Reproducible Quantitative Methods

Instructor Guide, Lesson 7

Programming in R, continued /Authorship and citation practices for non-manuscript research products

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Topics and Resources

  1. Even more with R (writing simple software)

    Review everything you did last week, and do some more! This is where things start getting very freeform and directed by your data- start really working towards the scientific questions your students asked of the data.

    Depending on where you are with your data and analysis, you may want to introduce plotting with ggplot2 at this point instead of week 9. This will vary with the type of data and the questions being asked of it, so use those to guide you.

Exercises

  1. Loops, Conditionals and functions

    2. And student directed analyses! Look! See what you find! This is the fun part :)

Discussion

Authorship on data, authorship in collaboration

Now that you're a little deeper into the course, the students are ready for a more in-depth discussion about scientific authorship. The readings here are meant to present some extreme views and really provoke discussion.

Readings

Pubwication of software papers, and authorship on them. This is a blog post by Titus Brown, a professor of data-intensive biology at UC Davis. Titus’ group built a software product, khmer, out in the open, and invited contributions from the community. Long story short, he awarded authorship to everyone who contributed when he published a paper on the subject, no matter what the contribution was. This sparked a big debate in the community about what authorship really meant.

Data Sharing (an editorial from the New England Journal of Medicine).This editorial presents another view of authorship- namely, making the argument that data is essentially owned by those who collected it, and that no derivative works should be created without the data owner’s permission.

A Fundamental Difference of Opinion is blog post by Christie Bahlai in response to the above editorial.

Questions

Why is there a discrepancy between the way credit is offered to data and software?

Are data and software research products?

What is the best way to credit data and software creators?

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