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Librarians play a critical role in curating, organizing and facilitating access to open data (OD) resources, supporting research, learning and information dissemination. This study aims to explore the use of OD for reference services delivery in academic libraries. It is believed that the concept of OD in librarianship refers to the practice of making publicly available data freely accessible, usable and shareable by anyone.

This research examined how the utilization of OD may affect library services and operations. This paper used the systematic review of literature to answer the research questions; hence, it was a desk study.

Given that there has been recently a research shift in linking OD to library metadata, this study revealed some interesting findings. More specifically, it discovered that incorporating OD into reference services can provide several benefits and enhance the quality and relevance of the information provided to library users. In addition, this paper reported that OD enriches reference services by providing comprehensive, current and interdisciplinary information, supporting evidence-based research, enabling data visualization and analysis, fostering community engagement and promoting innovation.

This paper proposes that libraries that possess extensive collections are in a favorable position to embrace the shift toward becoming OD and open big data libraries. It is also believed that academic libraries, through reference services, meet the challenge of providing access to their institutional repositories by holding many sets of data in various formats and providing various interfaces, developing metadata systems for various data streams, supporting all ecosystems of software and data products for reformatting and reusing large and complex data, and support for data sets identifiers. By actively engaging with OD, librarians can play a vital role in facilitating access to information and promoting the use of open knowledge for academic pursuits.

The purpose of the paper is to report on the presentations and discussions from The Evolution of Data conference.

Conference report.

Review of presentations about research data projects and initiatives that address issues throughout the data lifecycle.

The conference report captures a unique discussion among various different professionals and organizations around current efforts and needs for research data infrastructure and support. This one‐time conference presents a current snapshot of the field.

This study aims to examine research visualization trends in research data management (RDM), analyzing factors such as contributions, publications, document types, authors and research areas, emphasizing the dynamic nature of RDM research in the scholarly landscape.

The study analyzed citation histories for 1,401 publications from 2001 to 2021 in the Web of Science database, extracting no restrictions on document type or language. Literature visualization tools such as Biblioshiny, VOSviewer, ScientoPy and MS Excel were used. The researchers explored institutional collaborations, data-centric trends and RDM frontiers.

The majority of RDM research is conducted by librarians and information scientists. Research on RDM has increased over the past 21 years, peaking in 2019. Among universities, Sheffield and Pittsburgh have the most productivity in RDM research, and the USA is the most productive country. Most productive authors are Aleixandre-Benavent-R and Da Silva Jr. RDM; however, improvement is still needed, especially at academic universities.

This study provides valuable insights into the published literature on RDM and identifies patterns of collaboration among researchers in RDM.

The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.

As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.

After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.

Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

This study aims to examine the current status and different characteristic features of research data repositories established by BRICS nations in order to understand the research data infrastructure within BRICS countries.

The data were collected from the re3data repository (http://www.re3data.org/), focusing specifically on BRICS nations. The data were analyzed to grasp the current status of research data repositories in BRICS countries. The dataset was retrieved on March 2, 2024. A total of 195 Research Data Repositories (RDRs) originating from BRICS countries were identified and selected for comprehensive analysis.

As of March 2, 2024, re3data.org indexes a total of 3,192 Research Data Repositories (RDRs) worldwide, with BRICS nations contributing 195. China leads among BRICS nations, followed by India, Russia, and Brazil. Scientific and Statistical Formats are the most shared content categories, followed closely by Standard Office Documents. There is notable diversity in the subjects covered by RDRs across BRICS nations. English is the primary interface language, followed by Chinese and Portuguese. “House, tailor-made” software is widely used for creating RDRs, followed by Dataverse and DSpace. Fourteen metadata standards are found, with Dublin Core metadata being the most prevalent, followed by the DataCite Metadata Scheme. Most repositories are disciplinary, followed by institutional ones. Most repositories specify data upload types, with “restricted” being the most common, followed by closed types. Open access is predominant in data access, followed by restricted access and embargo periods, while a small number restrict access entirely.

The present study will help gauge the strengths and weaknesses of the RDRs of BRICS nations and also learn how open these RDRs are for data access and upload provisions. The study contributes to a broader comprehension of the accessibility and usability of research data within the BRICS community, ultimately fostering greater transparency, collaboration, and knowledge dissemination within the scientific community, thus fostering greater innovation and advancement in research endeavors. The study provides a nuanced understanding of the research data infrastructure within BRICS nations, highlighting key trends, strengths, and areas for potential improvement.

The increasingly active data practice in academic environments makes investigating college faculty users’ potential needs for library data services (LDS) essential. Guided by a conceptual framework rooted in the data lifecycle and the extended technology acceptance model, this study aims to investigate the relationship between faculty’s data engagement (DE) and their attitudes toward multiaspect LDS.

An online survey at a master’s college was conducted to collect data regarding faculty data practice, potential needs for data services (DS) and attitudes toward multiaspect LDS. Based on 139 complete and valid responses, the study built three conceptual models to demonstrate faculty users’ potential acceptance of LDS for research and teaching.

Participants’ research and teaching-related DE and background factors directly or indirectly affect their attitudes toward general DS, an institutional data repository if available and repository-based data curation.

The study contributes to DS and librarianship research by offering three conceptual models to explore LDS’ holistic support for faculty research and teaching. Moreover, the study provides insights into faculty’s job-related DE factors and calls for future research on effective DS in more college communities.

As libraries evolve, they accommodate and refine their services to support the varied institutional, student and faculty, and course needs, including developing workshops tailored to course requirements or that provide students and faculty with additional skills that promote their academic pursuits. Some services provided by academic libraries reflect strategic choices that promote the increased alignment of the users’ needs with the institution’s needs. Some needs anticipated and observed by many research-intensive institutions are data literacy, research, and software skills. This chapter describes the case of the academic library at the University of Nevada, Las Vegas (UNLV), an R1 research institution, which offered the Data Workshop Series (DWS) to help prepare students, faculty, and staff to clean, manipulate, analyze, and visualize research data. This applied, student-centered technical workshop series was guided by authentic assessment, specifically performance tasks, which were employed to ensure the participants’ engagement and comprehension of the applied techniques presented. The performance tasks also helped participants gain confidence in their data skills. From them, the participants learned that they can use the software and solve questions on their own. When reflecting on what they learned in the DWS, participants stated that they could develop their skills on their own with additional practice and that they plan on integrating the software into their academic work. By offering the DWS, UNLV Libraries has taken a step toward being part of the life of the user, a partner in more than information findings, but in knowledge creation.

This chapter provides an overview of the specific legal, ethical, and privacy issues that can arise when conducting research using Twitter data. Existing literature is reviewed to inform those who may be undertaking social media research. We also present a number of industry and academic case studies in order to highlight the challenges that may arise in research projects using social media data. Finally, the chapter provides an overview of the process that was followed to gain ethics approval for a Ph.D. project using Twitter as a primary source of data. By outlining a number of Twitter-specific research case studies, the chapter will be a valuable resource to those considering the ethical implications of their own research projects utilizing social media data. Moreover, the chapter outlines existing work looking at the ethical practicalities of social media data and relates their applicability to researching Twitter.

Big Data analysis is one of the key challenges to the provision of health care to emerge in the last few years. This challenge has been spearheaded by the huge interest in the “4Ps” of health care (predictive, preventive, personalized, and participatory). Big Data offers striking development opportunities in health care and life sciences. Healthcare research is already using Big Data to analyze the spatial distribution of diseases such as diabetes mellitus at detailed geographic levels. Big Data is also being used to assess location-specific risk factors based on data of health insurance claims. Other studies in systems medicine utilize bioinformatics approaches to human biology which necessitate Big Data statistical analysis and medical informatics tools. Big Data is also being used to develop electronic algorithms to forecast clinical events in real time, with the intent to improve patient outcomes and thus reduce costs.

Yet, this Big Data era also poses critically difficult ethical challenges, since it is breaking down the traditional divisions between what belongs to public and private domains in health care and health research. Big Data in health care raises complex ethical concerns due to use of huge datasets obtained from different sources for varying reasons. The clinical translation of this Big Data is thus resulting in key ethical and epistemological challenges for those who use these data to generate new knowledge and the clinicians who eventually apply it to improve patient care.

Underlying this challenge is the fact that patient consent often cannot be collected for the use of individuals’ personal data which then forms part of this Big Data. There is also the added dichotomy of healthcare providers which use such Big Data in attempts to reduce healthcare costs, and the negative impact this may have on the individual with respect to privacy issues and potential discrimination.

Big Data thus challenges societal norms of privacy and consent. Many questions are being raised on how these huge masses of data can be managed into valuable information and meaningful knowledge, while still maintaining ethical norms. Maintaining ethical integrity may lack behind in such a fast-changing sphere of knowledge. There is also an urgent need for international cooperation and standards when considering the ethical implications of the use of Big Data-intensive information.

This chapter will consider some of the main ethical aspects of this fast-developing field in the provision of health care, health research, and public health. It will use examples to concretize the discussion, such as the ethical aspects of the applications of Big Data obtained from clinical trials, and the use of Big Data obtained from the increasing popularity of health mobile apps and social media sites.

This chapter explores the ethical complexities of researching location-aware social discovery Smartphone applications (apps) and how they mediate contemporary experiences of travel. We highlight the context-specific approach required to carrying out research on Tinder, a location-aware app that enables people to connect with others in close proximity to them. By journeying through the early stages of our research project, we demonstrate how ethical considerations and dilemmas began long before our project became a project. We discuss the pulls toward data extraction/mining of user-generated content (i.e., Tinder user profiles) within digital social research and the ethical challenges of using this data for research purposes. We focus particularly on issues of informed consent, privacy, and copyright, and the differences between manual and automated data mining/extraction techniques. Excerpts from our university ethics application are included to demonstrate how our research sits uneasily within standardized ethical protocols. Our moves away from a ‘big data’ approach to more ‘traditional’ and participatory methodologies are located within questions of epistemology and ontology including our commitment to practicing a feminist research ethic. Our chapter concludes with the lessons learned in the aim to push forward with research in challenging online spaces and with new data sources.