Homework 9.3: Polishing (20 or 40 pts extra credit)¶
Choose either problem 9.1 or 9.2. Imagine that the analysis you did was part of a publication. This includes the work you did in previous problems working on the respective data sets. For example, for the Caulobacter analysis, you should start with the raw images (bacterium_1.tif and bactrium_2.tif) and go all the way through to your analysis in problem 9.2.
You will choose one of the following two options, but regardless of what you choose, you need to create a separate package with .py files containing code you will use in your analysis. You should create this as a separate repository. I would encourage you to use a public repository for this, but if you want to use a private one, you can; just give me access to it.
Note: You may do this problem for both 9.1 and 9.2 (separately), and the extra credit points add. That is, you can earn a maximum of 80 extra credit points.
20 pts extra credit option¶
Make a polished Jupyter notebook or set of notebooks that would be part of the supplement of your paper. It should clearly state what the problem is, go through modeling details, and demonstrate the analysis. Someone reading it should be able to reproduce your work.
I know this is roughly the standard we’ve had for the notebooks for homework submission. (We have been somewhat lax on this standard in grading.) For this submission, it should bereallypolished, something you would feel proud to put out into the scientific record.
40 pts extra credit option¶
Build a website similar to what Griffin Chure showed in lecture 9. Don’t forget that his reproducible website repository wiki has instructions on how to build the website. You can either build a stand-alone website or one hosted on GitHub. Bear in mind, though, that if you host your result on GitHub, it will be public. The whole world will see it. A stand-alone website, on the other hand, can be kept private if you like. You can keep the website contents in your repository, and I will serve it up locally to grade it. Alternatively, if you can serve it wherever you like and password protect it (though this requires some steps not discussed in the reproducible website wiki).
Obviously, if you do this option, you cannot write a whole paper. Instead, have a brief abstract discussing what you were studying. Your work for this problem should mostly be in the Analysis section of the website. For the Data section of the website, you can use the links I included in the homework statements to the original data sets.