Data Curation: A Perspective of Information Retrieval to Society

Data Curation: A Perspective of Information Retrieval to Society

Musa Dauda Hassan Ph.D.

Technical Support Research Deployment and Enablement, SAP America

DOI: https://doi.org/10.51244/IJRSI.2025.12030040

Received: 23 November 2024; Accepted: 04 December 2024; Published: 06 April 2025

INTRODUCTION

In today’s data-driven world, the ability to effectively manage, organize, and retrieve digital information is critical to societal advancement. The huge and expanding volume of digital content demands effective data curation procedures to assure accessibility, usefulness, and dependability. Information retrieval systems play an important part in this process by organizing data to facilitate knowledge sharing, decision-making, and innovation. This article investigates the importance of data curation, its impact on information retrieval, and its broader consequences for society.

Keywords: Data Curation, Digital Library, and Information Retrieval

UNDERSTANDING DATA CURATION

Data curation is the methodical management and preservation of digital information throughout its lifecycle to ensure its relevance and usability. Beagrie (2006) asserts that digital curation involves the steps necessary to preserve digital research data and other materials for both present and future generations. Shreeves and Cragin (2008) describe data curation as the proactive management of data to guarantee its accessibility and applicability in research, science, and education. Outside of academia, data curation is essential in numerous sectors, such as government, healthcare, business, and public services. The increasing dependence on data for decision-making and innovation requires systematic methods to guarantee information integrity, security, and accessibility (Thirumuruganathan, S., et al. (2020).

Data curation entails numerous essential activities, including data collection from several sources, error detection and correction, data conversion for analysis, and storage for long-term access. A strong structure is required to manage data and ensure it satisfies quality and usability criteria. This framework covers data gathering to assure completeness and consistency, data organization with consistent naming conventions, data validation against set standards, data storage in safe systems with backup plans, and data sharing in accessible formats via online platforms. Kamaldeen Lawal (2025).

Digital curation is the continual management and preservation of digital research data and materials throughout their lifecycle, ensuring long-term accessibility for current and future users. Beagrie,(2006). Shreeves, S.L., and Cragin, M.H. (2008); Yakel, E. (2007). The concept is continually growing, along with related words such as digital preservation and archiving, and its meaning differs across fields. Data is critical to scientific and economic growth, forming the cornerstone of the Data-Information-Knowledge-Wisdom (DIKW) hierarchy described by (Ackoff, Russell L. (1989). Libraries and communities play critical roles in defining standards and procedures to keep data organized, retrievable, and useful for knowledge transmission (Heidorn, 2011).

Similarly, Heidorn (2011) examined the nascent role of libraries in data curation and e-Science, emphasizing their adaptation to the changing digital environment. The paper analyzes the emergence of digital data curation as an essential function for libraries, necessitating the development of innovative strategies, tools, and skills to efficiently handle extensive and intricate datasets. Moreover, Heidorn examines how technological improvements, especially artificial intelligence (AI), are transforming the methods by which libraries manage data management, preservation, and accessibility. AI-driven solutions are progressively incorporated into library systems to automate metadata development, refine search functionality, and augment data retrieval procedures. This transition highlights the increasing significance of libraries in assisting researchers and institutions with digital information management, promoting open access, and guaranteeing the enduring preservation of essential scientific data.

Data Curation and Information Retrieval

Fan et al. (2014) pose significant challenges in large data exploitation and analysis. The evolving nature of data curation has had a considerable impact on information retrieval systems. Traditionally, libraries used metadata, classification algorithms, and keyword searches to organize data. However, modern retrieval systems integrate artificial intelligence, machine learning, and semantic search approaches to improve accuracy, speed, and user pleasure. Gold (2010) defines digital curation as the process of preserving and adding value to digital content for long-term usage. Effective data curation guarantees that information retrieval systems produce relevant, high-quality results that address a variety of social needs. Curated data benefits decision-making processes, research, and economic growth by increasing data organization, retrieval, and accessibility. Furthermore Manu, T. R., & Gala, B., (2021). Discuss the best practices for data curation in research data repositories. the study explore activities in research data repositories as best practice with the implementation of software enabling the research data retrieval and sharing.

Data Curation and Artificial Intelligence

The use of artificial intelligence (AI) into data curation has transformed the way digital material is handled, conserved, and accessible. AI-powered techniques increase efficiency by automating repetitive processes like metadata tagging, data classification, and anomaly detection. Agerfalk (2020) Machine learning algorithms improve information retrieval systems by detecting patterns, optimizing search functionality, and making personalized recommendations. These innovations help researchers, corporations, and politicians gain access to, organize, and use digital data more effectively. AI improves information retrieval by allowing faster and more accurate searches. Traditional approaches depended mainly on metadata and keyword searches, but AI-powered systems use natural language processing (NLP) and deep learning to determine context, relevance, and user intent. This move assures that consumers get more exact and useful answers, which improves decision-making and knowledge distribution across multiple disciplines.

Impact on Society

Data curation aids society by safeguarding knowledge, augmenting research, bolstering public policy, optimizing economic efficiency, and facilitating education. Nonetheless, obstacles such as data saturation, storage constraints, privacy issues, and interoperability necessitate coordination among data curators, information scientists, policymakers, and technology developers. Progress in artificial intelligence, blockchain, and automation presents promising possibilities for enhancing data curation procedures, including AI-driven metadata tagging, automated preservation methods, and decentralized data storage platforms.

CONCLUSION

Data curation is an essential element of information retrieval systems, guaranteeing that digital material stays accessible, organized, and beneficial to society. Effective data curation promotes knowledge distribution and advances societal progress by supporting research, governance, business, and education. As digital ecosystems advance, the necessity for advanced curation approaches will increasingly be vital in optimizing data’s potential for collective benefit.

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