in Writing on Writing, Workflows, Manuscripts, Science
For most, scientific writing is a chore: how many people actually enjoy writing grant proposals, dealing with typesetting, or just sharing the details behind an experiment? I, for one, actually quite like writing (perhaps for the wrong reason) because it provides me an opportunity to play around with workflows, discover new command line tools, or just mess around with typography and typesetting. In this post, I’ll cover some of the steps I take when writing; be it for grants or for publications. This post will be split into three parts: working solo or collaboratively, and themes common to both.
in Programming on Python, Programming, Data analysis
Data analysis usually (read: should) follow some consideration about what the objective of the analysis is, and what kind of transformations to a dataset is required to get to the objective. As part of planning, one needs to think as far ahead as possible as to what kind of information needs to be propagated throughout your analysis pipeline, which will inevitably evolve with your objectives and your methodology. What this article aims to convey is to convince you that it’s always worth taking some time to construct scientific analysis code properly, in the spirit of maintability for you and whomever may have the (mis)fortune of working with it! While these ideas are fairly language agnostic, the examples will be written in Python, which I think is the most natural language for implementing these concepts thanks to its object-oriented nature and readability.