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Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover the researchers\u2019 scientific impact. In addition, identifying the influential factors to predict the scientific impact is helpful for researchers and their organizations seeking solutions to improve it. This study investigates the effect of the author, paper\/venue-specific features on the future h-index. For this purpose, we used a machine learning approach to predict the h-index and feature analysis techniques to advance the understanding of feature impact. Utilizing the bibliometric data in Scopus, we defined and extracted two main groups of features. The first relates to prior scientific impact, and we name it \u2018prior impact-based features\u2019 and includes the number of publications, received citations, and h-index. The second group is \u2018non-prior impact-based features\u2019 and contains the features related to author, co-authorship, paper, and venue characteristics. We explored their importance in predicting researchers\u2019 h-index in three career phases. Also, we examined the temporal dimension of predicting performance for different feature categories to find out which features are more reliable for long- and short-term prediction. We referred to the gender of the authors to examine the role of this author\u2019s characteristics in the prediction task. Our findings showed that gender has a very slight effect in predicting the h-index. Although the results demonstrate better performance for the models containing prior impact-based features for all researchers\u2019 groups in the near future, we found that non-prior impact-based features are more robust predictors for younger scholars in the long term. Also, prior impact-based features lose their power to predict more than other features in the long term.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-023-00421-6","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T13:02:12Z","timestamp":1696597332000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Investigating the contribution of author- and publication-specific features to scholars\u2019 h-index prediction"],"prefix":"10.1140","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5572-575X","authenticated-orcid":false,"given":"Fakhri","family":"Momeni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Mayr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Dietze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"issue":"46","key":"421_CR1","doi-asserted-by":"publisher","first-page":"16569","DOI":"10.1073\/pnas.0507655102","volume":"102","author":"JE Hirsch","year":"2005","unstructured":"Hirsch JE (2005) An index to quantify an individual\u2019s scientific research output. 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