Reproducible Quantitative Methods

Instructor Guide, Lesson 8

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

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Topics and Resources

  1. Further topics in R

    Student directed exploratory analysis. As in week 7, you may consider putting a plotting lesson in at this time.

  2. Ethics and risks in sharing data

    This can be a tricky one. 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 your students are completely converted to open science, they may have advisors or collaborators that are a little more hesitant, or even outright against open data. Be gentle, but offer counter examples. Encourage students 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.


  1. Project workshop time
  2. Keep working on student-directed analyses.


Challenges to working open

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.


Which concerns about data sharing do you feel have merit?

How can we mitigate these issues?

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