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Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. The purpose of this paper is to analyses the factors which influence data sharing by investigating journal data policies and the behaviour of authors in sociology.

The web sites of 140 sociology journals were consulted to check their data policy. The results are compared with similar studies from political science and economics. A broad selection of articles published in five selected journals over a period of two years are examined to determine whether authors really cite and share their data and the factors which are related to this.

Although only a few sociology journals have explicit data policies, most journals make reference to a common policy supplied by their association of publishers. Among the journals selected, relatively few articles provide data citations and even fewer make data available – this is true both for journals with and without a data policy. But authors writing for journals with higher impact factors and with data policies are more likely to cite data and to make it really accessible.

No study of journal data policies has been undertaken to date for the domain of sociology. A comparison of authors’ behaviours regarding data availability, data citation, and data accessibility for journals with or without a data policy provides useful information about the factors which improve data sharing.

This paper aims to assess the policy framework and planning regarding research data management (RDM) in university libraries of Pakistan.

Data were collected from 30 Higher Education Commission high ranking university libraries by using mixed method explanatory sequential design.

The results indicate that library heads just heard about RDM, but there was lack of knowledge and awareness. Few libraries were at the planning stage. Other major challenges including lack of willingness, motivation and coordination with researchers, non-availability of skillful professional and support staff, poor infrastructure and networking were found in this regard.

This is the first study of its kind that explores the planning and policy development regarding RDM in university libraries of Pakistan.

The purpose of this paper is to provide a comprehensive cross-national comparative framework to compare open data policies from different countries and to derive lessons for developing open data policies. Open data policies guide the opening and stimulate the usage of public data. However, some countries have no or less developed open data policies, in this way missing the opportunity to reap the benefits of open data.

Literature review and case studies were conducted to extend an existing comparison framework, and the framework was used to compare open data policies of the UK, the USA, The Netherlands, Kenya and Indonesia.

The comparison of open data policies highlighted several lessons that can be learned, including actions regarding a robust legal framework, generic operational policies, data providers and data users, data quality, designated agencies or taskforces and initiatives and incentives for stimulating demand for data. National policies should also be focused on removing barriers on the operational level and policies for stimulating the release and use of data.

There is hardly any research systematically comparing open data policies. The comparative framework provided in this paper is a first analytical basis for cross-national comparison of open data policies and offers possibilities for systematic cross-national lesson-drawing.

The authors found two waves of policy-making. The first wave of policy is focused on stimulating the release of data, whereas the second wave of policy is aimed at stimulating use. The comparison can be used to learn from other policies and help to improve open data policies. A third wave of open data policy is expected to materialize focusing on realizing added value from utilizing open data.

Improving a country’s open data policy can help the country to reap the benefits of open data, such as government transparency, efficiency and economic growth.

Open data are a recent phenomenon and countries are looking for ways to obtain the benefits. This research can be used for developing and evaluating open data policies.

The shift from data-informed to data-driven educational policymaking is conceptually framed by institutional and transhumanist perspectives. Examples of the shift to large-scale quantitative data driving educational decision-making suggest that data-driven educational policy will not adjust for context to the degree as done by the data-informed or data-based policymaking. Instead, the algorithmization of educational decision-making is both increasingly realizable and necessary in light of the overwhelmingly big data on education produced annually around the world. Evidence suggests that the isomorphic shift from localized data and individual decision-making about education to large-scale assessment data has changed the nature of educational decision-making and national educational policy. Big data are increasingly legitimized in educational policy communities at national and international levels, which means that algorithms are assumed to be the best way to analyze and make decisions about large volumes of complex data. There is a conceptual concern, however, that decontextualized or de-humanized educational policies may have the effect of increasing student achievement, but not necessarily the translation of knowledge into economically, socially, or politically productive behavior.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

The paper aims to review rules and policy guidelines worldwide around non-personal data (NPD) and evaluate the policies on criteria that allow for the use of data for economic and social good. A review related to diverse policy approaches of various countries remains a research gap, and hence the analysis in the paper is designed with the intention of developing a research framework and providing policy gaps for further exploration.

A systematic review of academic and non-academic literature on theoretical foundations, applications of NPD for economic and social good and NPD policies and regulations was conducted to identify the evaluation criteria. A total of 32 dimensions got identified for evaluation. As second step, content analysis was used for evaluation. A total of 13 documents from 6 countries and 1 geographical region were identified for evaluation. The documents were evaluated based on the 32 dimensions spread across 5 domains that facilitate data access and sharing for economic and societal benefit.

The analysis highlights three distinct emerging perspectives on data exchange: most policy and regulatory documents acknowledge the importance of identifying different types of NPD and accordingly describing the distinct roles and responsibilities of data actors for leveraging the data; the policy and regulatory frameworks clearly focus on increasing business opportunities, data sharing cooperation and innovation; and findings also demonstrate certain gaps in the policy frameworks such as a more comprehensive discussion on data access and sharing mechanisms, particularly data sandboxes and open data, and concrete norms and rigorous standards regarding accountability, transparency, ownership and confidentiality. Furthermore, policies and regulations may include appropriate incentive structures for data providers and users to ensure unhindered and sustainable access to data for the common good.

To the best of the authors’ knowledge, this paper represents one of the first research contributions evaluating global data policies focused on NPD in the context of its increasing use as a public good. The paper first identifies evaluation criteria for the analysis on public and social good, and, thus, provides a conceptual framework for future research. Additionally, the analysis identifies the broad domains of policy analysis on social and public good for data economics.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

The purpose of this paper is three-fold. Firstly, through selected case studies, to provide an overview of how non-traditional data from digital public services were used as a source of knowledge for policymaking. Secondly, to argue for a design for policy approach to support the successful integration of non-traditional data into policymaking practice, thus supporting data-driven innovation for policymaking. Thirdly, to encourage a vision of the relation between data-driven innovation and public policy that considers policymaking outside the authoritative instrumental logic perspective.

A qualitative small-N case study analysis based on desk research data was developed to provide an overview of how data-centric public services could become a source of knowledge for policymaking. The analysis was based on an original theoretical-conceptual framework that merges the policy cycle model and the policy capacity framework.

This paper identifies three potential areas of contribution of a design for policy approach in a scenario of data-driven innovation for policymaking practice: the development of sensemaking and prefiguring activities to shape a shared rationale behind intra-/inter-organisational data sharing and data collaboratives; the realisation of collaborative experimentations for enhancing the systemic policy analytical capacity of a governing body, e.g. by integrating non-traditional data into new and trusted indicators for policy evaluation; and service design as approach for data-centric public services that connects policy decisions to the socio-technical context in which data are collected.

The small-N sample (four cases) selected is not representative of a broader population but isolates exemplary initiatives. Moreover, the analysis was based on secondary sources, limiting the assessment quality of the real use of non-traditional data for policymaking. This level of empirical understanding is considered sufficient for an explorative analysis that supports the original perspective proposed here. Future research will need to collect primary data about the potential and dynamics of how data from data-centric public services can inform policymaking and substantiate the proposed areas of a design for policy contribution with practical experimentations and cases.

This paper proposes a convergence, yet largely underexplored, between the two emerging perspectives on innovation in policymaking: data for policy and design for policy. This convergence helps to address the designing of data-driven innovations for policymaking, while considering pragmatic indications of socially acceptable practices in this space for practitioners.

This paper aims to understand the current development situation of scientific data management policy in China, analyze the content structure of the policy and provide a theoretical basis for the improvement and optimization of the policy system.

China's scientific data management policies were obtained through various channels such as searching government websites and policy and legal database, and 209 policies were finally identified as the sample for analysis after being screened and integrated. A three-dimensional framework was constructed based on the perspective of policy tools, combining stakeholder and lifecycle theories. And the content of policy texts was coded and quantitatively analyzed according to this framework.

China's scientific data management policies can be divided into four stages according to the time sequence: infancy, preliminary exploration, comprehensive promotion and key implementation. The policies use a combination of three types of policy tools: supply-side, environmental-side and demand-side, involving multiple stakeholders and covering all stages of the lifecycle. But policy tools and their application to stakeholders and lifecycle stages are imbalanced. The development of future scientific data management policy should strengthen the balance of policy tools, promote the participation of multiple subjects and focus on the supervision of the whole lifecycle.

This paper constructs a three-dimensional analytical framework and uses content analysis to quantitatively analyze scientific data management policy texts, extending the research perspective and research content in the field of scientific data management. The study identifies policy focuses and proposes several strategies that will help optimize the scientific data management policy.

The purpose of this paper is to advocate for and provide guidance for the development of a code of ethical conduct surrounding online privacy policies, including those concerning data mining. The hope is that this research generates thoughtful discussion on the issue of how to make data mining more effective for the business stakeholder while at the same time making it a process done in an ethical way that remains effective for the consumer. The recognition of the privacy rights of data mining subjects is paramount within this discussion.

The authors derive foundational principles for ethical data mining. First, philosophical literature on moral principles is used as the theoretical foundation. Then, using existing frameworks, including legislation and regulations from a range of jurisdictions, a compilation of foundational principles was derived. This compilation was then evaluated and honed through the integration of stakeholder perspective and the assimilation of moral and philosophical precepts. Evaluating a sample of privacy policies hints that current practice does not meet the proposed principles, indicating a need for changes in the way data mining is performed.

A comprehensive framework for the development a contemporary code of conduct and proposed ethical practices for online data mining was constructed.

This paper provides a configuration upon which a code of ethical conduct for performing data mining, tailored to meet the particular needs of any organization, can be designed.

The implications of data mining, and a code of ethical conduct regulating it, are far-reaching. Implementation of such principles serve to improve consumer and stakeholder confidence, ensure the enduring compliance of data providers and the integrity of its collectors, and foster confidence in the security of data mining.

Existing legal mandates alone are insufficient to properly regulate data mining, therefore supplemental reference to ethical considerations and stakeholder interest is required. The adoption of a functional code of general application is essential to address the increasing proliferation of apprehension regarding online privacy.