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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Published: |
Elsevier BV
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106863/ |
| _version_ | 1848864838898417664 |
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| author | K. A．, Nur Dalila Jusoh, Mohamad Huzaimy Mashohor, Syamsiah Sali, Aduwati Yoshikawa, Akimasa Kasuan, Nurhani Hashim, Mohd Helmy Hairuddin, Muhammad Asraf |
| 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 |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T13:55:11Z |
| format | Article |
| id | upm-106863 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:55:11Z |
| publishDate | 2023 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/106863/ http://psasir.upm.edu.my/id/eprint/106863/ http://psasir.upm.edu.my/id/eprint/106863/ |