“全球医学顶尖科研成果高质量数据集索引”发布

Core Insights - The article emphasizes the importance of safety compliance and privacy protection as prerequisites for large-scale data analysis in the medical field [1] - A new high-quality dataset index for global medical research from 2019 to 2024 has been released, aiming to provide authoritative data support for medical research trends, policy formulation, and industry innovation [1][2] Group 1: Dataset Overview - The dataset, developed by Dongbi Technology and Shanghai University of Finance and Economics, extracts high-value research data from a vast array of medical literature, covering four primary categories: basic research, medical devices, biomedicine, and artificial intelligence [1] - The dataset is based on the Dongbi Index, which evaluates 34 top medical journals, with over 80% of them having an impact factor exceeding 10 [2] - From 2019 to 2024, these journals published over 106,000 papers, providing a solid foundation for high-quality data mining [2] Group 2: Research Findings - The research team analyzed 15,260 papers and found that the United States leads with 9,719 core papers, followed by the UK, Germany, and France, with China ranking fifth [2] - In fields such as tumor mechanisms, disease treatment, and infectious disease prevention, the U.S. has a higher volume of research compared to China, indicating a need for China to enhance its foundational research and clinical translation efforts [2] - However, in emerging or high-tech fields like brain science, radiotherapy equipment, gene therapy, and medical imaging, the gap between China and the U.S. is relatively small, suggesting potential for China to catch up in precision medicine and advanced technology applications [2] Group 3: Recommendations for China - The report suggests that China should build a composite database based on multi-omics, multi-center clinical trials, and epidemiological surveys to ensure high-quality and diverse data [3] - It also recommends incorporating comprehensive clinical intervention, long-term follow-up, and integrated indicator systems in database design to enhance data mining value and research translation efficiency [3] - Furthermore, the report advocates for active participation in international data recognition and standardization, establishing metadata description norms and data exchange standards to promote resource sharing and collaborative innovation [3]