Peer-Reviewed · Triannual

Journal of Data Science, Informetrics, and Citation Studies

Peer-Reviewed · Open Access

Journal of Data Science, Informetrics, and Citation Studies (J Data Sci. Info. Citation Studies) is an open-access journal that publishes peer-reviewed articles online. It is peer-reviewed in a double-blind manner. The journal welcomes empirical and theoretical contributions that contribute to the advancement of research in Data Science, Data Analytics, Policy Studies, Scientometrics, Informetrics, Bibliometrics, Altmetrics, Webometrics, and Research Evaluation.

Editorial board

Dr. Anup Kumar Das
Editor-in-Chief
Room No. 228, CSSP, SSS1 Building, Jawaharlal Nehru University, New Delhi 110067, India
Dr. Satishkumar V E
Editor
Department of Computing and Information Systems, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia
Dr. Swapan Kumar Patra
Editor
Tshwane University of Technology, South Africa; and Sidho-Kanho-Birsha University, Purulia-723104, West Bengal, India
Dr. Ayesha Choudhary
Editorial Board Member
Assistant Professor, School of Computer & System Sciences, Jawaharlal Nehru University, New Delhi-110067, India
Dr. Kishor Chandra Satpathy
Editorial Board Member
Chief Librarian, Indian Statistical Institute, 203, B T Road, Kolkata-700108, West Bengal, India
Prof. Waltraut Ritter
Editorial Board Member
Director Research, Knowledge Dialogues, Germany
Dr. Siddhartha Roy
Editorial Board Member
Assistant Professor and Head of the Computer Science Department, The Heritage College, University of Calcutta, Kolkata-700107, West Bengal, India
Dr. Rabishankar Giri
Editorial Board Member
Assistant Librarian, Presidency University, Kolkata-700073, West Bengal, India
Dr. Chaman Sab M
Editorial Board Member
Head, Library and Information Center, A.R.G. College of Arts and Commerce, P.J. Extension, Davanagere-577004, Karnataka, India
Mr. Mallikarjun Kappi
Editorial Board Member
Head, Library and Information Centre, Government First Grade College, Jagalur, Davanagere-577528, Karnataka, India
Dr. Prathik Anandhan
Editorial Board Member
Assistant Professor, School of Computer Science, Veltech University, Chennai-600062, Tamil Nadu, India
Dr. Krishnamoorthy N
Editorial Board Member
Department of Computer Science and Engineering, Kongu Engineering College, Perundurai-638052, Tamil Nadu, India

Editors' Choice · Review Article

Artificial Intelligence and Data Science in Academic Libraries: Framework, Competencies, and Adoption Challenges among Library Professionals

Kayode Sunday John Dada

The accelerating integration of Artificial Intelligence (AI) and data science into academic knowledge systems has created an urgent imperative for library professionals to reassess their competency frameworks, service models, and institutional readiness. Drawing conceptually on the International Federation of Library Associations and Institutions (IFLA) Big Data Special Interest Group framework and synthesising evidence from Q1-ranked, Elsevier, and Scopus-indexed publications (2018-2025), this paper examines the theoretical foundations and practical dimensions of AI and data science adoption in academic libraries. Three research questions guided the study: the level of AI and data science awareness among library professionals; the nature of data science services currently offered or feasibly deployable in academic libraries; and the key barriers to adopting AI-driven services. A conceptual-analytical research design was employed, integrating systematic literature synthesis with a structured competency mapping exercise. Findings reveal that while foundational data science awareness is growing, significant skills gaps persist- particularly in library analytics, text and data mining, and AI-driven patron services. A hypothesis is advanced that academic librarians' digital literacy competency levels are significantly and positively associated with their institutional readiness to adopt AI and data science frameworks. The paper concludes with a multi-dimensional adoption framework organised around three pillars: Skills, Operations, and Services, and proposes evidence-based recommendations for embedding data science into the professional DNA of librarianship.

Read Full Text PDFDOI: 10.5530/jcitation.20260001