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

• Published in: Data in Brief
• Main Author: K．A． N.D.; Jusoh M.H.; Mashohor S.; Sali A.; Yoshikawa A.; Kasuan N.; Hashim M.H.; Hairuddin M.A.
• Format: Data paper
• Language: English
• Published: Elsevier Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175245779&doi=10.1016%2fj.dib.2023.109667&partnerID=40&md5=8d0c5b7b35bc4f78d46c5a6ed46b63e7

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. © 2023 The Author(s)