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

doi:10.1016/j.dib.2023.109667

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

Data Brief. 2023 Oct 18:51:109667. doi: 10.1016/j.dib.2023.109667. eCollection 2023 Dec.

Authors

Nur Dalila K A^{1

2}, Mohamad Huzaimy Jusoh³, Syamsiah Mashohor¹, Aduwati Sali⁴, Akimasa Yoshikawa^{5

6}, Nurhani Kasuan², Mohd Helmy Hashim^{3

7}, Muhammad Asraf Hairuddin²

Affiliations

¹ Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
² College of Engineering, Universiti Teknologi MARA Johor Branch, 81750 Masai Johor Malaysia.
³ School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Malaysia.
⁴ WiPNET Department of Computer and Communication Systems, Universiti Putra Malaysia, 43400 Serdang Selangor, Malaysia.
⁵ International Research Center for Space and Planetary Environmental Science (i-SPES), Kyushu University, 819-0395 Fukuoka, Japan.
⁶ Department of Earth and Planetary Sciences, Kyushu University, 819-0395 Fukuoka, Japan.
⁷ Exploration and Space Science Division, Malaysian Space Agency (MYSA), 42700 Banting Selangor, Malaysia.

Abstract

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.

Keywords: Bibliometric evaluation; Development trends analysis; Literature review data; Open-source R-package; Visualisation.