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Skill ranking of researchers via hypergraph

Researchers use various skills in their works, such as writing, data analysis and experiments design. These research skills have greatly influenced the quality of their research outputs, as well as their scientific impact. Although many indicators have been proposed to quantify the impact of researc...

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Detalles Bibliográficos
Autores principales: Kong, Xiangjie, Liu, Lei, Yu, Shuo, Yang, Andong, Bai, Xiaomei, Xu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924507/
https://www.ncbi.nlm.nih.gov/pubmed/33816835
http://dx.doi.org/10.7717/peerj-cs.182
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author Kong, Xiangjie
Liu, Lei
Yu, Shuo
Yang, Andong
Bai, Xiaomei
Xu, Bo
author_facet Kong, Xiangjie
Liu, Lei
Yu, Shuo
Yang, Andong
Bai, Xiaomei
Xu, Bo
author_sort Kong, Xiangjie
collection PubMed
description Researchers use various skills in their works, such as writing, data analysis and experiments design. These research skills have greatly influenced the quality of their research outputs, as well as their scientific impact. Although many indicators have been proposed to quantify the impact of researchers, studies of evaluating their scientific research skills are very rare. In this paper, we analyze the factors affecting researchers’ skill ranking and propose a new model based on hypergraph theory to evaluate the scientific research skills. To validate our skill ranking model, we perform experiments on the PLOS ONE dataset and compare the rank of researchers’ skills with their papers’ citation counts and h-index. Finally, we analyze the patterns about how researchers’ skill ranking increased over time. Our studies also show the change patterns of researchers between different skills.
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spelling pubmed-79245072021-04-02 Skill ranking of researchers via hypergraph Kong, Xiangjie Liu, Lei Yu, Shuo Yang, Andong Bai, Xiaomei Xu, Bo PeerJ Comput Sci Algorithms and Analysis of Algorithms Researchers use various skills in their works, such as writing, data analysis and experiments design. These research skills have greatly influenced the quality of their research outputs, as well as their scientific impact. Although many indicators have been proposed to quantify the impact of researchers, studies of evaluating their scientific research skills are very rare. In this paper, we analyze the factors affecting researchers’ skill ranking and propose a new model based on hypergraph theory to evaluate the scientific research skills. To validate our skill ranking model, we perform experiments on the PLOS ONE dataset and compare the rank of researchers’ skills with their papers’ citation counts and h-index. Finally, we analyze the patterns about how researchers’ skill ranking increased over time. Our studies also show the change patterns of researchers between different skills. PeerJ Inc. 2019-03-04 /pmc/articles/PMC7924507/ /pubmed/33816835 http://dx.doi.org/10.7717/peerj-cs.182 Text en ©2019 Kong et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Kong, Xiangjie
Liu, Lei
Yu, Shuo
Yang, Andong
Bai, Xiaomei
Xu, Bo
Skill ranking of researchers via hypergraph
title Skill ranking of researchers via hypergraph
title_full Skill ranking of researchers via hypergraph
title_fullStr Skill ranking of researchers via hypergraph
title_full_unstemmed Skill ranking of researchers via hypergraph
title_short Skill ranking of researchers via hypergraph
title_sort skill ranking of researchers via hypergraph
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924507/
https://www.ncbi.nlm.nih.gov/pubmed/33816835
http://dx.doi.org/10.7717/peerj-cs.182
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