insight - Science Mapping - # Overlay Maps of Science

Improving Overlay Maps of Science: Enhancing Research Visualization

Q: How can these overlay maps be applied beyond biomedical sciences?

Overlay maps can be applied to various fields beyond biomedical sciences by providing a visual representation of the research landscape. In other disciplines such as social sciences, engineering, or humanities, overlay maps can help in monitoring research activities, identifying collaboration patterns, exploring trends and developments, and understanding the structure of different research areas. By clustering publications based on citation relations and creating hierarchical classifications, researchers in diverse fields can gain insights into the distribution of topics and specialties within their domain.

Q: What are potential limitations or biases in using direct citations for clustering?

Using direct citations for clustering may introduce certain limitations and biases. One limitation is that not all relevant connections between publications are captured solely through direct citations. This could lead to overlooking important relationships that might exist through indirect citations or co-citations. Additionally, there might be issues with self-citations skewing the results if not appropriately accounted for during clustering. Biases could also arise from disparities in citation practices across different disciplines or regions, impacting the accuracy of the clusters generated based on citation data.

Q: How might interactive visualizations impact future bibliometric studies?

Interactive visualizations have the potential to revolutionize bibliometric studies by enhancing user engagement and facilitating deeper exploration of complex datasets. Researchers can interact with data more intuitively, allowing them to navigate through hierarchical classifications effectively and uncover hidden patterns or relationships within large sets of publications. Interactive features like zooming in/out on specific nodes, accessing detailed information about clusters/topics/specialties directly from the visualization tool itself, filtering data based on criteria set by users - all contribute to a more dynamic and insightful analysis process in bibliometrics studies.

Core Concepts

Enhancing overlay maps to provide both overview and detail in a single visualization.

Abstract

The article discusses the creation of hierarchical classification-based overlay maps to visualize research fields. It aims to improve existing flat maps by incorporating both broad disciplines for an overview and granular levels for detailed information. The study uses a citation network of 17 million PubMed records from 1995 onwards to cluster articles. By visualizing disciplines and specialties, the map provides insights into research structures. Applications include monitoring open access publishing and analyzing coronavirus/Covid-19 research.

Stats

The map was based on a hierarchical classification of publications from about 17 million records.
Open access rates ranged from 20% to 60% in different disciplines during the period 2008-2010.
Covid-19 research saw a significant increase from about 1,000 publications in 2013-2015 to almost 30,000 in 2018-2020.

Quotes

"Most overlay maps have been flat, failing to provide both overview and detail about the research being studied."
"Visualizations enable navigation through millions of articles, providing insights at various levels."
"The methodology used efficiently clusters articles based on direct citations for improved visualization."

Key Insights Distilled From

Improving overlay maps of science

by Pete... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2110.07917.pdf

Deeper Inquiries

How can these overlay maps be applied beyond biomedical sciences?

Overlay maps can be applied to various fields beyond biomedical sciences by providing a visual representation of the research landscape. In other disciplines such as social sciences, engineering, or humanities, overlay maps can help in monitoring research activities, identifying collaboration patterns, exploring trends and developments, and understanding the structure of different research areas. By clustering publications based on citation relations and creating hierarchical classifications, researchers in diverse fields can gain insights into the distribution of topics and specialties within their domain.

What are potential limitations or biases in using direct citations for clustering?

Using direct citations for clustering may introduce certain limitations and biases. One limitation is that not all relevant connections between publications are captured solely through direct citations. This could lead to overlooking important relationships that might exist through indirect citations or co-citations. Additionally, there might be issues with self-citations skewing the results if not appropriately accounted for during clustering. Biases could also arise from disparities in citation practices across different disciplines or regions, impacting the accuracy of the clusters generated based on citation data.

How might interactive visualizations impact future bibliometric studies?

Interactive visualizations have the potential to revolutionize bibliometric studies by enhancing user engagement and facilitating deeper exploration of complex datasets. Researchers can interact with data more intuitively, allowing them to navigate through hierarchical classifications effectively and uncover hidden patterns or relationships within large sets of publications. Interactive features like zooming in/out on specific nodes, accessing detailed information about clusters/topics/specialties directly from the visualization tool itself, filtering data based on criteria set by users - all contribute to a more dynamic and insightful analysis process in bibliometrics studies.

Improving Overlay Maps of Science: Enhancing Research Visualization