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bibliography.Rmd
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---
title: "Biliography & Recommended readings"
---
### Bibliography: Recommended/Supplemental Readings
This bibliography is a compiliation of some papers that extend ideas covered in this course and will be updated regularly.
## Weekly readings
Week listed is the one when it was assigned or mentioned.
Bullet points under articles indicate if that are required or recommended.
<br>
### Week 7: T-tests & effect sizes
**Anon. N.D.** Codebook cookbook: A guide to writing a good codebook for data analysis projects in medicine. [McGill University.](http://www.medicine.mcgill.ca/epidemiology/joseph/pbelisle/CodebookCookbook/CodebookCookbook.pdf)
* Contains information on how to make a data dictionary
**Broman, KW and K Woo. 2018.** Data organization in spreadsheet. [The American Statistician.](https://doi.org/10.1080/00031305.2017.1375989)
* Recommended
* **Excellent article for for understanding tidy data and data dictionaries**
**Ellis, SE and JT Leek. 2018.** How to share data for collaboration. [The American Statistician.](https://doi.org/10.1080/00031305.2017.1375987)
* Recommended reading
**Goodman et al. 2014.** Ten Simple Rules for the Care and Feeding of Scientific Data. [PLoS Computational Biology.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003542)
**Harrel (nd)**.[STATISTICAL GRAPHICS: Chapter 1](http://ubio.bioinfo.cnio.es/Cursos/CEU_MDA07_practicals/Further%20reading/Guidelines%20for%20quantitative%20presentations/Statistical%20graphics%20course%20Harrell.pdf)
Nakagawa & Cuthill. 2007. Effect size, confidence interval and statistical significance: a practical guide for biologists. [Biological Reviews](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1469-185X.2007.00027.x)
* To my knowledge on of the 1st (and few) proponents of reporting relative effect sizes like Cohen's d for use in ecology outside of meta-analyses.
**Ruxton. 2006.** The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test. [Behavioral Ecology.](https://academic.oup.com/beheco/article/17/4/688/215960)
* **Required**
* **Assigned reading on unequal variance t-tests**
**Savik.** Reporting P Values.[Journal of Wound, Ostomy and Continence Nursing](https://journals.lww.com/jwocnonline/fulltext/2006/05000/reporting_p_values.4.aspx?casa_token=2K9qcWiHw54AAAAA:GON4YfUcd7ZDmAj8jR1cmIYbnKlv_KXKGMzpZccvh_ozkbFtSHdm1NH5aUqSv6n2I7Iqenez3X_Ar8iptDu2bA)
* **Required**
* **Assigned reading on how to report p-values**
**Walker, J. 2018a.** [Combining data, distribution summary, model effects, and uncertainty in a single plot.](www.middleprofessor.com/files/quasipubs/harrell_plot_intro.html)
* **Required**
* **Assigned reading; Excellent discussion about why we should think in terms of effect sizes.** Fig. 2 in the blog currently isn't shown, but can be seen at Walker (2018b) and page 6 of Harrel(nd)
**Walker, J. 2018b.** When do we introduce best statistical practices to undergraduate biology majors? [Rapid Ecology.](https://rapidecology.com/2018/04/09/when-do-we-introduce-best-statistical-practices-to-undergraduate-biology-majors/)
* A nice abstract on Walker, J. 2018a
**Wilson, G, J Kitzes, et al. 2018.** Good enough practices in scientific computing. [PLoS Computational Biology.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510)
* Section 1 "Data management" of Box 1 is an excellent overview of key tasks in setting up and preserving your raw and analysis data.
<br>
### On deck
**Boldina & Beninger. 2016, Strengthening statistical usage in marine ecology: Linear regression. [Journal of Experimental Marine Biology & Ecology](https://www.sciencedirect.com/science/article/pii/S002209811530023X)
**Brinny, K.** The Rule of 3. http://dataabinitio.com/?p=320
* Store your digital data in at least 3 places (plus raw data sheets)! Eg, your hard drive, an external hard drive, and on the cloud (Box, Dropbox, private GitHub repository)
**Bryan, J.** Naming Files. [Speakerdeck](https://speakerdeck.com/jennybc/how-to-name-files)
* Excellent advice on naming files to facilitate downstream organization
**Bryan, J. 2018.** Happy Git for the userR. http://happygitwithr.com/
* One stop source for Git for R users
**Colegrave and Ruxton 2017.** Using Biological Insight and Pragmatism When Thinking about Pseudoreplication. [Trends in Ecology & Evolution.](https://www.sciencedirect.com/science/article/pii/S0169534717302690)
**Hart et al. 2016.** Ten Simple Rules for Digital Data Storage. [PLoS Comp Bio](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005097)
**Marwick et al. 2018.** Packaging Data Analytical Work Reproducibly Using R (and Friends). [Am Stat](https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1375986?casa_token=8soraoxwghMAAAAA:ksE_8SV3DB68ak4pDQ1LB8u6doQH5EPpmY6LfDKFfJOPlFAMGRS6ri6DmMuODYM-zLW8dhQcfRI)
**Parker et al. 2019.*8 Empowering peer reviewers with a checklist to improve transparency. [Nature Ecology & Evolution.](https://www.nature.com/articles/s41559-018-0545-z)
<br>
#### GitHub, Git & Version Control
Blischak, J. D., Davenport, E. R., & Wilson, G. (2016). A Quick Introduction to Version Control with Git and GitHub. PLoS Computational Biology, 12(1), 1–18.
https://doi.org/10.1371/journal.pcbi.1004668
**Bryan, J. 2018a** Happy Git for the userR. http://happygitwithr.com/
* One stop source for Git for R users
**Bryan, J. 2018b.** Excuse me, do you have a moment to talk about version control? Why Git? [American Stat.](www.tandfonline.com/doi/abs/10.1080/00031305.2017.1399928?journalCode=utas20)
* Good quick overivew of GitHub, especially if you will use it for collaboration, by the author of "Happy Git for the UserR."
Perez-Riverol et al. (2016). Ten Simple Rules for Taking Advantage of Git and GitHub. PLoS Computational Biology, 12(7), 1–11. https://doi.org/10.1371/journal.pcbi.1004947
Ram, K. (2013). Git can facilitate greater reproducibility and increased transparency in science. [Source Code for Biology and Medicine, 8](https://doi.org/10.1186/1751-0473-8-7)
**Vuorre, M., & Curley, J. P. (2018).** Curating Research Assets in Behavioral Sciences: A Tutorial on the Git Version Control System. [Advances in Methods and Practices in Psychological Science, 1–33.](http://journals.sagepub.com/doi/abs/10.1177/2515245918754826)
* Very thorough and readable intro
<br>
#### Reshaping data (dplyr, etc)
Richmond, Jenny. 2018. ["gather spread unite separate"](http://jenrichmond.rbind.io/post/gather-spread-unite-separate/)
#### Case studies
Code for understanding, reproducing and/or extending the analyses of these case studies will be used in the course or is available for self-study.
Skibiel et al. 2013. The evolution of the nutrient composition of mammalian milks. J. of Animal Eco. 82:1254–1264.
<br>
#### Nature Methods Tutorials
Nature Methods produces a number of short, useful tutorials. (Though inorder to be short some rely on compact equations more than I like.)
Altman, N., & M. Krzywinski. 2016. Analyzing outliers: influential or nuisance? Nature Methods 13:281–282.
Altman 2016. P values & the search for significance. Nat. Meth. 14:3–4.
Altman 2016. Regression diagnostics. Nature Methods 13:385–386.
Altman 2015. Simple linear regression. Nature Methods 12.
Krzywinski, M., & N. Altman. 2013. Significance, P values & t-tests. Nature Methods 10:1041–1042.
Krzywinski 2013. Error bars. Nat. Meth 10:921–922.
Krzywinski 2014. Visualizing samples w/ box plots. Nat. Meth. 11:
<br>
#### R introduction
Fox, J. 2006. Getting Started With R:1–42.
<br>
#### Regression
Fox, J. Dummy-Variable Regression. Applied Regression Analysis & Generalized Linear Models.
Fox, J. Bootstrapping Regression Models.
Fox, J., & S. Weisberg. 2011. Diagnosing Problems in Linear & Generalized Linear Models. An R Companion to Applied Regression:285–328.
Lever, J. et al 2016. Model selection & overfitting. Nature Methods 13:703–704.
Schielzeth, H. 2010. Simple means to improve the interpretability ofregression coefficients. Methods in Ecology & Evolution 1:103–113.
Steel, E. A. et al 2013. Applied statistics in ecology: common pitfalls & simple solutions. Ecosphere 4:art115.
Zuur, A. F. et al. 2010. A protocol for data exploration to avoid common statistical problems. Methods in Ecology & Evolution 1:3–14.
<br>
#### Reproducibility
A major emerging issue in science is how to assure the quality of our lab/field data and the integrity of our anlayses. Below are some examples from a rapidly growing literature on this topic.
Anon. 2015. Let’s think about cognitive bias. Nature.
Baggerly & Coombes. 2009. Deriving chemosensitivity from cell lines: Forensic bioinformatics & reproducible research in high-throughput biology. Ann. of App. Statistics 3:1309–1334.
Baker 2016. Reproducibility: Respect your cells. Nature 537:433–435.
Casadevall & Fang. 2010. Reproducible science. Infec. & Immunity 78:4972–4975.
Clark et al2016. Scientific Misconduct: The Elephant in the Lab. A Response to Parker et al. TREE 31:899–900.
Forstmeier et al 2016. Detecting & avoiding likely false-positive findings – a practical guide. Biological Reviews.
Gelman 2015. Working through some issues. Significance 12:33–35.
Gelman & Loken. 2014. The statistical crisis in Science:4–7.
Ioannidis 2014. How to Make More Published Research True. PLoS Med. 11.
Ioannidis, J. P. a., & M. J. Khoury. 2011. Improving validation practices in “omics” research. Science 334:1230–1232.
Ioannidis, J. P. A. 2003. Genetic associations: false or true? Trends in Molecular Medicine 9:133–135.
Ioannidis, J. P. A. 2005. Microarrays & molecular research: noise discovery? Lancet, The 365:454–455.
Landis et al. 2012. A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490:187–91.
Nuzzo, R. 2014. Statistical errors: P values, the “gold standard” of statistical validity, are not as reliable as many scientists assume. Nature 506:150–152.
Parker et al 2016. Transparency in Eco. & Evo: Real Problems, Real Solutions. Trends in Eco. & Evo. 31:711–719.
Schnitzer & Carson. 2016. Would Ecology Fail the Repeatability Test? BioScience 66:98–99.
Yamada & Hall. 2015. Reproducibility & cell biology. J. of Cell Bio. 209:191–193.
<br>