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Government administrative data have enormous potential for public and individual benefit through improved educational and health services to citizens, medical research, environmental and climate interventions and better use of scarce energy resources. The purpose of this study (part of the Administrative Data Research Centre in England, ADRC-E) was to examine perspectives about the sharing, linking and re-use (secondary use) of government administrative data. This study seeks to establish an analytical understanding of risk with regard to administrative data.

This qualitative study focused on the secondary use of government administrative data by academic researchers. Data collection was through 44 semi-structured interviews plus one focus group, and was supported by documentary analysis and a literature review. The study draws on the views of expert data researchers, data providers, regulatory bodies, research funders, lobby groups, information practitioners and data subjects.

This study discusses the identification and management of risk in the use of government administrative data and presents a risk framework.

This study will have resonance with records managers, risk managers, data specialists, information policy and compliance managers, citizens groups that engage with data, as well as all those responsible for the creation and management of government administrative data.

First, this study identifies and categorizes the risks arising from the research use of government administrative data, based on policy, practice and experience of those involved. Second, it identifies mitigating risk management activities, linked to five key stakeholder communities, and it discusses the locus of responsibility for risk management actions. The conclusion presents the elements of a new risk framework to inform future actions by the government data community and enable researchers to exploit the power of administrative data for public good.

Open government data and access to public sector information is commonplace, yet little attention has focussed on the essential roles and responsibilities in practice of the information and records management professionals, who enable public authorities to deliver open data to citizens. This paper aims to consider the perspectives of open government and information practitioners in England on the procedural and policy implications of open data across local public authorities.

Using four case studies from different parts of the public sector in England (local government, higher education, National Health Service and hospital trust), the research involved master’s level students in the data collection and analysis, alongside academics, thus enhancing the learning experience of students.

There was little consistency in the location of responsibility for open government data policy, the range of job roles involved or the organisational structures, policy and guidance in place to deliver this function. While this may reflect the organisational differences and professional concerns, it makes it difficult to share best practice. Central government policy encourages public bodies to make their data available for re-use. However, local practice is very variable and perhaps understandably responds more to local organisational strategic and resource priorities. The research found a lack of common metadata standards for open data, different choices about which data to open, problems of data redundancy, inconsistency and data integrity and a wide variety of views on the corporate and public benefits of open data.

The research is limited to England and to non-national public bodies and only draws data from a small number of case studies.

The research contributes to the debate about emerging issues around the complexities of open government data and its public benefits, contributing to the discussions around technology-enabled approaches to citizen engagement and governance. It offers new insights into the interaction between open data and public policy objectives, drawing on the experience of local public sectors in England.

Open government data (OGD) is making available data freely to all by the government with the aim to ensure accountability and transparency in government besides generating public value by its usage. OGD is an emerging government initiative in Pakistan, and there is a lot of potential for harnessing OGD for spearheading innovation and improvising services in different economic sectors. This paper aims to seek to investigate the usage of OGD in private sector and non-governmental organizations (NGOs) in Pakistan.

Documentary analysis is conducted to review the national OGD portal of Pakistan. Besides, structured interviews were conducted with 49 senior management representatives from private sector and NGOs to solicit their perspectives regarding the usage of OGD for professional purposes. Also, questions were posed regarding the challenges in harnessing OGD for professional purposes.

OGD has immense usage for private sector and NGOs; however, more initiatives are required on the part of the government to open their data sets. Further, involvement of stakeholders from the private sector and NGOs and other interested partners is required for an optimum usage of OGD in different economic sectors of Pakistan.

As the research focuses on private sector and NGOs in Pakistan, the study requires a more broad-based approach to take into account the perspectives of different users. Further research is required to appreciate the role of contextual factors in determining the usage of OGD in Pakistan.

Government should be more forthcoming in its OGD initiatives and encourage collaboration from citizens, businesses, voluntary organizations and the like.

Working professionals from different economic sectors should tap OGD for generating value by improvising their services.

The study is the first one to be conducted in Pakistan about the OGD initiatives of the country. Soliciting views from the key management representatives in the private sector and NGOs, the paper contributed to the extant OGD literature which is more supply-focused and not demand-driven. While conceding that there are ample usages of OGD for the different economic sectors, the paper underlines the need for refining the OGD initiatives of Pakistan.

Sustainable Development Goal (SDG) Indicator 4.2 calls for all girls and boys to have access to high-quality early childhood education by 2030. This global mandate establishes a new framework of accountability to increase access to preprimary education in low- and middle-income countries through measurement and reporting. As with other global indicators, however, the measurement of preprimary education access is more complex and nuanced than may be supposed. This data-oriented chapter delves deeply into the measurement of SDG 4.2 and explores the accuracy of the indicator being used: the adjusted net enrollment ratio, one year before the official age of primary entry. The chapter analyzes data from both education management information systems (EMIS) and household surveys to triangulate information about children’s access to preprimary education before they begin primary school. The analysis concludes that the indicator used to measure SDG 4.2 is overestimating access to preprimary education, because it includes large numbers of children who enroll in primary school before the official age of entry. This suggests that parents “vote for preschool” by sending their under-age children to primary school when access to affordable preprimary is limited. Implications for SDG measurement and preprimary policy are discussed.

The purpose of this paper is to understand the landscape of data curation services among public and academic libraries in the USA, with a focus on library capacity for providing data curation services.

The authors conducted an online survey by employing stratified sampling from the American Library Directory. A total of 198 responses were analyzed.

The authors’ findings provide insight into the current landscape of libraries’ data curation services. The survey participants evaluated six capacity dimensions for both public and academic libraries – value, financial, administrative, technical infrastructure, human resources and network. The ratings the participants gave to these capacity dimensions were significantly different between academic and public libraries.

This study suggests several areas in which libraries will benefit from further developing their capacity to successfully run data curation services.

This is among the first research study to address the concept of capacity in the context of libraries’ data curation services.

The purpose of this paper is to highlight the importance of understanding, analyzing and managing big data in the field of public administration.

It discusses the unique attributes of Twitter data and proposes a text-mining and analysis framework for conducting research using data on social networking sites. Furthermore, this paper reviews recent scholarship that collected and analyzed Twitter data to address public administration or policy issues. It examines what research questions have been addressed through analyzing Twitter data and how researchers collected and analyzed Twitter data.

It concludes that a more systematic process is needed for data collection, and more in-depth analysis is needed to better understand and utilize Twitter data for public administration.

This paper is one of the early attempts to synthesize and review the existing research that used Twitter data to address public administration issues.

This study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.

In this study, the authors extracted metadata of 442 documents from a bibliographic database. The authors used a bibliometric mapping tool for familiarization with the literature. After that, the authors used qualitative analysis software to develop taxonomy.

This paper developed taxonomy of OGD with three research areas: implementation and management, architecture, users and utilization. These research areas are further analyzed into seven topics and twenty-eight subtopics. The present study extends Charalabidis et al. (2016) taxonomy by adding two research topics, namely the adoption factors and barriers of OGD implementations and OGD ecosystems. Also, the authors include artificial intelligence in the taxonomy as an emerging research interest in the literature. The authors suggest four directions for future research: indigenous knowledge in open data, open data at local governments, development of OGD-specific theories and user studies in certain research themes.

Early career researchers and doctoral students can use the taxonomy to familiarize themselves with the literature. Also, established researchers can use the proposed taxonomy to inform future research. Taxonomy-building procedures in this study are applicable to other fields.

This study developed a novel taxonomy of research areas in OGD. Taxonomy building is significant because there is insufficient taxonomy of research areas in this discipline. Also, conceptual knowledge through taxonomy creation is a basis for theorizing and theory-building for future studies.

This paper aims to present pertinent research challenges in the field of (big) data-informed policy-making based on the research, undertaken within the course of the European Union-funded project Big Policy Canvas. Technological advancements, especially in the past decade, have revolutionised the way that both every day and complex activities are conducted. It is, thus, expected that a particularly important actor such as the public sector, should constitute a successful disruption paradigm through the adoption of novel approaches and state-of-the-art information and communication technologies.

The research challenges stem from a need, trend and asset assessment based on qualitative and quantitative research, as well as from the identification of gaps and external framework factors that hinder the rapid and effective uptake of data-driven policy-making approaches.

The current paper presents a set of research challenges categorised in six main clusters, namely, public governance framework, privacy, transparency, trust, data acquisition, cleaning and representativeness, data clustering, integration and fusion, modelling and analysis with big data and data visualisation.

The paper provides a holistic overview of the interdisciplinary research challenges in the field of data-informed policy-making at a glance and shall serve as a foundation for the discussion of future research directions in a broader scientific community. It, furthermore, underlines the necessity to overcome isolated scientific views and treatments because of a high complex multi-layered environment.

In the twenty-first century, data are the world’s most valuable resource. Technological capacities for the collection, storage, analysis and sharing of data are evolving rapidly, and as a result, so too are the possibilities for improving the day-to-day lives of people. However, data use can also result in exploitation and harm; nowhere is this more evident than for Indigenous peoples. Unfortunately, the rapid expansion of technology has not been matched by a sufficiently robust discussion of ethics nor the development of governance frameworks. Indigenous Data Sovereignty (IDS) has emerged as a key consideration for this cause. Beginning with the presumption of Indigenous rights to tribal/nation sovereignty, IDS weaves together Indigenous research ethics, cultural and intellectual property rights and Indigenous governance discourse, with the view to offer solutions to the challenges being presented in an open data environment. This chapter will expand on this existing literature base and consider Māori data sovereignty in the context of Aotearoa New Zealand. More specifically, it provides the basis for a discussion around how kawa and tikanga drawn from Te Ao Māori might inform approaches to data ethics and data governance.

The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.

Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.

A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.

Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.

The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.