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The purpose of this paper is to illustrate the value of extended time span coverage of state longitudinal education and workforce data system to inform and improve the effectiveness of future high impact expenditure decisions.

It used an analytical 29-year data file created by the author that links seven already-in-place education and workforce administrative record sources. Relying on the path dependency theory, multi-level mixed-effect logistic and multi-level mixed-effect linear regression models are used to test three hypotheses.

The findings are consistent with the hypotheses: inclusion of the multiple steps along a post-secondary education pathway and prior job histories are both critical to understanding workforce outcomes mechanisms; it takes time for the employment outcome effect to be evident and strong following education attainment.

The study concludes with research limitations and implications for decision makers to call for retaining and investing in administrative records with extended time span coverage, particularly for the already-in-place historical administrative records.

The paper is one of the first to demonstrate the value of extended time span coverage in a longitudinal state integrated data system through econometric modeling, using longitudinally integrated data linking seven administrative records covering continuously for 29 years. No matter for prior education or employment pathway, it is only through extended time span coverage that employment outcomes can be well measured and the rich nuances interpreting the mechanisms of education return on investment can be revealed.

The current research explores the structural elaboration of municipal American police organizations, specifically, the structural complexity of police organizations and its relationship to time. The purpose of this paper is to describe and test essential elements of the structural elaboration hypothesis.

The authors explore the structural elaboration hypothesis utilizing a sample of 219 large police departments across the USA. Data are drawn from multiple waves of the Law Enforcement Management and Administrative Statistics survey and are analyzed using tobit and OLS regression techniques.

While there is some evidence that police departments are becoming more elaborate, little evidence for the structural elaboration hypothesis as a function of time is found.

This project is the first to specifically explore the structural elaboration hypothesis across multiple time points. Additionally, results highlight structural trends across a panel of large American police organizations and provide potential explanations for changes. Suggestions for large-scale policing data collection are also provided.

The purpose of this paper is to provide international data on the occurrence (and rates) of clinical errors, identified by type and consequence in the Lombardy region, and to assess empirically the association between hospital accreditation‐type measures and clinical error rates by merging hospital discharge records and medical malpractice claim data in the Lombardy region (Italy).

Data were drawn from the regional database collecting claims and demands for reimbursement declared by patients hospitalized in regional healthcare structures and regional archives collecting hospital discharge records. To model the variability of clinical errors rates, binomial negative regression models were applied. For improved interpretation of the results, a regression tree methodology was used.

The results demonstrated that the rate of readmission for the same major diagnostic category and the rate of discharges against medical advice significantly affect the incidence of errors causing patient death, whereas the rate of unscheduled surgical readmission in the operating room significantly affects the rate of surgical error.

The findings confirm that claims data is problematic in nature because of the limited number of claims generally emerging from administrative sources. The article proposes using proper regression models for count data, taking into account over‐dispersion and excess zeroes and classification tree methods for a better interpretation of empirical evidence.

Health structures where quality outcomes have a significant impact on clinical error rates should be monitored in depth, investigating the medical charts of involved patients to identify quality problems and problematic areas.

As a risk management strategy, the combined use of claims data and clinical administrative data is proposed to shed light on the more problematic, error‐prone areas, allowing regional stakeholders to receive relevant, highly cost‐effective and timely information and an in‐depth understanding of the problematic areas in the assessment of risk.

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.

This paper aims to present the IZA Evaluation Dataset, a newly created data source allowing for the evaluation of active labor market policies in Germany.

The paper's approach is a description of the sampling and contents of the IZA Evaluation Dataset and an outline of its research potential.

The evaluation of active labor market policies is often confronted with a lack of adequate empirical data. The IZA Evaluation Dataset may serve as a role model for the provision of such data.

The scope of active labor market policy instruments that can be analyzed with the IZA Evaluation Dataset is mainly restricted to measures for unemployed individuals.

In recent years, many countries have opened their administrative databases for evaluation studies. However, information that might be relevant for economic modeling is often absent. The IZA Evaluation Dataset aims to overcome such limitations for Germany by complementing administrative data from the Federal Employment Agency with innovative survey data.

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.

This paper aims to share the contribution of records managers to the Open Government in the City Council of Girona (Catalonia), based on the strategy of participation in the Open Data project.

The contribution of the Records Management Department is to facilitate locating data sets and to be responsible for the data selection processes. Records Management allows a first global identification of the functions, activities and producers, using the Records Classification Scheme and the General Register of Case Files. In addition, it can obtain a greater detail of information using the Application Catalogue and the Register of Personal Data Files.

Records Management can contribute to data selection in the Open Data projects by the appraisal of data, also taking into consideration functions and records of the organization. At the same time, Open Data projects could reinforce the strategies of records preservation in databases, because the same extraction processes could be shared. In addition, Open Data projects could mean an opportunity for Historical Archives if they take charge of the maintenance of the Open Data Archive. This could be relevant in the future for the transformation of data from information systems into documentary heritage.

The opening of the city’s Open Government portal is scheduled for the beginning of 2014. Thus, it is necessary to consider this work as a work in progress.

This work is a specific case study of the contribution of Records Management to the Open Data projects; its proposals and reflections have been made from a very practical point of view.

As useful as police data have been in furthering our knowledge of gangs and gang violence networks, not everything about gang networks can be learned from examining police data alone. There are numerous alternative sources of data that already exist on gang networks and some that can be developed further. This study aims to introduce existing research on social networks and gangs with a specific focus on prisons and schools.

This study reviews the existing empirical literature on gang networks in schools and prison settings and use the broader literature on social networks and crime to propose directions for future research, including specific suggestions on data collection opportunities that are considered to be low-cost; that is, strategies that simply make use of existing administrative records in both settings, instead of developing original data collection procedures.

The author found the existing literature on each of these settings to be quite limited, especially when the spotlight is put specifically on gang networks. These shortcomings can be addressed via low-cost opportunities for data collection in each of these settings, opportunities that simply require the network coding of existing administrative records as a foundation for gang network studies.

Investing in these low-cost network data collection activities have the potential for theoretical and empirical contributions on our understanding of gang networks, and may also bring value to practitioners working in school and prison settings as a guide for network-based planning or interventions.

The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.

This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.

This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.

This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.

This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

Presents an investigation of the perennial problem of collecting valid empirical data on police vehicle pursuits, an organizationally sensitive and often controversial behavior, through a new data collection strategy using police emergency radio transmissions. Analyzes taped vehicle pursuits recorded on the Illinois State Police Emergency Radio Network and codes them for content as a data source on police vehicle pursuits. Compares these radio‐transmission data with more conventional pursuit data collection methods, e.g. administrative/official data, elicited‐pursuit‐reporting‐form data, and officer‐self‐report data. Evaluates these as an alternative or supplemental data window for empirically studying the incidence and content of police vehicle pursuits. While some differences appear, results from emergency‐channel radio transmission data largely converge with earlier findings from more conventional data collections. Divergent findings, which are few, appear to be largely the artifacts of different samplings of pursuits that the different data collection methods yield, rather than a result of differential validity.