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Related Experiment Video

Updated: Jul 24, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Visual Analysis of Research Paper Collections Using Normalized Relative Compression.

Pere-Pau Vázquez¹

¹ViRVIG Group, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This study introduces Normalized Relative Compression (NRC) and Normalized Conditional Compression (NCC) for visually comparing research papers. These methods effectively analyze research novelty and trends without extensive human input.

Keywords:

compression similarity text visualization

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Area of Science:

Bibliometrics
Information Science
Computational Linguistics

Background:

Analyzing research paper collections reveals scientific novelty and stagnation.
Previous methods relied on keyword or citation analysis with human intervention.

Purpose of the Study:

To introduce and validate a novel compression-based approach for comparing research articles and document collections.
To demonstrate the effectiveness of Normalized Relative Compression (NRC) and Normalized Conditional Compression (NCC) in analyzing research trends.

Main Methods:

Utilized Normalized Relative Compression (NRC) and Normalized Conditional Compression (NCC) for data processing.
Employed automated data-processing tasks for visual comparison of research articles.
Validated the technique through a series of comparative tests.

Main Results:

Successfully visually compared research articles and document collections using NRC and NCC.
Achieved comparable results with NCC using standard compression tools.
Demonstrated superior performance compared to previously proposed techniques for specific analysis tasks.

Conclusions:

Compression-based methods offer a powerful, automated approach to analyzing research paper collections.
NRC and NCC can effectively group papers across disciplines, track conference evolution, and profile researcher changes over time.
The proposed technique provides a more effective alternative for large-scale research analysis.