解码数据生成团队实现临床与科学成功的模式：来自Bridge2AI人才知识图谱的洞见
Decoding Patterns of Data Generation Teams for Clinical and Scientific Success: Insights from the Bridge2AI Talent Knowledge Graph
AI总结：
本研究基于Bridge2AI人才知识图谱等的1699篇数据集论文，用SHAP框架分析团队属性与数据集论文科学影响力、临床转化能力的关联，发现PI领导力等因素与成功相关，为组建高绩效数据生成团队提供洞见。
摘要：
高质量生物医学数据集对医学研究和疾病治疗创新至关重要。由美国国立卫生研究院（NIH）资助的Bridge2AI项目致力于通过联合顶尖的多元化团队，打造专为人工智能驱动的生物医学研究设计的数据集，以推动此类创新。我们研究了来自《核酸研究》（NAR）数据库刊期和Bridge2AI人才知识图谱的1699篇数据集论文，将每篇论文的作者视为一个团队，探究了团队属性（团队实力与公平性）与数据集论文质量之间的关系，其中质量以科学影响力（相对引用比百分位）和临床转化能力（APT，即被临床试验和指南引用的可能性）来衡量。借助SHAP可解释人工智能框架，我们识别出团队属性与数据集论文在引用影响力和临床转化两方面成功之间的相关性。核心发现表明：（1）首席研究员（PI）的领导力和团队学术实力是数据集成功的强预测因素；（2）团队规模和职业年龄与科学影响力呈正相关，但与临床转化的关联模式相反；（3）女性占比越高，数据集的成功度越高。尽管我们的结果是相关性的，但它们为组建高绩效数据生成团队提供了宝贵洞见。未来研究应纳入因果框架，以深化对这些关系的理解。
英文摘要：
High-quality biomedical datasets are essential for medical research and disease treatment innovation. The NIH-funded Bridge2AI project strives to facilitate such innovations by uniting top-tier, diverse teams to curate datasets designed for AI-driven biomedical research. We examined 1,699 dataset papers from the Nucleic Acids Research (NAR) database issues and the Bridge2AI Talent Knowledge Graph. By treating each paper's authors as a team, we explored the relationship between team attributes (team power and fairness) and dataset paper quality, measured by scientific impact (Relative Citation Ratio percentile) and clinical translation power (APT, likelihood of citation by clinical trials and guidelines). Utilizing the SHAP explainable AI framework, we identified correlations between team attributes and the success of dataset papers in both citation impact and clinical translation. Key findings reveal that (1) PI (Principal Investigator) leadership and team academic prowess are strong predictors of dataset success; (2) team size and career age are positively correlated with scientific impact but show inverse patterns for clinical translation; and (3) higher female representation correlates with greater dataset success. Although our results are correlational, they offer valuable insights into forming high-performing data generation teams. Future research should incorporate causal frameworks to deepen understanding of these relationships.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
