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This paper explores trends over time in library staffing and staffing expenditures among two- and four-year colleges and universities in the United States.

Researchers merged and analyzed data from 1996 to 2016 from the National Center for Education Statistics for over 3,500 libraries at postsecondary institutions. This study is primarily descriptive in nature and addresses the research questions: How do staffing trends in academic libraries over this period of time relate to Carnegie classification and institution size? How do trends in library staffing expenditures over this period of time correspond to these same variables?

Across all institutions, on average, total library staff decreased from 1998 to 2012. Numbers of librarians declined at master’s and doctoral institutions between 1998 and 2016. Numbers of students per librarian increased over time in each Carnegie and size category. Average inflation-adjusted staffing expenditures have remained steady for master's, baccalaureate and associate's institutions. Salaries as a percent of library budget decreased only among doctoral institutions and institutions with 20,000 or more students.

This is a valuable study of trends over time, which has been difficult without downloading and merging separate data sets from multiple government sources. As a result, few studies have taken such an approach to this data. Consequently, institutions and libraries are making decisions about resource allocation based on only a fraction of the available data. Academic libraries can use this study and the resulting data set to benchmark key staffing characteristics.

The purpose of this paper is to present the full range of choices that academic institutions presently have for attending to educational results.

The approach takes the form of a systematic comparison of the eight models currently available to colleges and universities for attending to educational results, relative to four necessary organizational purposes: individual student improvement, individual student accountability, organizational improvement, and organizational accountability.

This is a time of innovation, not of standardization. As new choices become available, the standard for accountability for educational results continues to rise.

The choices, ranging from established practices to expected alternatives to unexpected innovations, differ significantly in their capacities.

Postdocs make up a significant portion of the biomedical workforce. However, data about the postdoctoral position are generally scarce, and no systematic study of the landscape of individual postdoc salaries in the USA has previously been carried out. The purpose of this study was to assess actual salaries for postdocs using data gathered from US public institutions; determine how these salaries may vary with postdoc title, institutional funding and geographic region; and reflect on which institutional and federal policy measures may have the greatest impact on salaries nationally.

Freedom of Information Act Requests were submitted to US public universities or university systems containing campuses with at least 300 science, engineering and health postdocs, according to the 2015 National Science Foundation’s Survey of Graduate Students and Postdoctorates in Science and Engineering. Salaries and job titles of postdocs as of December 1, 2016, were requested.

Salaries and job titles for nearly 14,000 postdocs at 52 US institutions around December 1, 2016, were received. Individual postdoc names were also received for approximately 7,000 postdocs, and departmental affiliations were received for 4,000 postdocs. This exploratory study shows evidence of a postdoc gender pay gap, a significant influence of job title on postdoc salary and a complex relationship between salaries and the level of institutional National Institutes of Health/NSF funding.

These results provide insights into the ability of institutions to collate and report out annualized salary data on their postdocs, highlighting difficulties faced in tracking and reporting data on this population by institutional administration. Ultimately, these types of efforts, aimed at increasing transparency regarding the postdoctoral position, may lead to improved support for postdocs at all US institutions and allow greater agency for postdocs making decisions based on financial concerns.

An inherent challenge within the United Arab Emirates (UAE) higher education sector is the absence of both comprehensive system of data collection and consistency in the use of indictors among a variety of collection projects. At the root of this distributed system is a federal arrangement with two levels of government potentially involved in licensing and supervision combined with a series of academic “free zones” that can have unique or limited regulatory controls. As a result, there is a very limited systematic collection of institutional data in the country’s dynamic higher education sector, which hampers the alignment of planning activities and reduces the ability of institutions to benchmark performance with peers. To address this issue, an ambitious attempt to consolidate higher education data collection in the UAE is being developed by the Ministry of Higher Education and Scientific Research through the creation of Centre for Higher Education Data and Statistics (CHEDS). Combining the best international practices and an inclusive stakeholder-focused approach, CHEDS designed a system to collect raw data from institutions and then convert this data into a set of indicators with the potential for distribution to the public. The critical element for developing a truly comprehensive system is the degree to which international branch campuses of foreign institutions voluntarily participate, for these institutions tend to be located in the free zones and are therefore outside the jurisdiction of the central government ministry overseeing the CHEDS. If successful in recruiting these institutions, CHEDS has the potential to create a truly cooperative system of data collection that should be regionally replicated or even expanded to encompass other countries within the Gulf Cooperation Council.

The purpose of this paper is to assess such processing of personal data for identification purposes from the point of view of the principle of data minimisation, as set out in the EU’s General Data Protection Regulation (GDPR) and examine whether the processing of personal data for these purposes can be considered proportionate, i.e. whether it is performed for the purposes defined and only as much as is necessary.

In this paper, the authors discuss and present the relevant legal regulation and examine the goals and implementation of such regulation in Lithuania. This paper also examines the conditions for the lawful processing of personal data and their application for the above-mentioned purposes.

This paper addresses the problem that, on the one hand, financial institutions must comply with the objectives of collecting as much personal data as possible under the AML Directive (this practice is supported by the supervisory authority, the Bank of Lithuania), and, on the other hand, they must comply with the principle of data minimisation established by the GDPR.

Financial institutions process large amounts of personal data. These data are processed for different purposes. One of the purposes of processing personal data is (or may be) related to the prevention of money laundering and terrorist financing. In implementing the Know Your Customer principle and the relevant legal framework derived from the EU AML Directive, financial institutions collect various data, including projected account turnovers, account holders' relatives involved in politics, etc.

In today's global and competitive corporate environment characterised by rapidly changing information, knowledge and technology (IKT), researchers must be upskilled in all aspects of research data management (RDM). This study investigates a set of capabilities and competencies required by researchers at selected South African public universities, using the community capability model framework (CCMF) in conjunction with the digital curation centre (DCC) lifecycle model.

The post-positivist paradigm was used in the study, which used both qualitative and quantitative methodologies. Case studies, both qualitative and quantitative, were used as research methods. Because of the COVID-19 pandemic rules and regulations, semi-structured interviews with 23 study participants were conducted online via Microsoft Teams to collect qualitative data, and questionnaires were converted into Google Forms and emailed to 30 National Research Foundation (NRF)-rated researchers to collect quantitative data.

Participating institutions are still in the initial stages of providing RDM services. Most researchers are unaware of how long their institutions retain research data, and they store and backup their research data on personal computers, emails and external storage devices. Data management, research methodology, data curation, metadata skills and technical skills are critically important RDM competency requirements for both staff and researchers. Adequate infrastructure, as well as human resources and capital, are in short supply. There are no specific capacity-building programmes or strategies for developing RDM skills at the moment, and a lack of data curation skills is a major challenge in providing RDM.

The findings of the study can be applied widely in research, teaching and learning. Furthermore, the research could help shape RDM strategy and policy in South Africa and elsewhere.

The scope, subject matter and application of this study contribute to its originality and novelty.

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.

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data.

Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways.

Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs.

The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs.

Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience.

The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots.

The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions.

The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.

The Higher Education Funding Councils in the UK are working with higher education institutions (HEIs) to develop a reliable, accurate and relevant set of Key Estate Ratios (KERs). A successful pilot study was carried out in 1998/99, with a national project ‐ including full data collection from all HEIs ‐ currently under way. The project has developed 14 ‘critical’ estate ratios for senior managers, which have been endorsed by the sector. The current national project has collected data from all HEIs for the last two years to enable the 14 KERs to be produced for each institution. In addition, the project has refined certain of the definitions which have been subject to consultation with the sector. The KERs ‐ and supporting data ‐ have been welcomed by senior managers and are being used actively across the sector. The structure of the underlying system, and the results that emerge, have considerable potential benefits to other property sectors.