Cover Image

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

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 base...

Full description

Saved in:
Bibliographic Details
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
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.106863
record_format eprints
spelling my.upm.eprints.1068632024-08-06T01:49:13Z http://psasir.upm.edu.my/id/eprint/106863/ Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research K. A．, Nur Dalila Jusoh, Mohamad Huzaimy Mashohor, Syamsiah Sali, Aduwati Yoshikawa, Akimasa Kasuan, Nurhani Hashim, Mohd Helmy Hairuddin, Muhammad Asraf 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. Elsevier BV 2023 Article PeerReviewed K. A．, Nur Dalila and Jusoh, Mohamad Huzaimy and Mashohor, Syamsiah and Sali, Aduwati and Yoshikawa, Akimasa and Kasuan, Nurhani and Hashim, Mohd Helmy and Hairuddin, Muhammad Asraf (2023) Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research. Data in Brief, 51. pp. 1-8. ISSN 2352-3409; ESSN: 2352-3409 https://linkinghub.elsevier.com/retrieve/pii/S2352340923007527 10.1016/j.dib.2023.109667
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description 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.
format Article
author K. A．, Nur Dalila
Jusoh, Mohamad Huzaimy
Mashohor, Syamsiah
Sali, Aduwati
Yoshikawa, Akimasa
Kasuan, Nurhani
Hashim, Mohd Helmy
Hairuddin, Muhammad Asraf
spellingShingle K. A．, Nur Dalila
Jusoh, Mohamad Huzaimy
Mashohor, Syamsiah
Sali, Aduwati
Yoshikawa, Akimasa
Kasuan, Nurhani
Hashim, Mohd Helmy
Hairuddin, Muhammad Asraf
Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
author_facet K. A．, Nur Dalila
Jusoh, Mohamad Huzaimy
Mashohor, Syamsiah
Sali, Aduwati
Yoshikawa, Akimasa
Kasuan, Nurhani
Hashim, Mohd Helmy
Hairuddin, Muhammad Asraf
author_sort K. A．, Nur Dalila
title Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
title_short Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
title_full Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
title_fullStr Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
title_full_unstemmed Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
title_sort bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research
publisher Elsevier BV
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/106863/
https://linkinghub.elsevier.com/retrieve/pii/S2352340923007527
_version_ 1806701231067889664
score 13.211869

Similar Items