Poldrank and Poline’s new paper in TICS (2015) asserts pretty clearly that the field of neuroimaging is behind on open science. Data and analysis code are rarely shared, despite the clear need: studies are often underpowered, there are multiple possible analytic paths.
They offer some guidelines for best practice around data sharing and re-analysis:
- Recognise that researcher error is not fraud
- Share analysis code, as well as data
- Distinguish ‘Empirical irreproducibility’ (failure to replicate a finding on the original researchers’ own terms) from ‘interpretative irreproducibility’ (failure to endorse the original researchers’ conclusions based on a difference of, e.g., analytic method)
They also over three useful best practice guidelines for any researchers who are thinking of blogging a reanalysis based on other researchers’ data (as Russ has himself)
- Contact the original authors before publishing to give them right of reply
- Share your analysis code, along with your conclusions
- Allow comments
And there are some useful comments about authorship rights for research based on open data. Providing the original data alone should not entitle you to authorship on subsequent papers (unless you have also contributed significant expertise to a re-analysis). Rather, it would be better if the researchers contributing data to an open repository publish a data paper which can be cited by anyone performing additional analyses.
REFERENCE
Poldrack, R. A., & Poline, J. B. (2015). The publication and reproducibility challenges of shared data. Trends in Cognitive Sciences, 19(2), 59–61.