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Evaluation of researchers' output is vital for hiring committees and fundingbodies, and it is usually measured via their scientific productivity,citations, or a combined metric such as h-index. Assessing young researchers ismore critical because it takes a while to get citations and increment ofh-index. Hence, predicting the h-index can help to discover the researchers'scientific impact. In addition, identifying the influential factors to predictthe scientific impact is helpful for researchers seeking solutions to improveit. This study investigates the effect of author, paper and venue-specificfeatures on the future h-index. For this purpose, we used machine learningmethods to predict the h-index and feature analysis techniques to advance theunderstanding of feature impact. Utilizing the bibliometric data in Scopus, wedefined and extracted two main groups of features. The first relates to priorscientific impact, and we name it 'prior impact-based features' and includesthe number of publications, received citations, and h-index. The second groupis 'non-impact-based features' and contains the features related to author,co-authorship, paper, and venue characteristics. We explored their importancein predicting h-index for researchers in three different career phases. Also,we examine the temporal dimension of predicting performance for differentfeature categories to find out which features are more reliable for long- andshort-term prediction. We referred to the gender of the authors to examine therole of this author's characteristics in the prediction task. Our findingsshowed that gender has a very slight effect in predicting the h-index. We foundthat non-impact-based features are more robust predictors for younger scholarsthan seniors in the short term. Also, prior impact-based features lose theirpower to predict more than other features in the long-term.

Investigating the contribution of author- and publication-specific features to scholars' h-index prediction