Overview
Data visualization is the representation of data in graphical form, such as charts, graphs, maps, and dashboards, to make information easier to understand and interpret. By translating numbers and relationships into visual elements, it helps reveal patterns, trends, outliers, and correlations that may be difficult to detect in raw tables. Data visualization is a core component of data analysis and decision-making across science, business, and public health, supporting both exploratory work and the clear communication of findings to others. As part of big data research, data visualization is closely tied to the tools and design principles that make large and complex datasets usable. Research in this subject area has examined dashboard design principles for self-service business intelligence, addressing how visual interfaces can be structured for clarity and effective use, as well as issues of data quality that affect how reliably visualized information can support decisions. This page gathers peer-reviewed, open-access research relevant to data visualization and the design, analysis, and quality considerations involved in presenting data effectively.
Research published in this journal
3 peer-reviewed articles, ranked by relevance. Each links to its DOI.
How this research is being cited
The 3 articles above have been cited 15 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
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2026 · Journal of Water Resources Planning and Management
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Mustafa Pamuk et al. · 2024 · Machine Learning and Knowledge Extraction
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Sara Hjelle et al. · 2024 · Information Manager (The)
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2024 · International Journal of Human–Computer Interaction
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2024 · Machine Learning and Knowledge Extraction
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2023 · Siberian Journal of Oncology
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2023 · Siberian journal of oncology
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2023 · International Journal of Human-Computer Interaction
A sample of recent works citing this journal's research on Data Visualization, linking to each citing work.