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?
Parvin’s answer: From the Retraction Watch website, among the top 10 most highly retracted papers, I picked the paper with the title of “Selective killing of cancer cells by a small molecule targeting the stress response to ROS”. This paper has been retracted because of issues and data availability related to Fig. 1d, and supplementary Fig. 31b. Basically, these two figures shows the results of the experiments that authors did; in the other words, authors summarized their results using showing the associations by graphical figures. Unfortunately, the authors did not provided the original data related to these two figures when the Nature asked them. The fact of not providing the original results of their experiment questioned the integrity and correctness of this study. Some of the authors agreed with the retraction while rest of the authors never reply to this letters. Based on the article with the tittle of “Ten Simple Rules for Reproducible Computational Research”, I think rule number 7 which is “Always Store Raw Data behind Plots” is the first helpful rule to this study. If the authors had saved the raw data related to the plots, they could share them. Then, possibly rule number 5 which is “Record All Intermediate Results, When Possible in Standardized Formats” could help them to have the intermediate and final results to defend from themselves. Then, rule number 10 which is “Provide Public Access to Scripts, Runs, and Results” could prevent any problem or suspicions related to the experiments.
- 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.
Parvin’s answer: I always follow all the rules except than rules number 4 and 10. For me, these two rules are not easy to follow because of some reasons. About rule number 4, I sometimes need to update my R or Anaconda version, or update some packages; keep tracking of theses details for each research parts is not easy for me because I may forget to do it while I am busy. About rule number 10, I often cannot publish my scripts and data because most of data set I use are not public. People must have grants and enrolled research projects, pay for the data to have them. THerefore, publishing the codes and data is limited.
Submission
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author: "Your Name"
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date: "2023-02-23"
categories: "Ethics and Reproducibility..."
---