- 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 Retraction Watch, under the “Top 10 most highly cited retracted papers”, is a paper entitled, “Selective killing of cancer cells by a small molecule targeting the stress response to ROS”. This paper is listed 10 on the list and was published in the journal Nature in 2011. This article recieved 612 citations prior to its retraction in 2018 and 206 citations even after its retraction. There were also two corrigendums to this article prior to its retraction; one correigendum in 2012 and another in 2015. The integrity of the paper was comprimised as there were issues with reported data, figure legends, and reported measurements.
The official reason for retaction of this article is stated as, “This Letter is being retracted owing to issues with Fig. 1d and Supplementary Fig. 31b, and the unavailability of original data for these figures, which raises concerns regarding the integrity of the figures. Nature published two previous corrections related to this Letter1,2. These issues in aggregate undermine the confidence in the integrity of this study. Authors Michael Foley, Monica Schenone, Nicola J. Tolliday, Todd R. Golub, Steven A. Carr, Alykhan F. Shamji, Andrew M. Stern and Stuart L. Schreiber agree with the Retraction. Authors Lakshmi Raj, Takao Ide, Aditi U. Gurkar, Anna Mandinova and Sam W. Lee disagree with the Retraction. Author Xiaoyu Li did not respond.” as stated through the Retraction Note.
Some of the rules by Sandve et al. that may have helped in this situation include:
Rule 2: Avoid Manual Data Manupulation steps
Rule 7: Always Store Raw Data behind Plots
Rule 9: Connect Textual Statements to Underlying Results
and
Rule 10: Provide Public Access to Scripts, Runs, and Results
- 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.
The rule that I am most lilely to follow from Sandve et al ’s Ten Simple Rules is Rule 1: For Every Result, Keep Track of How It Was Produced …
This is a rule that I have been trying to follow since I have began working with code for my research. I began to work with code for my project as I am trying to formulate a novel method for document analysis and the importance of reproducibility for this process is very important.
The rule that I will likey not be able to follow in my future projects is Rule 3: Archive the Exact Versions of All External Programs Used …
This rule is more difficult to follow as I am not always aware of the particular versions of packages or resources that I am using. Through lab procedure I understand how important this rule is but it is not something that I have been great in doing so far with my coding practices.