Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
复制标题
教育未来一代的研究人员：分析方法趋势的跨学科调查。
DOI：
10.1371/journal.pbio.3001313
复制
发表时间：
2021-07
期刊：
影响因子：
9.8
通讯作者：
Uddin LQ
中科院分区：
文献类型：
--
作者：
Bolt T;Nomi JS;Bzdok D;Uddin LQ
生物医学、生命科学和社会科学（BLS）中的数据分析方法正在快速发展。与此同时，越来越多的人担心，定量方法的教育未能使学生为当代研究做好充分的准备。这些趋势促使人们呼吁对本科生和研究生的定量研究方法课程进行改革。我们认为，这种改革应该基于数据驱动的见解，在内部和跨学科的分析方法的使用。我们对同行评议文献的调查分析了近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该语料库的一个大子集的“方法”和“结果”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趋势。我们发现，t检验、方差分析（ANOVA）、线性回归、卡方检验等经典统计方法一直是生物医学、生命科学和社会科学研究文章中被提及最多的分析方法。然而，从2009年到2020年，这些方法在已发表文献中所占的比例有所下降。另一方面，多元统计和机器学习方法，如人工神经网络（ann），在科学出版物的总份额中显着增加。我们还发现了与每个BLS科学学科相关的分析方法的独特分组，例如心理学中结构方程模型（SEM）的使用，肿瘤学中的生存模型，以及生态学中的流形学习。我们讨论了这些发现对统计学和研究方法教育的影响，以及内部和跨学科合作。一项针对100万篇已发表的研究文章的定量调查显示，尽管经典统计方法仍在广泛使用，但多元统计和机器学习方法已显著增加；统计课程应加以修订，以充分利用这些新的分析工具。
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. A quantitative survey of >1 million published research articles reveals that while classical statistical methods remain in widespread use, multivariate statistical and machine-learning approaches have seen a significant increase; statistics curricula should be revised to take full advantage of these new analytical tools.
登录
查看更多内容
影响因子：
5.7
作者：
Bzdok, Danilo;Yeo, B. T. Thomas
通讯作者：
Yeo, B. T. Thomas
影响因子：
2.4
作者：
Barker, M;Rayens, W
通讯作者：
Rayens, W
影响因子：
15.8
作者：
Ioannidis, JPA
通讯作者：
Ioannidis, JPA
影响因子：
1.8
作者：
Baumer, Ben
通讯作者：
Baumer, Ben
DOI：
10.1111/rssa.12132
发表时间：
2015-10-01
影响因子：
2
作者：
Diggle, Peter J.
通讯作者：
Diggle, Peter J.