IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v114y2018i3d10.1007_s11192-017-2618-1.html
   My bibliography  Save this article

Predicting scientific impact based on h-index

Author

Listed:
  • Samreen Ayaz

    () (Capital University of Science & Technology)

  • Nayyer Masood

    () (Capital University of Science & Technology)

  • Muhammad Arshad Islam

    () (Capital University of Science & Technology)

Abstract

Abstract Predicting the future impact of a scientist/researcher is a critical task. The objective of this work is to evaluate different h-index prediction models for the field of Computer Science. Different combinations of parameters have been identified to build the model and applied on a large data set taken from Arnetminer comprised of almost 1.8 million authors and 2.1 million publications’ record of Computer Science. Machine learning prediction technique, regression, is used to find the best set of parameters suitable for h-index prediction for the scientists from all career ages, without enforcing any constraint on their current h-index values with R 2 as a metric to measure the accuracy. Further, these parameters are evaluated for different career ages and different thresholds for h-index values. Prediction results for 1 year are really good, having R 2 0.93 but for 5 years R 2 declines to 0.82 on average. Hence inferred that prediction of h-index is difficult for longer periods. Predictions for the researchers having 1 year experience are not precise, having R 2 0.60 for 1 year and 0.33 for 5 years. Considering scientists of different career ages, average R 2 values for researchers having 20–36 years of experience were 0.99. For the researches having different h-index values, researchers having low h-index were difficult to predict. Parameters set comprising of current h-index, average citations per paper, number of coauthors, years since publishing first article, number of publications, number of impact factor publications, and number of publications in distinct journals performed better than all other combinations.

Suggested Citation

  • Samreen Ayaz & Nayyer Masood & Muhammad Arshad Islam, 2018. "Predicting scientific impact based on h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 993-1010, March.
  • Handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2618-1
    DOI: 10.1007/s11192-017-2618-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2618-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2485-9 is not listed on IDEAS
    2. Schreiber, Michael, 2013. "How relevant is the predictive power of the h-index? A case study of the time-dependent Hirsch index," Journal of Informetrics, Elsevier, vol. 7(2), pages 325-329.
    3. Samreen Ayaz & Muhammad Tanvir Afzal, 2016. "Identification of conversion factor for completing-h index for the field of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1511-1524, December.
    4. repec:eee:infome:v:11:y:2017:i:3:p:810-822 is not listed on IDEAS
    5. repec:elg:eechap:15330_1 is not listed on IDEAS
    6. Amjad, Tehmina & Ding, Ying & Xu, Jian & Zhang, Chenwei & Daud, Ali & Tang, Jie & Song, Min, 2017. "Standing on the shoulders of giants," Journal of Informetrics, Elsevier, vol. 11(1), pages 307-323.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    h-Index prediction; Regression; Career age; R 2;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2618-1. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.