ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency
- Published
- Accepted
- Subject Areas
- Bioinformatics, Science and Medical Education, Statistics, Data Science
- Keywords
- data visualization, raw data, behavior, data transparency, R package
- Copyright
- © 2019 Garnier et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency. PeerJ Preprints 7:e27665v1 https://doi.org/10.7287/peerj.preprints.27665v1
Abstract
Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small sample representation. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of a behavior and/or process observations. In this paper, we present ViSiElse, a R-package that offers a new approach in raw data visualization.
Methods. This visualization tool was developed as a package of the open-source software R to provide a solution to both the lack of tools allowing visual insights of a whole dataset and the lack of innovative tools for raw data transparency.
Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps and all participants in a single graph. Individuals and/or group behavior can easily be assessed and supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse allows a global visualization of actions, acquired from timestamps in any quantifiable observations.
Author Comment
This is a submission to PeerJ for review.