Tracing database usage: Detecting main paths in database link networks

Qi Yu; Ying Ding; Min Song; Sungjeon Song; Jianhua Liu; Bin Zhang

doi:10.1016/j.joi.2014.10.002

Tracing database usage: Detecting main paths in database link networks

Qi Yu, Ying Ding, Min Song, Sungjeon Song, Jianhua Liu, Bin Zhang

Department of Library and Information Science

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

This paper presents a database link network to measure the impact of databases on biological research. To this end, we used the 20,861 full-text articles from PubMed Central in the field of Bioinformatics. We then extracted databases from the methodology sections of these articles and their references. The list of databases was built with The 2013 Nucleic Acids Research Molecular Biology Database Collection (available online), which includes 1512 databases. The database link network was constructed from sets of pairs of databases mentioned in the methodology sections of full-text PubMed Central articles. The edges of the database link network represent the link relationships between two databases. The weight of each edge is determined either by the link frequency of the two databases (i.e., in the link-weighted database link network) or the topic similarity between two databases (i.e., in the similarity-weighted database link network). With the database link network, we analyzed the topological structure and main paths of the database link network to trace the usage, connection, and evolution of databases. We also conducted content analysis by comparing content similarities among the papers citing databases.

Original language	English
Pages (from-to)	1-15
Number of pages	15
Journal	Journal of Informetrics
Volume	9
Issue number	1
DOIs	https://doi.org/10.1016/j.joi.2014.10.002
Publication status	Published - 2015 Jan 1

Bibliographical note

Funding Information:
This work, done as part of the project “Cooperation Analysis of Technology Innovation Team Member Based on Knowledge Network—Empirical Evidence in the Biology and Biomedicine Field” (Grant Number: 71103114), was supported by National Natural Science Foundation of China ; and also supported partly by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant Number: 2013M3A9C4078138 ); and also supported by National Science Foundation (Grant Number: NSF 1158670 ).

Publisher Copyright:
© 2014 Elsevier Ltd.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Library and Information Sciences

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10.1016/j.joi.2014.10.002

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title = "Tracing database usage: Detecting main paths in database link networks",

abstract = "This paper presents a database link network to measure the impact of databases on biological research. To this end, we used the 20,861 full-text articles from PubMed Central in the field of Bioinformatics. We then extracted databases from the methodology sections of these articles and their references. The list of databases was built with The 2013 Nucleic Acids Research Molecular Biology Database Collection (available online), which includes 1512 databases. The database link network was constructed from sets of pairs of databases mentioned in the methodology sections of full-text PubMed Central articles. The edges of the database link network represent the link relationships between two databases. The weight of each edge is determined either by the link frequency of the two databases (i.e., in the link-weighted database link network) or the topic similarity between two databases (i.e., in the similarity-weighted database link network). With the database link network, we analyzed the topological structure and main paths of the database link network to trace the usage, connection, and evolution of databases. We also conducted content analysis by comparing content similarities among the papers citing databases.",

author = "Qi Yu and Ying Ding and Min Song and Sungjeon Song and Jianhua Liu and Bin Zhang",

note = "Funding Information: This work, done as part of the project “Cooperation Analysis of Technology Innovation Team Member Based on Knowledge Network—Empirical Evidence in the Biology and Biomedicine Field” (Grant Number: 71103114), was supported by National Natural Science Foundation of China ; and also supported partly by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant Number: 2013M3A9C4078138 ); and also supported by National Science Foundation (Grant Number: NSF 1158670 ). Publisher Copyright: {\textcopyright} 2014 Elsevier Ltd.",

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AU - Yu, Qi

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AU - Song, Sungjeon

AU - Liu, Jianhua

AU - Zhang, Bin

N1 - Funding Information: This work, done as part of the project “Cooperation Analysis of Technology Innovation Team Member Based on Knowledge Network—Empirical Evidence in the Biology and Biomedicine Field” (Grant Number: 71103114), was supported by National Natural Science Foundation of China ; and also supported partly by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant Number: 2013M3A9C4078138 ); and also supported by National Science Foundation (Grant Number: NSF 1158670 ). Publisher Copyright: © 2014 Elsevier Ltd.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - This paper presents a database link network to measure the impact of databases on biological research. To this end, we used the 20,861 full-text articles from PubMed Central in the field of Bioinformatics. We then extracted databases from the methodology sections of these articles and their references. The list of databases was built with The 2013 Nucleic Acids Research Molecular Biology Database Collection (available online), which includes 1512 databases. The database link network was constructed from sets of pairs of databases mentioned in the methodology sections of full-text PubMed Central articles. The edges of the database link network represent the link relationships between two databases. The weight of each edge is determined either by the link frequency of the two databases (i.e., in the link-weighted database link network) or the topic similarity between two databases (i.e., in the similarity-weighted database link network). With the database link network, we analyzed the topological structure and main paths of the database link network to trace the usage, connection, and evolution of databases. We also conducted content analysis by comparing content similarities among the papers citing databases.

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Tracing database usage: Detecting main paths in database link networks

Abstract

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