[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128091-en":3,"doc-seo-128091-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128091,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms - read online","Research impact is treated as a multi-dimensional outcome reflecting societal, economic, environmental, cultural, and health-related benefits. The study analyzes interrelationships among variables shaping scientific outputs and identifies machine learning methods best suited for predicting scientific, social, and economic impacts. Using scientometric data on Iran’s biotechnology publications indexed in Scopus from 2003–2024, the work applies Pearson correlation, SciVal analytics, and R/Python-based modeling (multiple linear regression, k-nearest neighbors, trees, random forests, gradient boosting).","Scientometrics Research Journal  \nScientific Bi-Quarterly of Shahed University  \nVol. 11, No. 1, Spring & Summer 2025 (Serial 21)  \nResearch Article DOI: 10.22070/rsci.2024.18868.1719  \nGhasem  \nAzadi Ahmadabadi  \nReceived: 22/08/2024  \nRevised: 28/10/2024  \nAccepted: 12/11/2024  \nEarly online access: 19/11/2024  \nPublished: 01/04/2025  \nPredicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms  \n 1. Assistant Professor, Policy Evaluation and Monitoring of Science, Technology, and Innovation Department, National Research Institute for Science Policy, Tehran, Iran.  \nEmail: [azadi_gh@yahoo.com](azadi_gh@yahoo.com)  \nAbstract  \nPurpose: Research impact is a key concern for stakeholders, as it reflects the beneficial and profitable applications of research across multiple dimensions, including society, economy, environment, culture, and health. This study aims to analyze the interrelationships among variables influencing scientific outputs and to identify the most effective machine learning algorithms for predicting their scientific, social, and economic impacts.  \nMethodology: The current research is applied in purpose and descriptive in method, utilizing a scientometric approach. This study aims to explore the relationship between the quantity of scientific publications and scientific cooperation, as well as the scientific, social, and economic impact of Iran's scientific contributions in the field of biotechnology. Additionally, it seeks to determine which machine learning algorithms are more effective in predicting the impact of scientific outputs across various dimensions. The research focuses on Iran's biotechnology scientific outputs indexed in the Scopus database from 2003 to 2024. The data, extracted on 21 January 2024, were analyzed using the SciVal analytical database. In this research, Pearson's correlation coefficient and the R software package were used to examine the relationships between the studied indicators. Machine learning algorithms, including multiple linear regression, nearest neighbors, decision trees, random forests, and gradient boosting, were applied and evaluated as predictive models. The Python programming language was employed to conduct tests and implement these algorithms.  \nFindings: The findings of this study showed that Iran's scientific outputsin this field in the period from 2003 to 2023 had increased 36 times, which is considered extremely high progress. A positive and significant relationship was observed between international collaboration and various impact indicators, including citation counts, Field-Weighted Citation Impact, Output in Top 10% Citation Percentiles, Patent-Citations Count, Patent-Citations per Scholarly Output, Scholarly Output cited by Patents, Patents Count, Views Count, Output in Top 10% Views Percentiles,  \nScientometrics Research Journal  \nScientific Bi-Quarterly of Shahed University  \nVol. 11, No. 1, Spring & Summer 2025 (Serial 21)  \nResearch Article DOI: 10.22070/rsci.2024.18868.1719  \nGhasem  \nAzadi Ahmadabadi  \nReceived: 22/08/2024  \nRevised: 28/10/2024  \nAccepted: 12/11/2024  \nEarly online access: 19/11/2024  \nPublished: 01/04/2025  \nViews per Publication, and Field-Weighted Views Impact. The index of academic collaboration also shows a positive and significant relationship with several indicators, including citation counts, Field-Weighted Citation Impact, Output in the Top 10% Citation Percentiles, Publications in the Top 10% Journal Percentiles (by Cite Score Percentile), Patents Count, Scholarly Output cited by Patents, Views Count, and FieldWeighted Views Impact. Academic-government collaboration is also positively and significantly correlated with three indicators citations per publication, Patent-Citations Count, and Patent-Citations per Scholarly Output. In the case of the impact of the studied scientific outputs, citation counts are positively and significantly associated with several indicators, including Scholarly Output c","cbCaiawpZm8D3Vgj","https://ap.wps.com/l/cbCaiawpZm8D3Vgj","pdf",931146,1,26,"English","en",105,"# Abstract\n## Purpose\n## Methodology\n## Findings\n## Conclusion\n## Keywords","[{\"question\":\"What is the main goal of this study on biotechnology research impact?\",\"answer\":\"To examine relationships among factors influencing scientific outputs and to determine which machine learning algorithms best predict scientific, social, and economic impacts.\"},{\"question\":\"What data and tools are used in the analysis?\",\"answer\":\"The study uses Iran’s biotechnology outputs indexed in Scopus from 2003 to 2024, extracted on 21 January 2024, analyzed via the SciVal database and evaluated with Pearson correlation using R and Python for modeling.\"},{\"question\":\"Which factor is highlighted as most influential for article quality?\",\"answer\":\"International cooperation is identified as the most significant factor affecting quality-related metrics such as citations, views, and applicability.\"}]","Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms - 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