Raincloud plots: a multi-platform tool for robust data visualization
Micah Allen,Micah Allen,Micah Allen,Davide Poggiali,Kirstie Whitaker,Tom R. Marshall,Rogier A. Kievit +6 more
- Vol. 4, pp 63-63
TLDR
This tutorial paper provides basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provides open-source code for their streamlined implementation in R, Python and Matlab.Abstract:
Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.read more
Citations
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Biophysical and Mechanistic Models for Disease-Causing Protein Variants.
TL;DR: This work focuses on the role of changes inprotein stability as a driver for disease, and how experiments, biophysical models, and computation are providing a framework for understanding and predicting how changes in protein sequence affect cellular protein stability.
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The Psychological Impact of 'Mild Lockdown' in Japan during the COVID-19 Pandemic: A Nationwide Survey under a Declared State of Emergency.
TL;DR: Psychological distress severity was influenced by specific interactional structures of risk factors: high loneliness, poor interpersonal relationships, COVID-19-related sleeplessness and anxiety, deterioration of household economy, and work and academic difficulties.
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Best practices for fNIRS publications
Meryem A. Yücel,Alexander von Lühmann,Alexander von Lühmann,Felix Scholkmann,Judit Gervain,Ippeita Dan,Hasan Ayaz,David A. Boas,Robert J. Cooper,Joseph P. Culver,Clare E. Elwell,Adam T. Eggebrecht,Maria Angela Franceschini,Christophe Grova,Fumitaka Homae,Frédéric Lesage,Hellmuth Obrig,Ilias Tachtsidis,Sungho Tak,Yunjie Tong,Alessandro Torricelli,Heidrun Wabnitz,Martin Wolf +22 more
TL;DR: In this article, the authors provide guidelines to enhance the reliability, repeatability, and traceability of reported functional near-infrared spectroscopy studies and encourage best practices throughout the community.
Journal ArticleDOI
Me, myself, bye: regional alterations in glutamate and the experience of ego dissolution with psilocybin
Natasha L. Mason,Kim P. C. Kuypers,Felix Müller,Felix Müller,J. Reckweg,Desmond H. Y. Tse,Stefan W. Toennes,Nadia R P W Hutten,Jacobus F.A. Jansen,Peter Stiers,Amanda Feilding,Johannes G. Ramaekers +11 more
TL;DR: It is demonstrated that psilocybin induced region-dependent alterations in glutamate, which predicted distortions in the subjective experience of one’s self (ego dissolution), which may provide a neurochemical basis for therapeutic effects as witnessed in ongoing clinical trials.
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Navigating the garden of forking paths for data exclusions in fear conditioning research
Tina B. Lonsdorf,Maren Klingelhöfer-Jens,Marta Andreatta,Marta Andreatta,Tom Beckers,Anastasia Chalkia,Anna Gerlicher,Valerie L. Jentsch,Shira Meir Drexler,Gaëtan Mertens,Jan Richter,Rachel Sjouwerman,Julia Wendt,Christian J. Merz +13 more
TL;DR: It is illustrated how flexibility in data collection and analysis can be avoided, which will benefit the robustness and replicability of research findings and can be expected to be applicable to other fields of research that involve a learning element.
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