Raincloud plots: a multi-platform tool for robust data visualization
Aarhus University · University of Cambridge · +7 more institutions
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,…
Citation impact
- FWCI
- 80.02
- Percentile
- 100%
- References
- 23
Authors
5- MAMicah AllenCorresponding
Aarhus University, University of Cambridge, Aarhus University Hospital
- DPDavide Poggiali
University of Padua
- KWKirstie Whitaker
The Alan Turing Institute
- TRTom R. Marshall
University of Oxford, Wellcome Centre for Integrative Neuroimaging
- RKRogier Kievit
Radboud University Nijmegen, New Zealand Brain Research Institute
Topics & keywords
- Visualization
- Computer science
- Computer graphics (images)
- Data science
- Data mining
Funding
- WWellcomeAward: 107392
- AUAarhus Universitet
- WTWellcome TrustAwards: R272-2017-4345, /Z/15/Z, 107392/Z/15/Z, 754513
- ECEuropean CommissionAward: 754513
- AUAarhus Universitets ForskningsfondAwards: 754513, R272-2017-4345
- LLundbeckfondenAwards: R272-2017-4345, 754513
- EAEngineering and Physical Sciences Research CouncilAwards: N510129, EP/N510129/, EP/N510129/1, EP/N510129/1
- H2Horizon 2020Award: Grantagreementno754513