Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
Nur Dalila K．A．,
Mohamad Huzaimy Jusoh,
Syamsiah Mashohor,
Aduwati Sali,
Akimasa Yoshikawa,
Nurhani Kasuan,
Mohd Helmy Hashim,
Muhammad Asraf Hairuddin
Affiliations
Nur Dalila K．A．
Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia; College of Engineering, Universiti Teknologi MARA Johor Branch, 81750 Masai Johor Malaysia
Mohamad Huzaimy Jusoh
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Malaysia; Corresponding authors.
Syamsiah Mashohor
Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia; Corresponding authors.
Aduwati Sali
WiPNET Department of Computer and Communication Systems, Universiti Putra Malaysia, 43400 Serdang Selangor, Malaysia
Akimasa Yoshikawa
International Research Center for Space and Planetary Environmental Science (i-SPES), Kyushu University, 819-0395 Fukuoka, Japan; Department of Earth and Planetary Sciences, Kyushu University, 819-0395 Fukuoka, Japan
Nurhani Kasuan
College of Engineering, Universiti Teknologi MARA Johor Branch, 81750 Masai Johor Malaysia
Mohd Helmy Hashim
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Malaysia; Exploration and Space Science Division, Malaysian Space Agency (MYSA), 42700 Banting Selangor, Malaysia
Muhammad Asraf Hairuddin
College of Engineering, Universiti Teknologi MARA Johor Branch, 81750 Masai Johor 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.
