Conery, R. 2016. The imposter’s handbook. Big Machine, Inc.
Haddock, S., and C. Dunn. 2010. Practical computing for biologists. Sinauer Associates.
Noble, W.S. 2009. A quick guide to organizing computational biology projects. PLoS Computational Biology. 5. doi:10.1371/journal.pcbi.1000424.
How to organize a small to medium-sized bioinformatics project.
Patil, P. et al. 2016. A statistical definition for reproducibility and replicability. doi:10.1101/066803.
Scopatz, A., and K.D. Huff. 2015. Effective computation in physics. O’Reilly Media.
A comprehensive introduction to scientific computing in Python
Taschuk, M., and G. Wilson. 2017. Ten simple rules for making research software more robust. PLoS Computational Biology. 13. doi:10.1371/journal.pcbi.1005412.
A short guide to making research software usable by other people.
Wickham, H. 2014. Tidy data. Journal of Statistical Software. 59. doi:10.18637/jss.v059.i10.
The defining paper on tidy data.
Wilson, G. et al. 2014. Best practices for scientific computing. PLoS Biology. 12. doi:10.1371/journal.pbio.1001745.
Outlines what a mature research software project should look like.
Wilson, G. et al. 2017. Good enough practices in scientific computing. PLoS Computational Biology. 13. doi:10.1371/journal.pcbi.1005510.
Outlines what a “good enough” research software project should look like.
Yenni, G.M. et al. 2019. Developing a modern data workflow for regularly updated data. PLOS Biology. 17:e3000125. doi:10.1371/journal.pbio.3000125.