Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research

• Main Authors: K. A．, Nur Dalila, Jusoh, Mohamad Huzaimy, Mashohor, Syamsiah, Sali, Aduwati, Yoshikawa, Akimasa, Kasuan, Nurhani, Hashim, Mohd Helmy, Hairuddin, Muhammad Asraf
• Format: Article
• Published: Elsevier BV 2023
• Online Access: http://psasir.upm.edu.my/id/eprint/106863/; https://linkinghub.elsevier.com/retrieve/pii/S2352340923007527
• Institution: Universiti Putra Malaysia

The field of space weather research has witnessed growing interest in the use of machine learning techniques. This could be attributed to the increasing accessibility of data, which has created a high demand for investigating scientific phenomena using data-driven methods. The dataset, which is based on bibliographic records from the Web of Science (WoS) and Scopus, was compiled over the last several decades and discusses multidisciplinary trends in this topic while revealing significant advances in current knowledge. It provides a comprehensive examination of trends in publication characteristics, with a focus on publications, document sources, authors, affiliations, and frequent word analysis as bibliometric indicators, all of which were analysed using the Biblioshiny application on the web. This dataset serves as the document profile metrics for emphasising the breadth and progress of current and previous studies, providing useful insights into hotspots for projection research subjects and influential entities that can be identified for future research.