How to share data for collaboration
1
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
2
Center for Computational Biology, The Johns Hopkins University, Baltimore, Maryland, United States
- Published
- Accepted
- Subject Areas
- Statistics, Data Science
- Keywords
- data, collaboration, data analysis, statistician, analysis, tidy data, guidelines
- Copyright
- © 2017 Ellis 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
- 2017. How to share data for collaboration. PeerJ Preprints 5:e3139v1 https://doi.org/10.7287/peerj.preprints.3139v1
Abstract
Within the statistics community, a number of guiding principles for sharing data have emerged; however, these principles are not always made clear to collaborators generating the data. To bridge this divide, we have established a set of guidelines for sharing data. In these, we highlight the need to provide raw data to the statistician, the importance of consistent formatting, and the necessity of including all essential experimental information and pre-processing steps carried out to the statistician. With these guidelines we hope to avoid errors and delays in data analysis.
Author Comment
This is a preprint submission to PeerJ Preprints.