Open science in human electrophysiology

Clayson, Peter E.; Keil, Andreas; Larson, Michael J.

doi:10.1016/j.ijpsycho.2022.02.002

Cited by 13 publications

(6 citation statements)

References 36 publications

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“…Geared toward novice and experienced researchers alike, all articles in this issue not only explain the theoretical and conceptual steps involved in each approach, but each article is accompanied by a step-by-step tutorial, publicly available code, and example data. Consistent with similar initiatives by other groups ( Clayson et al, 2022 , Kujawa and Brooker, 2022 , Weisz and Keil, 2022 ) and the broader movement towards open-science ( Foster and Deardorff, 2017 , Markiewicz et al, 2021 , Molloy, 2011 , Munafò et al, 2017 ), it is our hope that improved access to methodological resources will further accelerate the pace of discovery within the field of developmental cognitive neuroscience.…”

Section: Introductionsupporting

confidence: 60%

Maximizing the potential of EEG as a developmental neuroscience tool

Buzzell

Morales

Valadez

et al. 2023

Developmental Cognitive Neuroscience

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Section: Introductionsupporting

confidence: 60%

Maximizing the potential of EEG as a developmental neuroscience tool

Buzzell

Morales

Valadez

et al. 2023

Developmental Cognitive Neuroscience

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“…That is, there is a one-to-many mapping from hypothesis to statistical tests of that hypothesis, and this flexibility is related to the idea of the "garden of forking paths" (Gelman & Loken, 2014). Considerable attention has appropriately focused on exploiting the flexibility of raw data by selectively reporting statistically significant effects and the harmful consequences of doing so broadly (Chambers et al, 2014;Gelman & Loken, 2014;Kerr, 1998;Simmons et al, 2011) and for studies of psychophysiology (Baldwin, 2017;Clayson et al, 2019a;Clayson et al, 2020;Clayson et al, 2022c;Luck & Gaspelin, 2017). A multiverse analysis 1 respects the understanding that a final dataset used for analysis is the result of numerous decisions made by a researcher (Harder, 2020;Steegen et al, 2016), and any analysis that examines different choices to recording, scoring, or analyzing data can be considered a 'multiverse analysis'.…”

Section: Multiverse Analysismentioning

confidence: 99%

“…Open science practices are based on principles of transparency and wide sharing of information, and these practices are increasingly common in studies of psychophysiology (Clayson et al, 2021b;Clayson et al, 2022c). Multiverse analyses embrace the spirit of open science by transparently examining different decision points, from study design to statistical analysis.…”

Section: Open Sciencementioning

confidence: 99%

Beyond Single Paradigms, Pipelines, and Outcomes: Embracing Multiverse Analyses in Psychophysiology

Clayson

2023

Preprint

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Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study’s findings. These decisions create a “multiverse” of potential outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of the study findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. The review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as a guide, helping researchers make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

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“…Indeed, reproducibility issues have been undermining the foundation of scientific research in several fields, such as psychology (Open Science Collaboration 2015; Klein et al 2018), social sciences (Camerer et al 2016(Camerer et al , 2018, neuroimaging (Munafò et al 2017;Botvinik-Nezer et al 2020;Li et al 2021), preclinical cancer biology research (Errington, Mathur, et al 2021;, and more (Hutson 2018;Nissen et al 2016;Serra-Garcia and Gneezy 2021). As a response, there has been a rise in the development of tools and approaches to facilitate reproducibility and open science, in the spirit of Findability, Accessibility, Interoperability, and Reusability principles (FAIR) (Wilkinson et al 2016;Gorgolewski and Poldrack 2016;Nosek, Spies, and Motyl 2012;Nosek and Lakens 2014;Poldrack et al 2017;Poldrack, Huckins, and Varoquaux 2020;Poldrack et al 2019;Clayson, Keil, and Larson 2022). Beyond mitigating transparency and reproducibility issues, these practices provide important benefits for individual researchers by increasing exposure, reputation, chances of publication, number of citations, media attention, potential collaborations, and position and funding opportunities (Allen and Mehler 2019;McKiernan et al 2016;Nosek et al 2022;Markowetz 2015;Hunt 2019).…”

Section: Empirical Observations Of How Labs Conduct Research Indicate...mentioning

confidence: 99%

Open and reproducible neuroimaging: from study inception to publication

Niso¹,

Botvinik‐Nezer²,

Appelhoff³

et al. 2022

Preprint

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Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, and reproducible neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

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Open science in human electrophysiology

Cited by 13 publications

References 36 publications

Maximizing the potential of EEG as a developmental neuroscience tool

Maximizing the potential of EEG as a developmental neuroscience tool

Beyond Single Paradigms, Pipelines, and Outcomes: Embracing Multiverse Analyses in Psychophysiology

Open and reproducible neuroimaging: from study inception to publication

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