A Social Network-Empowered Research Analytics Framework for Project Selection

Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality,...

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Main Authors: SILVA, Thushari, GUO, Zhiling, MA, Jian, JIANG, Hongbing, CHEN, Huaping
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/1854
https://ink.library.smu.edu.sg/context/sis_research/article/2853/viewcontent/DSS_SocialNetwork_av.pdf
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spelling sg-smu-ink.sis_research-28532020-03-02T13:30:25Z A Social Network-Empowered Research Analytics Framework for Project Selection SILVA, Thushari GUO, Zhiling MA, Jian JIANG, Hongbing CHEN, Huaping Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality, quantity, and citations of published journal articles) of researchers is largely ignored. To overcome these limitations, this paper proposes a social network-empowered research analytics framework (RAF) for research project selections. Scholarmate.com, a professional research social network with easy access to research relevant information, serves as a platform to build researcher profiles from three dimensions, i.e., relevance, productivity and connectivity. Building upon profiles of both proposals and researchers, we develop a unique matching algorithm to assist decision makers (e.g. panel chairs or division managers) in optimizing the assignment of reviewers to research project proposals. The proposed framework is implemented and tested by the largest government funding agency in China to aid the grant proposal evaluation process. The new system generated significant economic benefits including great cost savings and quality improvement in the proposal evaluation process. 2013-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1854 info:doi/10.1016/j.dss.2013.01.005 https://ink.library.smu.edu.sg/context/sis_research/article/2853/viewcontent/DSS_SocialNetwork_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Research project selection Research social networks Research analytics Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Research project selection
Research social networks
Research analytics
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Research project selection
Research social networks
Research analytics
Databases and Information Systems
Numerical Analysis and Scientific Computing
SILVA, Thushari
GUO, Zhiling
MA, Jian
JIANG, Hongbing
CHEN, Huaping
A Social Network-Empowered Research Analytics Framework for Project Selection
description Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality, quantity, and citations of published journal articles) of researchers is largely ignored. To overcome these limitations, this paper proposes a social network-empowered research analytics framework (RAF) for research project selections. Scholarmate.com, a professional research social network with easy access to research relevant information, serves as a platform to build researcher profiles from three dimensions, i.e., relevance, productivity and connectivity. Building upon profiles of both proposals and researchers, we develop a unique matching algorithm to assist decision makers (e.g. panel chairs or division managers) in optimizing the assignment of reviewers to research project proposals. The proposed framework is implemented and tested by the largest government funding agency in China to aid the grant proposal evaluation process. The new system generated significant economic benefits including great cost savings and quality improvement in the proposal evaluation process.
format text
author SILVA, Thushari
GUO, Zhiling
MA, Jian
JIANG, Hongbing
CHEN, Huaping
author_facet SILVA, Thushari
GUO, Zhiling
MA, Jian
JIANG, Hongbing
CHEN, Huaping
author_sort SILVA, Thushari
title A Social Network-Empowered Research Analytics Framework for Project Selection
title_short A Social Network-Empowered Research Analytics Framework for Project Selection
title_full A Social Network-Empowered Research Analytics Framework for Project Selection
title_fullStr A Social Network-Empowered Research Analytics Framework for Project Selection
title_full_unstemmed A Social Network-Empowered Research Analytics Framework for Project Selection
title_sort social network-empowered research analytics framework for project selection
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/1854
https://ink.library.smu.edu.sg/context/sis_research/article/2853/viewcontent/DSS_SocialNetwork_av.pdf
_version_ 1770571629078773760