Bibliographic dataset of literature for analysing global trends and progress of the machine learning paradigm in space weather research.
Bibliometric evaluation
Development trends analysis
Literature review data
Open-source R-package
Visualisation
Journal
Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
30
08
2023
revised:
03
10
2023
accepted:
06
10
2023
medline:
15
11
2023
pubmed:
15
11
2023
entrez:
15
11
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37965602
doi: 10.1016/j.dib.2023.109667
pii: S2352-3409(23)00752-7
pmc: PMC10641112
doi:
Types de publication
Journal Article
Langues
eng
Pagination
109667Informations de copyright
© 2023 The Author(s).
Références
PLoS Med. 2009 Jul 21;6(7):e1000097
pubmed: 19621072
Chronobiol Int. 2021 Jan;38(1):27-37
pubmed: 33164592