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Related Concept Videos

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Evolution of New Traits in Microbes

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Updated: Jun 13, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Mapping the evolution of scientific fields.

Mark Herrera1, David C Roberts, Natali Gulbahce

  • 1Department of Physics and Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland, United States of America.

Plos One
|May 14, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network analysis to map the evolution of scientific fields. The approach identifies scientific communities and tracks their development over time, aiding in predicting future scientific trends.

Related Experiment Videos

Last Updated: Jun 13, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Area of Science:

  • Bibliometrics
  • Network Science
  • History of Science

Background:

  • Scientific fields exhibit complex interactions, yet their evolutionary dynamics lack formal description.
  • Understanding the evolution of scientific fields is crucial for predicting future research directions.

Purpose of the Study:

  • To develop and present a novel network-based approach for analyzing the dynamics and evolution of scientific fields.
  • To formally describe the temporal evolution of scientific concepts and their interrelations.

Main Methods:

  • Constructed an "idea network" using American Physical Society Physics and Astronomy Classification Scheme (PACS) numbers as nodes.
  • Linked PACS numbers based on simultaneous co-occurrence in publications.
  • Applied a community finding algorithm to identify scientific fields and analyze their evolution from 1985 to 2006.

Main Results:

  • Identified communities that correspond to established scientific fields.
  • Demonstrated that the age of identified scientific fields is correlated with their size and activity.
  • Quantified the temporal evolution of scientific fields and their constituent concepts.

Conclusions:

  • The network-based approach effectively maps and quantifies the evolution of scientific fields.
  • The findings provide a framework for understanding scientific progress and potentially guiding future scientific development.
  • This method offers insights into the dynamics of scientific idea evolution.