remote analysis access. This article describes GERTRUD’s governance and legal–technical framework, its multisource data architecture, and the potential for collaboration across Germany and internationally. Examples of early data implementations further illustrate that the continuously collected multimodal twin data constitute a critical asset, essential for successful harmonization, replication, and collaborative and integrative behavioral genetics research.

Public health data modernization in the United States has accelerated since COVID-19 exposed systemic weaknesses in fragmented data infrastructure and governance. Technical solutions have advanced, but legal and relational barriers still complicate data sharing across jurisdictions. Traditionally, interjurisdictional data sharing has relied on individually negotiated Data Use Agreements (DUAs), a process that is both resource-heavy and often opaque. To address this, the Centers for Disease Control and Prevention have proposed a Core DUA to standardize terms and reduce administrative burden. However, its success depends on trust — a fragile foundation increasingly strained by politicization, perceived lack of transparency, and controversial federal actions involving sensitive data. Jurisdictional concerns about compliance, security, and misuse underscore the need for governance frameworks that prioritize clarity, reciprocity, and accountability. Coercive approaches risk deepening fragmentation and undermining collaborative governance. Ultimately, modernization efforts will fail without supporting trust as the cornerstone of public health data governance. This article examines legal variation, transactional friction, and evolving jurisdictional perspectives to illuminate the critical role of trust in shaping the future of public health data systems.

As data are becoming increasingly important resources for municipal administrations in the context of urban development, formalization of urban data governance (DG) is considered a prerequisite to systematic municipal data practice for the common good. Unlike for larger cities, it is unclear how common such formalized DG is in rural districts and small towns. We therefore mapped the current status quo in small municipalities in Germany as a case exemplifying the broader phenomenon. We systematically searched online for policy documents on DG in all metropolitan regions, all rural districts, and a quota sample of nearly a sixth of all German small towns. We then performed content analysis of the identified documents along predefined categories of urban development. Results show that hardly any small towns dispose of relevant policy documents. Rural districts are somewhat more active in formally defining DG. Identified policy documents tend to address mostly economic activities, social infrastructure, and demography, whereas Housing and Urban design and public space are among the least mentioned categories of urban development.

The ongoing development of a Swiss Health Data Space (SHDS) presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. The successful implementation of the SHDS depends on its trustworthiness, as public trust is closely linked to public participation in data-sharing initiatives. We conducted four focus groups across the German-, French-, and Italian-speaking regions of Switzerland to identify public expectations and requirements related to the attributes that define a trustworthy SHDS. The participants discussed four fictitious case studies on: (1) consent management; (2) record linkage via the national social security number; (3) national data coordination center; and (4) cross-border data exchange. To best inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication recommendations. Policy recommendations are proposed based on insights from the fictitious case studies discussed with participants, accompanied by guidance on implementation measures that contribute to proactively building trust in the development of the SHDS. Communication recommendations are further provided, highlighting that the success of the SHDS will depend on early and continuous trustworthy public communication efforts that actively engage the Swiss public, address their concerns, and foster support throughout its development. Overarching these efforts will be a foundational governance approach that meaningfully involves relevant stakeholders and members of the Swiss public, while allocating appropriate responsibility to maintain trustworthiness of the SHDS.

In our digital world, reusing data to inform: decisions, advance science, and improve people’s lives should be easier than ever. However, the reuse of data remains limited, complex, and challenging. Some of this complexity requires rethinking consent and public participation processes about it. First, to ensure the legitimacy of uses, including normative aspects like agency and data sovereignty. Second, to enhance data quality and mitigate risks, especially since data are proxies that can misrepresent realities or be oblivious to the original context or use purpose. Third, because data, both as a good and infrastructure, are the building blocks of both technologies and knowledge of public interest that can help societies work towards the well-being of their people and the environment. Using the case study of the European Health Data Space, we propose a multidimensional, polytopic framework with multiple intersections to democratising decision-making and improving the way in which meaningful participation and consent processes are conducted at various levels and from the point of view of institutions, regulations, and practices.

Data governance has emerged as a pivotal area of study over the past decade, yet despite its growing importance, a comprehensive analysis of the academic literature on this subject remains notably absent. This paper addresses this gap by presenting a systematic review of all academic publications on data governance from 2007 to 2024. By synthesizing insights from more than 3500 documents authored by more than 9000 researchers across various sources, this study offers a broad yet detailed perspective on the evolution of data governance research.

Participation is a prevalent topic in many areas, and data-driven projects are no exception. While the term generally has positive connotations, ambiguities in participatory approaches between facilitators and participants are often noted. However, how facilitators can handle these ambiguities has been less studied. In this paper, we conduct a systematic literature review of participatory data-driven projects. We analyse 27 cases regarding their openness for participation and where participation most often occurs in the data life cycle. From our analysis, we describe three typical project structures of participatory data-driven projects, combining a focus on labour and resource participation and/or rule- and decision-making participation with the general set-up of the project as participatory-informed or participatory-at-core. From these combinations, different ambiguities arise. We discuss mitigations for these ambiguities through project policies and procedures for each type of project. Mitigating and clarifying ambiguities can support a more transparent and problem-oriented application of participatory processes in data-driven projects.

The EU’s Common European Data Space (CEDS) aims to create a single market for data-sharing in Europe, build trust among stakeholders, uphold European values, and benefit society. However, there is the possibility that the values of the EU and the benefits for the common good of European society may get overlooked for the economic benefits of organisations if norms and social values are not considered. We propose that the concept of “data commons” is relevant for defining openness versus enclosure of data in data spaces and is important when considering the balance and trade-off between individual (market) versus collective (societal) benefits from data-sharing within the CEDS. Commons are open-access resources governed by a group, either formally by regulation or informally by local customs. The application of the data commons to the CEDS would promote data-sharing for the “common good.” However, we propose that the data commons approach should be balanced with the market-based approach to CEDS in an inclusive hybrid data governance approach that meets material, price-driven interests, while stimulating collective learning in online networks to form social communities that offer participants a shared identity and social recognition.

The REDATAM (retrieval of data for small areas by microcomputer) statistical package and format, developed by ECLAC, has been a critical tool for disseminating census data across Latin America since the 1990s. However, significant limitations persist, including its proprietary nature, lack of documentation, and restricted flexibility for advanced data analysis. These challenges hinder the transformation of raw census data into actionable information for policymakers, researchers, and advocacy groups. To address these issues, we developed Open REDATAM, an open-source and multiplatform tool that converts REDATAM data into widely supported CSV files and native R and Python data structures. By providing integration with R and Python, Open REDATAM empowers users to work with the tools they already know and perform data analyses without leaving their R or Python window. Our work emphasizes the need for a REDATAM official format specification to further enable informed policy debates that can improve policy processes’ implementation and feedback.

The Belt and Road Initiative (BRI) is a Chinese infrastructure and investment project launched in 2013 that seeks to link China with over 70 countries through transport, communication, and trading networks. The BRI consists of building and construction projects including railways, ports, roads, and other vital trade infrastructure. Importantly, the BRI also includes the establishment of a new “Digital Silk Road” (DSR) enhancing digital connectivity using the latest 5G high bandwidth, low latency mobile and satellite technology. In 2019, several Western states (notably USA, Australia, Canada, and the UK) banned Chinese telecommunications technology companies, such as Huawei, from rolling out 5G networks in their respective jurisdictions. The purported basis for the bans were security concerns over the ability of the Chinese government to control and potentially intercept communications over the Huawei systems. In the MENA region, no such bans have been adopted and the DSR is proceeding to connect MENA economies to China at a rapid pace. This places MENA countries in a precarious position between strategic links with the US as the global hegemon with a strategic interest in the region, and the emerging Chinese global political and economic order. The regulation of digital communication technologies is one dimension where legal frameworks must be designed with care and discernment to balance competing geopolitical forces. This article seeks to answer the question of how best to understand the legal regulation of new technologies in the MENA region and argues that the conceptual lens of “Tech-diplomacy” helps to provide such an understanding. In addition to privacy-centric, security-centric, and growth-centric philosophical and jurisprudential approaches to understanding data regulation, the predicament of the MENA region is a case study in how geopolitics can also inform our understanding of tech regulation.

Some observers of the data economy have proposed that we treat data as labor. But are data contributions labor? Our folk conception of work emphasizes its importance and effort, such that work has a special interpersonal priority and deserves appreciation and compensation. The folk conception does not generally favor counting data as work, and so it serves as an error theory for reluctance to regulate data as labor. In contrast, labor regulation and policy focus on the political economy of labor, and in particular the bargaining dynamics that participants in a labor market face. Labor regulation aims to protect workers against the threats characteristically posed by such bargaining dynamics. Data-transferring interactions between internet platforms and users share similar bargaining dynamics, and so there is a promising functional similarity between labor regulation and proposals for data regulation that would facilitate data strikes and data unions.

The COVID-19 pandemic underscored the critical need for timely data and information to aid interventions and decision-making. Efforts by different actors resulted in various data-driven initiatives, constituting experiences of deploying data in the COVID-19 response and valuable lessons that can advance the sharing and use of data for social good beyond COVID-19. This commentary highlights key case studies detailing the experiences and lessons of those who implemented data science solutions for the COVID-19 response, as well as findings from 74 data-centric COVID-19 interventions. These interventions demonstrated successful data access strategies, productive intervention processes, and effective stakeholder engagement, all of which present potential pathways to overcoming data access obstacles across Africa. Additionally, this study also briefly explores three areas for action (i.e., institutions, people, and platforms) that can inform future policy development to increase data sharing for societal benefit in the long term.

Public agencies routinely collect administrative data that, when shared and integrated, can form a rich picture of the health and well-being of the communities they serve. One major challenge is that these datasets are often siloed within individual agencies or programs and using them effectively presents legal, technical, and cultural obstacles. This article describes work led by the North Carolina Department of Health and Human Services (NCDHHS) with support from university-based researchers to establish enterprise-wide data governance and a legal framework for routine data sharing, toward the goal of increased capacity for integrated data analysis, improved policy and practice, and better health outcomes for North Carolinians. We relied on participatory action research (PAR) methods and Deliberative Dialogue to engage a diverse range of stakeholders in the co-creation of a data governance process and legal framework for routine data sharing in NCDHHS. Four key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy road map, created a data sharing guidebook to operationalize legal and ethical review of requests, staffed the Data Office, and implemented a legal framework. In addition to describing how these ongoing streams of work support data use across a large state health and human services agency, we provide three use cases demonstrating the impact of this work. This research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, integration, and use.

Enabling private sector trust stands as a critical policy challenge for the success of the EU Data Governance Act and Data Act in promoting data sharing to address societal challenges. This paper attributes the widespread trust deficit to the unmanageable uncertainty that arises from businesses’ limited usage control to protect their interests in the face of unacceptable perceived risks. For example, a firm may hesitate to share its data with others in case it is leaked and falls into the hands of business competitors. To illustrate this impasse, competition, privacy, and reputational risks are introduced, respectively, in the context of three suboptimal approaches to data sharing: data marketplaces, data collaboratives, and data philanthropy. The paper proceeds by analyzing seven trust-enabling mechanisms comprised of technological, legal, and organizational elements to balance trust, risk, and control and assessing their capacity to operate in a fair, equitable, and transparent manner. Finally, the paper examines the regulatory context in the EU and the advantages and limitations of voluntary and mandatory data sharing, concluding that an approach that effectively balances the two should be pursued.