Background: The five Central Asian republics comprise of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Their research and publication activities are gradually improving but there is limited data on how good their peer reviewing practices are. Methods: We have use the Publons database to extract information on the reviewers registered including the number of verified review, Publons award winners, and top universities in the domain of peer reviewing. This has been analysed overall and country wise. Results: Of 15,764 researchers registered on Publons, only 370 (11.7%) have verified records of peer-reviewing. There are 8 Publons award winners. There is great heterogeneity in the number of active reviewers across the five countries. Kazakhstan and Uzbekistan account for more than 90% of verified reviewers. Only Kazakhstan has more than 100 active reviewers and 6 Publons award recipients. Amongst the top 20 reviewers from Central Asia, half of them are from the Nazarbayev University, Nur-Sultan, Kazakhstan. Three countries have less than 10 universities registered on Publons. Conclusion Central Asia has a good number of peer reviewers on Publons though only a minority of researchers are involved in peer reviewing. However, the heterogeneity between the nations can be best dealt with by promoting awareness and international networking including e-learning and mentoring programs.

Background: The five Central Asian republics comprise of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Their research and publication activities are gradually improving but there is limited data on how good their peer reviewing practices are. Methods: We have use the Publons database to extract information on the reviewers registered including the number of verified review, Publons award winners, and top universities in the domain of peer reviewing. This has been analysed overall and country wise. Results: Of 15,764 researchers registered on Publons, only 370 (11.7%) have verified records of peer-reviewing. There are 8 Publons award winners. There is great heterogeneity in the number of active reviewers across the five countries. Kazakhstan and Uzbekistan account for more than 90% of verified reviewers. Only Kazakhstan has more than 100 active reviewers and 6 Publons award recipients. Amongst the top 20 reviewers from Central Asia, half of them are from the Nazarbayev University, Nur-Sultan, Kazakhstan. Three countries have less than 10 universities registered on Publons. Conclusion Central Asia has a good number of peer reviewers on Publons though only a minority of researchers are involved in peer reviewing. However, the heterogeneity between the nations can be best dealt with by promoting awareness and international networking including e-learning and mentoring programs.

The article analyzes the publication activity of scientific authors from Kazakhstan based on Scopus and SCImago Journal & Country Rank data from 2010 to 2015. The number of indexed multidisciplinary and medical articles from the country has been steadily growing from 2011 onward and this can be due to the adoption of the new Law on Science in that year. Several regulatory legal acts have been issued in recent years aimed at improving the quality of local journals and the international recognition of academic degrees and titles. Publication activity of scientific authors from Kazakhstan was found to be higher than that from other countries in the Central Asian region. However, there are still many unresolved issues related to the English language barrier, lack of indexing status of local journals, and poor topical education on science writing and editing. As such, the number of articles published in 'predatory' journals remains sizable, and there are concerns over authors' negligence and plagiarism. The global solution to the discussed problems may be achieved by educating researchers, authors, reviewers, and editors.
Bibliometrics ; Humans ; Kazakhstan ; Language ; Periodicals as Topic ; Publications/ethics/legislation & jurisprudence/*trends ; Publishing/trends ; Scientific Misconduct

The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI’s potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI’s efficiencies in editorial processes.

The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI’s potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI’s efficiencies in editorial processes.

The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI’s potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI’s efficiencies in editorial processes.

The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI’s potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI’s efficiencies in editorial processes.

No abstract available.
Humans ; Peer Review, Research/*ethics ; Periodicals as Topic ; Publishing/*ethics ; Research Personnel

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports.Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Background: Plagiarism is one of the most common violation of publication ethics, and it still remains an area with several misconceptions and uncertainties. Methods: This online cross-sectional survey was conducted to analyze plagiarism perceptions among researchers and journal editors, particularly from non-Anglophone countries. Results: Among 211 respondents (mean age 40 years; M:F, 0.85:1), 26 were scholarly journal editors and 70 were reviewers with a large representation from India (50, 24%), Turkey (28, 13%), Kazakhstan (25, 12%) and Ukraine (24, 11%). Rigid and outdated pre- and post-graduate education was considered as the origin of plagiarism by 63% of respondents. Paraphragiarism was the most commonly encountered type of plagiarism (145, 69%). Students (150, 71%), nonAnglophone researchers with poor English writing skills (117, 55%), and agents of commercial editing agencies (126, 60%) were thought to be prone to plagiarize. There was a significant disagreement on the legitimacy of text copying in scholarly articles, permitted plagiarism limit, and plagiarized text in methods section. More than half (165, 78%) recommended specifically designed courses for plagiarism detection and prevention, and 94.7% (200) thought that social media platforms may be deployed to educate and notify about plagiarism. Conclusion Great variation exists in the understanding of plagiarism, potentially contributing to unethical publications and even retractions. Bridging the knowledge gap by arranging topical education and widely employing advanced anti-plagiarism software address this unmet need.