An Examination Of The Use Of Deep Learning For Identifying Data Curation Activities: A Comprehensive Analysis

Abstract

In the field of data science and machine learning, data plays a crucial role as the fundamental resource that drives the algorithms, models, and insights that underpin contemporary technology. Nevertheless, it is imperative to acknowledge that the quality and trustworthiness of data cannot be assumed. This is the point at which the practice of data curation becomes relevant. Data curation refers to the systematic procedure of gathering, refining, arranging, and up keeping datasets to guarantee their precision, comprehensiveness, and suitability for analysis. While it may not possess the same level of allure as the training of sophisticated deep learning models, it is unquestionably one of the most crucial stages in the data science workflow. The primary aim of this study is to provide a thorough examination of the application of deep learning techniques in the identification of data curation activities. This study utilizes a qualitative research methodology. Through this study, Data Curation is a persistent issue that requires innovative solutions to effectively manage the growing big data environment. Deep Learning is increasingly being adopted in several fields, encompassing both computer science and other domains. The convergence of these two fields will initiate a sequence of research endeavours resulting in practical solutions for numerous Data Curation challenges.