Reproducible Quantitative Methods

Lesson 8

Student-directed processing and analysis of project data / Data and code sharing challenges/Visualization principles

Topics and Resources

Further topics in R

Student directed exploratory analysis.
Visual communication with data

We're going to introduce visualization concepts at the 10,000 foot view this week to get you thinking about how you're going to approach visualizations of your data for your project.This is a great opportunity to talk about accessibility, communication and openness in science.
Ethics and risks in sharing data

We've talked about this a fair bit, and I don't want to beat a dead horse, but I just wanted to offer a few more resources. Data and code sharing can be tricky to navigate as you go out into the world. Everyone has a story about how their third cousin’s best friend was ‘scooped,’ and it’s important to be reassuring and sensitive to these concerns. Even if you are are completely converted to open science, you will likely encounter advisors or collaborators that are a little more hesitant, or even outright against open data. Be gentle, but offer counter examples. Encourage colleagues to weigh benefits and risks. ‘Scooping’ from published data source has been empirically demonstrated to be a rare phenomenon, but even some open advocates have had trouble supporting blanket open data policies.

Also, reread Challenges to Open Data and How To Respond. If you have access to this paywalled article through your library, you can look at Archiving Primary Data: Solutions for Long-Term Studies and have the students read it. It should be noted that not a single author of this paper was associated with the Long Term Ecological Research network (at least at the time of its publication) so this should probably not be taken as the view of long term scientists, who are generally awesome data sharers. This is also a good time to think About Data Repositories.

Exercises

Project workshop time

Keep working on student-directed analyses.

Discussion

Visualization for communication

For this discussion, there's a lot of material we could potentially look at, but the idea is to give you a taste of the things we need to consider when thinking about our visualizations. Conceptually, this is pretty open ended, but I want you to leave with an idea of why they should put a little bit more thought into graphing their data. Two readings I like are:

• Ten simple rules for better figures

• Finding the Right Color Palettes for Data Visualizations