Trends and Network Analysis of Data Literacy in Various Fields: Bibliometric Analysis

2023

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DOI 10.31643/2023.24
Data confidence 70 medium
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It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Data literacy has emerged as a key aspect in meeting the challenges of the evolving digital age. However, existing bibliometric analyses related to this topic are scarce. To fill the gap, this study uses a bibliometric approach to conduct a comprehensive review spanning nearly seven decades in order to assess the research landscape, progress, and emerging trends in data literacy. This article presents a new review of scientific production through an in-depth bibliometric analysis of the Scopus database using two software packages namely VOSviewer and RStudio. The article presents a synthesis of 2896 peer-reviewed articles from 1956 to 2023. Using keyword and co-occurrence analyses to identify key trends and relationships in research on this topic. Researchers highlighted the most researched concepts and the most important relationships between different publications. The analysis process was conducted by presenting the publication time, disciplinary area, scientific publications on data literacy, prolific authors, most cited publications, productive scientific countries, and keyword co-occurrence analysis on this topic.