Abstract:

Purpose: This study interrogates the transformative impact of Library Carpentry and data-intensive methodologies on the field of Library and Information Science (LIS). By bridging the gap between traditional librarianship and modern data science, the research explores how open-source tools like OpenRefine can optimise data management, streamline discovery services, and uphold the fundamental tenets of Ranganathan’s Five Laws of Library Science.

Methodology: The research employs a data carpentry-based approach to analyse trends in social science research, specifically within the Indian context from 2001 to 2025. It evaluates the utility of OpenRefine as a discovery tool for fetching, extracting, and reconciling datasets. Furthermore, the study explores a novel interdisciplinary recruitment framework for LIS professionals by integrating psychological personality traits with statistical performance metrics.

Findings: The findings indicate that the adoption of library carpentry significantly enhances the efficiency and accuracy of library services, particularly in the domain of Open Access (OA) resource harvesting. While the research demonstrates that these tools effectively "save the time of the reader," it also identifies critical barriers, including a lack of specialised technical expertise, inadequate internet infrastructure, and high energy consumption required for large-scale data processing.

Originality/ Value: This research pioneers a unique territory by connecting LIS with data science and psychology. It provides a strategic roadmap for libraries transitioning from legacy OPAC systems to sophisticated, centralised discovery interfaces. The study concludes that the future of LIS lies in the evolution of "librarian-as-coder," where technical data skills become essential for navigating the changing global information landscape.

Keywords:

Library Carpentry, Data Carpentry, OpenRefine, Ranganathan’s Laws, Data Wrangling, LIS Education, Open Access, Information Retrieval

Aim/Objectives

Methods