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Small world of the miRNA science drives its publication dynamics

Many scientific articles became available in the digital form which allows for querying articles data, and specifically the automated metadata gathering, which includes the affiliation data. This in turn can be used in the quantitative characterization of the scientific field, such as organizations...

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Detalles Bibliográficos
Autores principales: Firsov, A.B., Titov, I.I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Federal Research Center Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837159/
https://www.ncbi.nlm.nih.gov/pubmed/36694723
http://dx.doi.org/10.18699/VJGB-22-100
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author Firsov, A.B.
Titov, I.I.
author_facet Firsov, A.B.
Titov, I.I.
author_sort Firsov, A.B.
collection PubMed
description Many scientific articles became available in the digital form which allows for querying articles data, and specifically the automated metadata gathering, which includes the affiliation data. This in turn can be used in the quantitative characterization of the scientific field, such as organizations identification, and analysis of the co-authorship graph of those organizations to extract the underlying structure of science. In our work, we focus on the miRNA science field, building the organization co-authorship network to provide the higher-level analysis of scientific community evolution rather than analyzing author-level characteristics. To tackle the problem of the institution name writing variability, we proposed the k-mer/n-gram boolean feature vector sorting algorithm, KOFER in short. This approach utilizes the fact that the contents of the affiliation are rather consistent for the same organization, and to account for writing errors and other organization name variations within the affiliation metadata field, it converts the organization mention within the affiliation to the K-Mer (n-gram) Boolean presence vector. Those vectors for all affiliations in the dataset are further lexicographically sorted, forming groups of organization mentions. With that approach, we clustered the miRNA field affiliation dataset and extracted unique organization names, which allowed us to build the co-authorship graph on the organization level. Using this graph, we show that the growth of the miRNA field is governed by the small-world architecture of the scientific institution network and experiences power-law growth with exponent 2.64 ± 0.23 for organization number, in accordance with network diameter, proposing the growth model for emerging scientific fields. The first miRNA publication rate of an organization interacting with already publishing organization is estimated as 0.184 ± 0.002 year–1.
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spelling pubmed-98371592023-01-23 Small world of the miRNA science drives its publication dynamics Firsov, A.B. Titov, I.I. Vavilovskii Zhurnal Genet Selektsii Original Article Many scientific articles became available in the digital form which allows for querying articles data, and specifically the automated metadata gathering, which includes the affiliation data. This in turn can be used in the quantitative characterization of the scientific field, such as organizations identification, and analysis of the co-authorship graph of those organizations to extract the underlying structure of science. In our work, we focus on the miRNA science field, building the organization co-authorship network to provide the higher-level analysis of scientific community evolution rather than analyzing author-level characteristics. To tackle the problem of the institution name writing variability, we proposed the k-mer/n-gram boolean feature vector sorting algorithm, KOFER in short. This approach utilizes the fact that the contents of the affiliation are rather consistent for the same organization, and to account for writing errors and other organization name variations within the affiliation metadata field, it converts the organization mention within the affiliation to the K-Mer (n-gram) Boolean presence vector. Those vectors for all affiliations in the dataset are further lexicographically sorted, forming groups of organization mentions. With that approach, we clustered the miRNA field affiliation dataset and extracted unique organization names, which allowed us to build the co-authorship graph on the organization level. Using this graph, we show that the growth of the miRNA field is governed by the small-world architecture of the scientific institution network and experiences power-law growth with exponent 2.64 ± 0.23 for organization number, in accordance with network diameter, proposing the growth model for emerging scientific fields. The first miRNA publication rate of an organization interacting with already publishing organization is estimated as 0.184 ± 0.002 year–1. The Federal Research Center Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences 2022-12 /pmc/articles/PMC9837159/ /pubmed/36694723 http://dx.doi.org/10.18699/VJGB-22-100 Text en Copyright © AUTHORS https://creativecommons.org/licenses/by/2.5/This work is licensed under a Creative Commons Attribution 4.0 License
spellingShingle Original Article
Firsov, A.B.
Titov, I.I.
Small world of the miRNA science drives its publication dynamics
title Small world of the miRNA science drives its publication dynamics
title_full Small world of the miRNA science drives its publication dynamics
title_fullStr Small world of the miRNA science drives its publication dynamics
title_full_unstemmed Small world of the miRNA science drives its publication dynamics
title_short Small world of the miRNA science drives its publication dynamics
title_sort small world of the mirna science drives its publication dynamics
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837159/
https://www.ncbi.nlm.nih.gov/pubmed/36694723
http://dx.doi.org/10.18699/VJGB-22-100
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