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This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services.

A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data.

LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills.

The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others.

This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.

The purpose of this study was to establish the current skills base of librarians working in research data management services in academic and research libraries in South Africa. The purpose was also to determine the relevance of courses and programmes that are currently being offered by library and information studies programmes in response to the needs of research data management services and make recommendations on curriculum improvement.

About 13 institutions which were considered early adopters of research data management services were identified as participants in an online survey. In addition, a review of Web pages of existing library and information studies schools was carried to establish courses that would support research data management services. Data collected through the two approaches were analysed and presented quantitatively and qualitatively.

The findings reveal an environment in a developmental stage, with limited skilled personnel to run research data management services. The findings also show an absence of specific data librarianship courses within existing library and information studies programmes and a very limited scope for the full range of data management courses within professional development programmes.

The paper provides information on approaches to further develop existing curriculum and contribute to the data management needs and support governments, funders and publishers' requirements for the discoverability and re-use of research data across research domains.

As an emerging discipline, data science represents a vital new current of school of library and information science (LIS) education. However, it remains unclear how it relates to information science within LIS schools. The purpose of this paper is to clarify this issue.

Mission statement and nature of both data science and information science are analyzed by reviewing existing work in the two disciplines and drawing DIKW hierarchy. It looks at the ways in which information science theories bring new insights and shed new light on fundamentals of data science.

Data science and information science are twin disciplines by nature. The mission, task and nature of data science are consistent with those of information science. They greatly overlap and share similar concerns. Furthermore, they can complement each other. LIS school should integrate both sciences and develop organizational ambidexterity. Information science can make unique contributions to data science research, including conception of data, data quality control, data librarianship and theory dualism. Document theory, as a promising direction of unified information science, should be introduced to data science to solve the disciplinary divide.

The results of this paper may contribute to the integration of data science and information science within LIS schools and iSchools. It has particular value for LIS school development and reform in the age of big data.

The paper presents a literature review on research data management services in African academic and research libraries on the backdrop of the advancing open science and open research data infrastructures. It provides areas of focus for library to support open research data.

The literature analysis and future role of African libraries in research data management services were based on three areas as follows:open science, research infrastructures and open data infrastructures. Focussed literature searches were conducted across several electronic databases and discovery platforms, and a qualitative content analysis approach was used to explore the themes based on a coded list.

The review reports of an environment where open science in Africa is still at developmental stages. Research infrastructures face funding and technical challenges. Data management services are in formative stages with progress reported in a few countries where open science and research data management policies have emerged, cyber and data infrastructures are being developed and limited data librarianship courses are being taught.

The role of the academic and research libraries in Africa remains important in higher education and the national systems of research and innovation. Libraries should continue to align with institutional and national trends in response to the provision of data management services and as partners in the development of research infrastructures.

The purpose of this paper is to describe a new approach to education for library/information students in data literacy – the principles and practice of data collection, manipulation and management – as a part of the Masters programmes in library and information science (CityLIS) at City, University of London.

The course takes a socio-technical approach, integrating, and giving equal importance to, technical and social/ethical aspects. Topics covered include: the relation between data, information and documents; representation of digital data; network technologies; information architecture; metadata; data structuring; search engines, databases and specialised retrieval tools; text and data mining, web scraping; data cleaning, manipulation, analysis and visualisation; coding; data metrics and analytics; artificial intelligence; data management and data curation; data literacy and data ethics; and constructing data narratives.

The course, which was well received by students in its first iteration, gives a basic grounding in data literacy, to be extended by further study, professional practice and lifelong learning.

This is one of the first accounts of an introductory course to equip all new entrants to the library/information professions with the understanding and skills to take on roles in data librarianship and data management.

The purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library.

The research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step.

The HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines.

This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation.

The purpose of this paper is to address the use of librarians as a study population in social science research outside of the field of library and information science. Additionally, it aims to make general claims about how frequently librarians have been studied compared to other occupations, as well as to identify and describe existing research that has used librarians as a study population.

The described study had two parts. Two social science databases were searched using the subject headings “librarians” and eight additional occupations, and the results for all nine occupations were analyzed. The peer-reviewed results for “librarians” were then coded by content. The articles that used librarians as a study population were identified, reviewed and described.

Although librarians, as an occupational group, possess many characteristics that should make them an ideal choice for social science research, they seem to be studied less frequently than other occupations.

Other occupational groups, such as mathematicians, were also studied infrequently. Further research might consider, more broadly, why some occupations are studied more frequently than others. Future studies might also compare librarianship to other female-dominated professions, such as nursing and education. Additionally, the subject heading “librarians” was applied to articles that studied non-professional library employees, making it difficult to isolate only articles with a focus on degreed librarians.

Few other studies have examined social science research in which librarians are used as the study population. By focusing on how librarians are studied and written about in other fields, this paper will add to the body of literature on the professional image of librarians.

The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.

Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.

A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.

Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.

The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.

This paper presents results of a study on data managers' perspectives on the evolution of Designated Communities and the FAIR Principles using an example of a geological repository.

The study employed 10 semi-structured interviews with data managers at a state geological survey and qualitative analysis of the interview transcripts.

The Designated Community for a collection in this data repository has evolved from petroleum industry users to include academic researchers and the public. This change was accompanied by significant user interaction changes from in-person, reference interview-style conversations to anonymous digital, automated interactions. The main factors driving these changes were developments in technology which allowed the data managers to shift data discovery and access into the online environment. The online data portal has seen increasing non-expert use, driving the data team to develop additional services for these new communities. Repository data team participants varied in their familiarity with the FAIR Principles and their perceptions of the FAIRness of the data in the repository.

The study was limited to one organization in the United States. However, the results are applicable to other data environments working through the tensions between high-level global frameworks such as FAIR, and continuing to serve the day-to-day needs of their designated communities. Continued work on how to assess success in this complex space is needed.

This paper lies at the nexus of two digital preservation frameworks and contributes to a limited extant literature providing guidance on implementing the concept of a Designated Community in practice.