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?
The reason COVID-19 admissions calculators by Victor Grech was retracted was because they based their paper on very early data and drew incorrect conclusions. The paper seems to throw around numbers without really putting thought into where they are coming from. This author could implement rule 7 and 10. We know they got the data from the World Health Organization, but do not know what formulas they are using to create this “calculator”.
- 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.
Rule 1: For Every Result, Keep Track of How It Was Produced is probably the rule I am most likely to follow. I have found it is not only helpful for reproducibility, but also for recording for myself. Knowing how I got things makes it 10 times easier when I am going back and writing about them. The rule that is hardest to follow is Rule 2: Avoid Manual Data Manipulation Steps. A lot of what we do is data cleaning and manipulation. I do not think I can avoid doing this in my research.
Submission
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