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

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 ed...

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Autores principales:	Taylor Bolt, Jason S Nomi, Danilo Bzdok, Lucina Q Uddin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4fbee725c66e454fb82077cb2ff81a8e
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spelling	oai:doaj.org-article:4fbee725c66e454fb82077cb2ff81a8e2021-12-02T19:54:21ZEducating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.1544-91731545-788510.1371/journal.pbio.3001313https://doaj.org/article/4fbee725c66e454fb82077cb2ff81a8e2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pbio.3001313https://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885Methods 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.Taylor BoltJason S NomiDanilo BzdokLucina Q UddinPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 19, Iss 7, p e3001313 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Taylor Bolt Jason S Nomi Danilo Bzdok Lucina Q Uddin Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
description	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.
format	article
author	Taylor Bolt Jason S Nomi Danilo Bzdok Lucina Q Uddin
author_facet	Taylor Bolt Jason S Nomi Danilo Bzdok Lucina Q Uddin
author_sort	Taylor Bolt
title	Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
title_short	Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
title_full	Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
title_fullStr	Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
title_full_unstemmed	Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.
title_sort	educating the future generation of researchers: a cross-disciplinary survey of trends in analysis methods.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4fbee725c66e454fb82077cb2ff81a8e
work_keys_str_mv	AT taylorbolt educatingthefuturegenerationofresearchersacrossdisciplinarysurveyoftrendsinanalysismethods AT jasonsnomi educatingthefuturegenerationofresearchersacrossdisciplinarysurveyoftrendsinanalysismethods AT danilobzdok educatingthefuturegenerationofresearchersacrossdisciplinarysurveyoftrendsinanalysismethods AT lucinaquddin educatingthefuturegenerationofresearchersacrossdisciplinarysurveyoftrendsinanalysismethods
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Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.

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