Bibliometric Scopus data for Leaning Analytics

Citation Author(s):
Dr S Md Karimulla
Basha
Jain Deemed-to-be University
Submitted by:
Dr S Md Karimul...
Last updated:
Tue, 04/01/2025 - 06:28
DOI:
10.21227/sm45-gt08
Data Format:
*.csv
License:
Creative Commons Attribution
55 Views
Categories:
Education and Learning Technologies
Machine Learning
Keywords:
Learning Analytics, Bibliometric analysis, citation analysis, science mapping, Bradford’s Law and Lotka’s law for author’s productivity.
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Abstract 

This dataset provides bibliometric information of academic publications related to learning analytics and decision sciences, sourced from Scopus. It includes metadata for a wide range of papers, including author details, titles, publication years, journal sources, and document types. Key columns in the dataset include author names, IDs, titles of publications, source titles (journals or conferences), document types, publication stage, and open access status. This dataset offers a comprehensive view of the research landscape in the fields of learning analytics and decision sciences, and it is particularly useful for conducting bibliometric analysis, tracking research trends, author collaborations, and identifying key themes and methodologies in these disciplines. The dataset serves as an essential resource for scholars and researchers interested in understanding the evolution of learning analytics research and its application to decision-making processes in various domains.

Instructions: 

Dataset Instructions

The dataset provided contains bibliometric information on various academic papers in the field of learning analytics and decision sciences. Each row corresponds to a publication with the following key attributes:

  • Authors: Names of the authors of the paper.

  • Author full names: Full names of the authors, often with their unique identifiers.

  • Author(s) ID: Unique IDs of the authors, typically from databases like Scopus.

  • Title: Title of the publication.

  • Year: The year of publication.

  • Source title: The journal or conference where the paper was published.

  • Volume: The volume number, if applicable.

  • Issue: The issue number, if applicable.

  • Page start: The starting page number of the article.

  • Language of Original Document: The language in which the paper is written (e.g., English).

  • Document Type: The type of document (e.g., conference paper, article).

  • Publication Stage: The stage of the publication process (e.g., final).

  • Open Access: Information about whether the paper is open access or not.

  • Source: The database source (e.g., Scopus).

  • EID: A unique identifier for the paper in Scopus.

This dataset can be used for various bibliometric analyses, including trends in publications, author collaborations, research themes, and more. It is a rich resource for understanding the research landscape in learning analytics and decision sciences.

About the Dataset

 

The dataset consists of metadata from Scopus, capturing essential bibliographic details of publications. It includes papers published in journals, conference proceedings, and other academic sources. The records span various types of documents, such as articles, book chapters, and conference papers, and provide detailed information on authors, publication outlets, and other bibliographic metrics. This data serves as an excellent foundation for a bibliometric exploration of trends in learning analytics and decision sciences. ​

Comments

This dataset offers a comprehensive collection of bibliometric data related to learning analytics and decision sciences, extracted from Scopus-indexed publications. It includes detailed information about authors, publication titles, sources, document types, and their respective open access statuses. The dataset is intended to support research in analyzing trends, methodologies, and collaborations in these fields. Researchers can use this resource for conducting citation analyses, identifying emerging research topics, or understanding the progression of learning analytics applications in decision sciences. Additionally, the dataset can help in creating visualizations for bibliometric exploration or in conducting meta-analyses across multiple academic disciplines.

Submitted by Dr S Md Karimul... on Tue, 04/01/2025 - 06:32

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