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Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods

Bolt, Taylor ; Nomi, Jason S. ; Bzdok, Danilo ; Uddin, Lucina Q. Dirnagl, Ulrich

PLoS Biology, 2021-07, Vol.19 (7), p.e3001313-e3001313 [Periódico revisado por pares]

San Francisco: Public Library of Science

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Título:
Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods
Autor: Bolt, Taylor ; Nomi, Jason S. ; Bzdok, Danilo ; Uddin, Lucina Q.
Dirnagl, Ulrich
Assuntos: Artificial intelligence ; Artificial neural networks ; Biochemistry ; Biology ; Biomedical data ; Chi-square test ; Computer and Information Sciences ; Computer science ; Curricula ; Data analysis ; Data mining ; Education ; Engineering and Technology ; Epidemiology ; Immunology ; Informatics ; Interdisciplinary aspects ; Learning algorithms ; Machine learning ; Manifolds (mathematics) ; Mathematical analysis ; Meta ; Methodology ; Multivariate statistical analysis ; Neural networks ; Neurosciences ; Physical Sciences ; Physiology ; Polls & surveys ; Principal components analysis ; Psychology ; Public health ; Quantitative analysis ; Quantitative research ; Regression analysis ; Research and Analysis Methods ; Research methodology ; Research methods ; Scientific papers ; Statistical analysis ; Statistical methods ; Statistical tests ; Statistics ; Survival ; Teaching methods ; Trends ; Variance analysis
É parte de: PLoS Biology, 2021-07, Vol.19 (7), p.e3001313-e3001313
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
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The authors have declared that no competing interests exist.
Descrição: 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.
Editor: San Francisco: Public Library of Science
Idioma: Inglês

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Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods

Bolt, Taylor ; Nomi, Jason S. ; Bzdok, Danilo ; Uddin, Lucina Q. Dirnagl, Ulrich

San Francisco: Public Library of Science

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