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N8 CIR Northern tour ReproHack slides |
ReproHack, introduction, slides |
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Event Repository: http://bit.ly/n8-reprohacks
Contains all event information and links to materials
- Research Software Engineer University of Sheffield
- 2019 Fellow Software Sustainability Institute
- Software Peer Review Editor rOpenSci
- Co-organiser Sheffield R Users Group
I believe there's lots to learn about Reproducibility from working with real published projects.
Stitch!
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Project review and team formation
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Select and register your project
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Work on your project!
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Re-group part-way through.
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Feedback at the end (group & authors)
Event governed by ReproHack Code of Conduct
- How easy was it to gain access to the materials?
- How easy / automated was installation?
- Did you have any problems?
- Were data clearly separated from code and other items?
- Were large data files deposited in a trustworthy data repository and referred to using a persistent identifier?
- Were data documented ...somehow...
Was there adequate documentation describing:
- how to install necessary software including non-standard dependencies?
- how to use materials to reproduce the paper?
- how to cite the materials, ideally in a form that can be copy and pasted?
- Were you able to fully reproduce the paper? ✅
- How automated was the process of reproducing the paper?
- How easy was it to link analysis code to:
- the plots it generates
- sections in the manuscript in which it is described
- Did results (e.g. model outputs, tables, figures) differ to those published? By how much?
- Were missing dependencies?
- Was the computational environment not adequately described / captured?
- Regroup part way through to discuss progress and troubleshoot any sticking points
- Feedback to authors using form by end of session
- Feedback to group at the end, contribute to discussions
- Try and run additional analyses.
- Create new plots.
- Combine materials with your own or other open materials available on the web!
- Consider attempting replication!
- Replications could be considered for publication in ReScience C Journal
ReScience C is an open-access peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible.
- Repeating a published protocol
- Respecting its spirit and intentions
- Varying the technical details, e.g. using different software, initial conditions, etc.
Change something that everyone believes shouldn’t matter, and see if the scientific conclusions are affected
- Have a look at the papers available for reproduction
- Fine to work individually
- Add your details to the hackpad.
- Register your team and paper on the hackpad
- Which paper have you selected? Briefly describe what it's about.
- Briefly describe the approach to reproducibility the paper has taken.
- Anything in particular you like about the paper's approach so far?
- Anything you're having difficulty with?
- Please complete the feedback form for authors
- Feel free to record general findings the hackpad
- So, how did you get on?
- Final comments.
- If there's time, tackle some discussion topics (see hackpad).
- On post-its: One thing you liked, one thing that can be improved.
- The Turing Way: a lightly opinionated guide to reproducible data science.
- Packaging data analytical work reproducibly using R (and friends): how researchers can improve the reproducibility of their work using research compendia based on R packages and related tools