Prompt:
In May 2015 Science retracted - without consent of the lead author - a paper on how canvassers can sway people’s opinions about gay marriage, see also: http://www.sciencemag.org/news/2015/05/science-retracts-gay-marriage-paper-without-agreement-lead-author-lacour The Science Editor-in-Chief cited as reasons for the retraction that the original survey data was not made available for independent reproduction of results, that survey incentives were misrepresented and that statements made about sponsorships turned out to be incorrect.
The investigation resulting in the retraction was triggered by two Berkeley grad students who attempted to replicate the study and discovered that the data must have been faked.
FiveThirtyEight has published an article with more details on the two Berkeley students’ work.
Malicious changes to the data such as in the LaCour case are hard to prevent, but more rigorous checks should be built into the scientific publishing system. All too often papers have to be retracted for unintended reasons. Retraction Watch is a data base that keeps track of retracted papers (see the related Science magazine publication).
Read the paper Ten Simple Rules for Reproducible Computational Research by Sandve et al.
Write a blog post addressing the questions:
- Pick one of the papers from Retraction Watch that were retracted because of errors in the paper (you might want to pick a paper from the set of featured papers, because there are usually more details available). Describe what went wrong. Would any of the rules by Sandve et al. have helped in this situation?
From the retraction watch, I picked the paper “Selective killing of cancer cells by a small molecule targeting the stress response to ROS” The retraction reason was the unavailability of original data for the figures. I was surprised that it was still published in the “nature” journal.
The rules by Sandve et al. will defintely help in this situation. Specially the Data related rules which this paper had mistakes in. Rule 2, Rule 5, and Rule 7 would have been the best thing to do for this paper.
- After reading the paper by Sandve et al. describe which rule you are most likely to follow and why, and which rule you find the hardest to follow and will likely not (be able to) follow in your future projects.
I will most likely to follow Rule 4 which is version control. I did not know about how good the git version control is and after learning it in the class I am more likely to follow it and even like to have versions of my code.
The rule which might be hardest to follow for me is Rule 5, keeping track of intermediate results. We generate tons of data in my lab. Not everything is useful and some of the results are just useless. Keeping track of them takes time which could be better utilized in storing only the successful results. However, I will try to find a way to do this efficiently and make a process where I could dump all the data in cybox folder quickly.
Submission
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author: "Your Name"
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---