Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the "Methods" and "Results" sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.

中文翻译：

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教育下一代研究人员：分析方法趋势的跨学科调查。

生物医学、生命和社会 (BLS) 科学中的数据分析方法正在快速发展。与此同时，人们越来越担心定量方法教育未能为学生做好当代研究的充分准备。这些趋势导致了对本科和研究生定量研究方法课程进行教育改革的呼声。我们认为，这种改革应该基于数据驱动的对分析方法内部和跨学科使用的洞察。我们对同行评审文献的调查分析了大约 130 万篇公开可用的研究文章，以监测过去十年中对分析方法的跨学科提及。我们将数据驱动的文本挖掘分析应用于“方法”和“结果” 这个语料库的一个大子集的部分，以确定跨学科共享的分析方法提及的趋势，以及每个学科独有的那些。我们发现 t 检验、方差分析 (ANOVA)、线性回归、卡方检验和其他经典统计方法一直是并且仍然是生物医学、生命科学和社会科学研究文章中提及最多的分析方法。然而，在 2009 年至 2020 年期间，这些方法的提及占已发表文献的百分比有所下降。 另一方面，多变量统计和机器学习方法，例如人工神经网络 (ANN)，总份额显着增加的科学出版物。我们还发现了与每个 BLS 科学学科相关的独特的分析方法分组，例如在心理学中使用结构方程模型 (SEM)，在肿瘤学中使用生存模型，在生态学中使用流形学习。我们讨论了这些发现对统计学和研究方法教育以及学科内和跨学科合作的影响。