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Because of online digital resources, academic libraries no longer need to spend as much time and energy organizing their own collections as they used to. They now have an opportunity to pivot their expertise in organizing information outward. “Inside-out” library services can include support for special collections, digital scholarship, scholarly communication, and data management. A key characteristic of such services is that an academic library takes on broader information management challenges at their college or university. This chapter will examine what it takes to build successful inside-out library services by looking at their cost, how well they complement existing library expertise and culture, and their impact on teaching, research, and the wider community.

The purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use.

The checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data.

Over the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use.

While some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines.

Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

The purpose of this paper is to identify details of technological, organizational and people (TOP) factors affecting maintenance technicians’ use of computerized maintenance management systems (CMMS) in manual collection of asset data.

In addition to TOP factor details, results from six case studies in Finland, India and the Caribbean are presented. Interviews and observations clarify the role of TOP factors in CMMS use in industrial maintenance.

In total, 17 detailed TOP factors are identified and criteria for analyzing CMMS contexts with them are defined. Analyzing the cases with these factors reveals that technicians who collect good quality data have received good training and instructions for the CMMS, are competent, and understand how manually collected data benefits them in their own work. However, even these sites struggle with the usability of the CMMS.

The 17 TOP factors and the criteria for CMMS evaluation extend understanding on context and usability in manual data collection. Case study method does not imply the relative importance of the TOP factors, which calls for future research using quantitative methods.

Management can use the criteria to analyze the context of manual data collection for improvements, e.g., in CMMS usability.

Insights from industrial environments and a new way of studying contextual factors of CMMS use are presented. The results extend a data quality research framework with details to manual data collection and define the TOP factors in CMMS context.

Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

A survey of the current state of documentation practice in museums is presented. This concentrates on the broad themes of the practice, making comparisons with analogous library procedures, where appropriate. A brief introduction to museums and their organizational framework within the United Kingdom is given. With this as background, the methods of documentation used by museums are reviewed, and a survey presented of current developments on an international and national scale.

Why create another collection assessment tool? One reason is the highly personal nature of the assessment process. Each local library describes and evaluates its own collection in relation to the library's goals for meeting the needs of its local clients. In our times of focusing on local needs, each library wants to customize the assessment process to meet local challenges as closely as possible. These local needs are greatly felt in the data collection step of the assessment process. An intermediate tool is helpful for collecting and organizing appropriate categories of data to prepare information for entry onto the WLN Conspectus worksheets and database, or into other assessment methods if desired.

This study aims to examine a comparative analysis of electronic- and paper-based data collection methods.

Survey research approach was adopted to conduct the study. A total of 500 library and information science (LIS) researcher/information professionals selected randomly from seven states in Nigeria represent the sample for the study. A self-designed questionnaire was used to gather data from the respondents. Six objectives were developed to guide the study.

The findings reveal that there is preference for paper-based than the electronic-based data collection methods, and similarly, respondents indicated they cannot use any other data collection methods other than paper-based. Electronic data collection method is beneficial in terms of gathering a representative sample within a short period compared to the paper-based method. The researcher has control over access to the survey in the paper-based method compared to the electronic method, and there is opportunity to monitor the progress of the research and clarify ambiguity by the researcher. The electronic method is limited in terms of collecting data from only the respondents who are computer-literate with good Web skills, while the paper-based method is limited by higher attrition rate, money and time consumption.

The study did not consider the time needed to gather data with each method or the amount of time needed for data entry, cleaning and coding. It is possible that a time-motion study would reveal clear superiority of either one method or the other. The total sample of participants in the study is also limited, considering the total number of LIS researcher/information professionals in Nigeria.

The study has underlined the value of electronic/Web-based collection as a cost- and time-saving instrument in modern LIS research. Regardless of method and trial, electronic data collection could fulfill in all examined parameters, its promises, realizing cost savings of up to a 50 per cent in comparison with the paper-based data collection method.

The paper is solely the idea of the author.

The widespread acceptance of the use of online techniques in market research necessitates appreciation of the relative advantages and disadvantages of these techniques over more traditional research methods. This paper reports on a study which directly compares online and postal data collection methods using the same survey instrument on two samples drawn from the same population of football club subscribers. The results confirm that the online and postal respondents are demographically different. Online data collection is shown to be less expensive per respondent and that data collection is faster, however, an overall lower response level is achieved relative to the postal data collection method. Of greater importance, though, are the findings that respondents seem to answer questions differently online than they do via postal methods. The conclusion here is that online data collection should not be treated as a direct substitute for postal data collection in every instance.