Peer review reading list

Are you in the process of reviewing a paper? do you need to evaluate the stats or experimental design in a manuscript but could do with some additional guidance? You need look no further, as eLife Ambassador Savio Chan has pulled together a list of articles (below) that can help you to identify and address common problems with published articles in the basic biomedical sciences.

1. Why you shouldn’t use bar graphs to show continuous data (and what to do instead)
   • Weissgerber T, Milic N, Winham S, Garovic VD. Beyond Bar Graphs: Time for a New Data Presentation Paradigm. PLoS Biol. 2015;13: e1002128. http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128
   • Free online tools for creating better graphics for scientific publications
   • Interactive dotplots, box plots and violin plots: Weissgerber, T.L., et al. Data visualization, bar naked: A free tool for creating interactive graphics. The Journal of biological chemistry 292, 20592-20598 (2017). http://www.jbc.org/content/292/50/20592.full.pdf
   • Interactive line graphs: Weissgerber TL, Garovic VD, Savic M, Winham SJ, Milic NM. From static to interactive: Transforming data visualization to improve transparency. PLoS Biol. 2016;14:e1002484. http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002484
   • Other free tools for creating static graphics: https://twitter.com/T_Weissgerber/status/953334933019398145

2. The problem with underpowered studies (Low power is a problem even when you find a significant difference)
   • Button, K.S., et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013). https://www.nature.com/articles/nrn3475

3. Why it’s important to report all excluded observations & the reasons for exclusion
   • Holman, C., et al. Where Have All the Rodents Gone? The Effects of Attrition in Experimental Research on Cancer and Stroke. PLoS Biol 14, e1002331 (2016). http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002331

4. P-values are often reported incorrectly: Why it’s important to present the information needed to verify the test result
   • Nuijten MB, Hartgerink CH, van Assen MA, Epskamp S, Wicherts JM. 2015. The prevalence of statistical reporting errors in psychology (1985-2013). Behav Res Methods. doi: 10.3758/s13428-015-0664-2. https://link.springer.com/article/10.3758/s13428-015-0664-2

5. Unblinded studies find larger effects
   • Holman, L., Head, M., Lanfear, R. Jennions, MD. Evidence of experimental bias in the life sciences: Why we need blind data recording. PLoS Biol. 13(7): e1002190. http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002190

6. Animal research: Follow the ARRIVE guidelines to improve transparency and reproducibility
   • Baker, D., Lidster, K., Sottomayor, A. & Amor, S. Two years later: journals are not yet enforcing the ARRIVE guidelines on reporting standards for pre-clinical animal studies. PLoS Biol 12, e1001756 (2014). http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001756
   • Macleod MR, The NPQIP Collaborative group. 2017. Findings of a retrospective, controlled cohort study of the impact of a change in Nature journals’ editorial policy for life sciences research on the completeness of reporting study design and execution. bioRxiv. doi: 10.1007/s11192-016-1964-8. https://www.biorxiv.org/content/early/2017/09/12/187245
   • Florez-Vargas, O., et al. Bias in the reporting of sex and age in biomedical research on mouse models. Elife 5(2016). https://elifesciences.org/articles/13615

7. Animal research: Multi-lab studies may improve reproducibility
   • Voelkl, B., Vogt, L., Sena, E. & Wuerbel, H. Reproducibility of preclinical animal research improves with heterogeneity of study samples. PLoS Biol 16, e2003693 (2018). http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2003693

8. Check for clusters of non-independent data (replicates, mice from the same litter, etc.): Did the authors account for non-independence in their analysis?
   • Lazic, S.E. The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC neuroscience 11, 5 (2010). https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-11-5

9. Beware of image manipulation
   • Bik, E.M., Casadevall, A. & Fang, F.C. The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications. MBio 7(2016). http://mbio.asm.org/content/7/3/e00809-16.long

Educational Articles for Peer Reviewers

1. Common misconceptions about data analysis and statistics
   • Motulsky HJ. Common misconceptions about data analysis and statistics. The Journal of Pharmacology and Experimental Therapeutics. 2014;351: 200-205. http://jpet.aspetjournals.org/content/351/1/200.full.pdf+html

2. Small samples are more likely to give spurious results
   • Wainer, H. The Most Dangerous Equation: Ignorance of how sample size affects statistical variation has created havoc for nearly a millennium. American Scientist 95, 249-256 (2007). http://www.jstor.org/stable/27858964

3. Transparency is the key to quality
   • Fosang AJ, Colbran RJ. Transparency is the key to quality. The Journal of Biological Chemistry. 2015;290(50): 29692-29694. http://www.jbc.org/content/290/50/29692.full.pdf+html

4. Research methods: Know when your numbers are significant
   • Vaux DL. Research methods: Know when your numbers are significant. Nature. 2012;492: 180-181. http://www.nature.com/nature/journal/v492/n7428/full/492180a.html