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Formulation of rules for the scientific community using deep learning

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Abstract

In a deluge of scientific literature, it is important to build scientific quantitative rules (SQR) that can be applied to researchers' quantitative data in order to produce a uniform format for making decisions regarding the nomination of outstanding researchers. Google Scholar and other search engines track scholars’ papers, citations, etc. However, the scientific community hasn't agreed on standards a researcher must meet to be regarded as important. In this paper, we suggest rules for the scientific community based on the top five quantitative scientific parameters. The significance of the parameters is measured based on two factors: (i) parameters’ impact on the model’s performance while classifying awardees and non-awardees, and (ii) the number of award-winning researchers elevated in the ranking of researchers through each respective parameter. The experimental dataset includes information from researchers in the civil engineering, mathematics, and neuroscience domains. There are 250 awardees and 250 non-awardees from each field. The SQR for each discipline has attained an accuracy of 70% or more for their respective award-winning researchers. In addition to this, the top ranked parameters from each discipline have elevated more than 50% of the award-winning researchers into their respective ranked lists of the top 100 researchers. These findings can guide individual researchers to be on the list of prestigious scientists, and scientific societies can use the SQR to filter the list of researchers for subjective evaluation in order to reward prolific researchers in the domain.

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  1. https://figshare.com/articles/dataset/ScientometricData/21206231.

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Acknowledgements

This research work was funded by the Institutional Fund Project under grant no. (IFPIP; 812-611-1442). Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia. Moreover, there is no conflict of interest between the authors and the funding department.

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Authors and Affiliations

  1. University of Jeddah, Jeddah, Kingdom of Saudi Arabia

    Abdulrahman A. Alshdadi

  2. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

    Madini O. Alassafi & Rayed AlGhamdi

  3. Shifa Tameer-e-Millat University, Islamabad, Pakistan

    Muhammad Usman & Muhammad Tanvir Afzal

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  1. Abdulrahman A. Alshdadi
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  2. Muhammad Usman
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  3. Madini O. Alassafi
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  4. Muhammad Tanvir Afzal
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  5. Rayed AlGhamdi
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Correspondence to Muhammad Usman.

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Alshdadi, A.A., Usman, M., Alassafi, M.O. et al. Formulation of rules for the scientific community using deep learning. Scientometrics 128, 1825–1852 (2023). https://doi.org/10.1007/s11192-023-04633-5

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  • DOI: https://doi.org/10.1007/s11192-023-04633-5

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