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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.

The present paper constructed a new framework for government data governance based on the concept of a data middle platform to elicit the detailed requirements and functionalities of a government data governance framework.

Following a three-cycle activity, the design science research (DSR) paradigm was used to develop design propositions. The design propositions are obtained based on a systematic literature review of government data governance and data governance frameworks. Cases and experts further assessed the effectiveness of the implementation of the artifacts.

The study developed an effective framework for government data governance that supported the digital service needs of the government. The results demonstrated the advantages of the framework in adapting to organizational operations and data, realized the value of data assets, improved data auditing and oversight and facilitated communication. From the collection of data to the output of government services, the framework adapted to the new characteristics of digital government.

Knowledge of the “data middle platforms” generated in this study provides new knowledge to the design of government data governance frameworks and helps translate design propositions into concrete capabilities. By reviewing earlier literature, the article identified the core needs and challenges of government data governance to help practitioners approach government data governance in a structured manner.

The aim of this paper is to ask why the libraries and the computer and media centres of German universities are picking up too little to improve the conditions for learning, teaching and research.

With the founding of the Information, Communication and Media Center/Informations‐, Kommunikations‐ und Medienzentrums (ICMC/IKMZ), the central services of the Brandenburg Technical University of Cottbus (BTU Cottbus) library, computer center, multimedia center and administrative data processing were combined on a common management level, and the prerequisites for the development of integrated information management were created.

On this basis the following goal was set: the realization of more efficient organizational structures for a broad spectrum of customer‐ and service‐oriented information, communications and media (ICM) offers for research, teaching and administration.

The paper gives an overview of German discussion on the convergence of services, and presents the ICMC/IKMZ of Cottbus University as an example of a converged institution.

Like Rome, the University of Michigan's campus‐wide fiber optic network was not built in a day. During an intense three‐week period in May 1988, Pradeep Patel, Transmission Engineer at University Telecommunications, managed to squeeze in 210 hours to complete the detailed engineering plans for the campus‐wide fiber optic network. Prior to this period, Patel and other UMTel and Information Technology Division employees were involved in every stage of network specification and implementation. Designing a practical, workable cable layout for a network covering 25 miles and connecting 72 buildings was no small job.

The purpose of this paper is to develop a holistic model for educational purposes using data‐mining techniques for exploring the effects of probable changes in processes related to admissions, course delivery and recruitments.

The paper proposes a framework for an effective educational process using data‐mining techniques to uncover the hidden trends and patterns and making accuracy based predictions through a higher level of analytical sophistication in the process of counselling students.

Data‐mining tools are used in academia for capitalizing on the advances of information technology. This process improves research and academic decision making through uncovering hidden trends and patterns that predict using a combination of explicit knowledge base, sophisticated analytical skills and academic domain knowledge.

The paper presents a model using a data‐mining approach for academics.

The developing role of information is explored within modern business, and in particular the use of strategic information systems to gain competitive advantage. It is suggested that IT requires significant redesign of enterprises with regard to organisation, behaviour, direction and planning.

Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

The purpose of this paper is to identify the relationships between process modeling and Industry 4.0, the strategic themes and the most used process modeling language in smart factories. The study also presents the growth of the field of study worldwide, the perspectives, main challenges, trends and suggestions for future works.

To do this, a science mapping was performed using the software SciMAT, supported by VOS viewer.

The results show that the Business Process Model and Notation (BPMN), Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing. The authors also highlighted the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories.

It was possible to identify the most used process modeling languages in smart environments and understand how these languages assist control and manage smart processes. Besides, the authors highlighted challenges, new perspectives and the need for future works in the field.

This paper will describe the way in which the USMARC cataloging schema is used at the Eisenhower National Clearing‐house (ENC). Discussion will include how ENC MARC extensions were developed for cataloging mathematics and science curriculum resources, and how the ENC workflow is integrated into the cataloging interface. The discussion will conclude with a historical look at the in‐house data transfer from ENC MARC to the current production of IEEE LOM XML encoding for record sharing and OAI compliance, required under the NSDL project guidelines.

Clinical coding is a process of accurate translation of written medical terms into codes. The Payment by Results initiative has focused attention on the quality of clinical coded data as all income for in patient services is derived from coded clinical data. The aim of this study was to evaluate the quality of clinical coded data by making comparisons between the information held on the dialect encoder system and the information recorded in the clinical case notes.

The 50 episodes for this study were randomly selected from a list of all episodes ending August 2005 within the ENT specialty in a teaching hospital.

There were only 17 (34 per cent) episodes with a structured summary within the case notes. Of the 50 recorded primary diagnoses 42 (84 per cent) were correctly coded. Of the 43 recorded primary procedures, 37 (86 per cent) were correctly coded.

This study promotes a better awareness of the impact of poor coding and gives recommendations that will be helpful to those involved in coding processes.