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As digital platforms are increasingly shaping access to information, their control over platform data has profound implications for research, innovation, and accountability. This article presents the first comprehensive empirical study of how digital platforms have used contractual terms to enclose publicly available data over the past decade. Analyzing the Terms of Use of 279 platforms from 2012 and 2022, we identify a systematic increase in both restrictive and permissive clauses governing access to data for research purposes. We find that platforms adopted a dual contractual strategy: restrictive terms limit third-party access to data for research, while permissive terms preserve the platform's own ability to use and share data with selected partners. This strategy emerged alongside the rise of consent-based privacy regulation, especially the EU's General Data Protection Regulation (GDPR), which, despite its objective of protecting users, inadvertently expands platforms’ discretion over data access. Our findings suggest that privacy regulation has facilitated, rather than curtailed, platform data enclosures—enabling platforms to assert de facto control over data in the public domain. We contribute to the literature on data governance, platform regulation, and contract theory by documenting how platforms adapt boilerplate contracts in response to regulatory change and leverage privacy obligations to restrict independent research. These findings carry significant implications for legal and policy debates surrounding data access, transparency, and the future of platform accountability. They underscore the need to regulate the privatization of publicly available data to ensure equitable access for research serving the public interest.

The May 2022 proposal from the European commission for a ‘European health data space’ envisages advantages for health from exploiting the growing mass of health data in Europe. However, key stakeholders have identified aspects that demand clarification to ensure success. Data will need to be freed from traditional silos to flow more easily and to cross artificial borders. Wide engagement will be necessary among healthcare professionals, researchers, and the patients and citizens that stand to gain the most but whose trust must be won if they are to allow use or transfer of their data. This paper aims to alert the wider scientific community to the impact the ongoing discussions among lawmakers will have. Based on the literature and the consensus findings of an expert multistakeholder panel organised by the European Alliance for Personalised Medicine (EAPM) in June 2022, it highlights the key issues at the intersection of science and policy, and the potential implications for health research for years, perhaps decades, to come.

Understanding how Antarctica is changing and how these changes influence the rest of the Earth is fundamental to the future robustness of human society. Strengthening our understanding of these changes and their implications requires dedicated, sustained and coordinated observations of key Antarctic indicators. The Troll Observing Network (TONe), now under development, is Norway’s contribution to the global need for sustained, coordinated, complementary and societally relevant observations from Antarctica. When fully implemented within the coming three years, TONe will be a state-of-the-art, multi-platform, multi-disciplinary observing network in data-sparse Dronning Maud Land. A critical part of the network is a data management system that will ensure broad, free access to all TONe data to the international research community.

Researchers, governments, and funding agencies are calling on research disciplines to embrace open data—data that anyone can access and use. They have done so based on the premise that research efforts can draw and generate several benefits from open data because it might provide further insight and enable individuals to replicate and extend current knowledge in different contexts. These potential benefits, coupled with a global push towards open data policies, bring open data into the agenda of research disciplines, which includes information systems (IS). In this paper, we respond to these developments as follows. We outline themes in the ongoing discussion around open data in the IS discipline. The themes fall into two clusters: 1) the motivation for open data includes themes of mandated sharing, benefits to the research process, extending the life of research data, and career impact; and 2) the implementation of open data includes themes of governance, socio-technical system, standards, data quality, and ethical considerations. In this paper, we outline the findings from a pre-ICIS 2016 workshop on the topic of open data. The workshop discussion confirmed themes and identified issues that require attention in terms of the approaches that IS researchers currently use. The IS discipline offers a unique knowledge base, tools, and methods that can advance open data across disciplines. Based on our findings, we provide suggestions on how IS researchers can drive the open data conversation. Further, we provide advice for adopting and establishing procedures and guidelines for archiving, evaluating, and using open data.

When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does.

Limited international authorship in psychological journals is one indicator of the need for greater internationalization of research in psychology. This paper is a review of six international large-scale recurring survey programs with open access to data with regard to their contributions to international research collaborations and publications in psychology. These programs are the International Social Survey Programme, the World Values Survey, Children’s Worlds: International Survey of Children’s Well-Being, the Programme for International Student Assessment, the Programme for International Assessment of Adult Competencies, and the Skills Toward Employment and Productivity Measurement Programme. Each program is described in terms of the stages of research (planning and standard setting, instrumentation, data-gathering procedures and quality control, data access and user support) and its development and expansion through the years. The ensuing research outputs and journal publications have international authorship and have analyzed the survey data to address broad, international issues in psychology. These programs exemplify features that encourage and facilitate international collaborations and publications in psychology: an international collaborative network that gathers comprehensive data from large nationally diverse samples over the years, provisions for open, unrestricted access to data, survey documentation, and user support.

Understanding the causes of interstate conflict continues to be a primary goal of the field of international relations. To that end, scholars continue to rely on large datasets of conflict in the international system. This paper introduces the latest iteration in the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 4 data. In this paper we first outline the updated data-collection process for the MID4 data. Second, we present some minor changes and clarifications to the coding rules for the MID4 datasets, as well as pointing out how the MID coding procedures affect several notable \"close call\" cases. Third, we introduce updates to the existing MID datasets for the years 2002–2010 and provide descriptive statistics that allow comparisons of the newer MID data to prior versions. We also offer some best practices and point out several ways in which the new MID data can contribute to research in international conflict.

This paper describes a project aimed at digitizing and openly sharing the natural history collections (AMUNATCOLL) of the Faculty of Biology at Adam Mickiewicz University in Poznań (Poland). The result of this project is a database (including 2.2 million records) of plant, fungal and animal specimens, which is available online via the AMUNATCOLL portal and on the Global Biodiversity Information Facility website. This article presents selected aspects of the “life cycle” of this project, with a particular focus on its preparatory phase.

We define the concept of information quality 'InfoQ' as the potential of a data set to achieve a specific (scientific or practical) goal by using a given empirical analysis method. InfoQ is different from data quality and analysis quality, but is dependent on these components and on the relationship between them. We survey statistical methods for increasing InfoQ at the study design and post-data-collection stages, and we consider them relatively to what we define as InfoQ. We propose eight dimensions that help to assess InfoQ: data resolution, data structure, data integration, temporal relevance, generalizability, chronology of data and goal, construct operationalization and communication. We demonstrate the concept of InfoQ, its components (what it is) and assessment (how it is achieved) through three case-studies in on-line auctions research. We suggest that formalizing the concept of InfoQ can help to increase the value of statistical analysis, and data mining both methodologically and practically, thus contributing to a general theory of applied statistics.

Objective. Since 2008, the Standardized World Income Inequality Database (SWIID) has provided income inequality data that seek to maximize comparability while providing the broadest possible coverage of countries and years. This article describes the current SWIID’s construction, highlighting differences from its original version, and reevaluates the SWIID’s utility to cross-national income inequality research in light of recently available alternatives. Methods. Coverage of inequality data sets is assessed across country-years; comparability is evaluated in terms of success in predicting the Luxembourg Income Study (LIS), recognized in the field as the gold standard in comparability, before those data are released. Results. The SWIID offers coverage double that of the next largest income inequality data set, and its record of comparability is three to eight times better than those of alternate data sets. Conclusions. As its coverage and comparability far exceed those of the alternatives, the SWIID remains better suited for broadly cross-national research on income inequality than other available sources.