**Yihui Xie**, and kindly contributed to R-bloggers)

I'm attending a workshop on reproducibility at ICERM (Brown University) this week. I really appreciate this great opportunity offered by ICERM, Randy and Victoria.

It is pretty exciting to meet people that you only knew before through indirect ways. One coincidence was that I met Fernando here (for the first time)! We did not know each other before I wrote the IPython post back in November, and I did not expect that we would meet each other so soon. Anyway, it is great to see this extremely energetic guy in person. Nerdy as I am, I immediately asked him (did I even say hello?) how IPython saves and displays plots, and he quickly showed me on the whiteboard.

Some simple notes as bullet points:

- One big question about reproducible research is,
*where is the reward system*? e.g. why should young researchers spend more time on making papers reproducible instead of publishing more papers? There seems to be no immediate reward (although some argue that papers with data/code available have higher citation rates) - You should look at the Fortran code blackened out in Bill Rider's slides; it is not that people do not want to share...
- Victoria has a nice historical review of reproducible research
- I learned MacTutor from Jon Borwein's talk, which seems to be a good old website like Wikipedia (for Math)
- I enjoyed the talk by David Donoho most; they wrote a paper "Deterministic Matrices Matching the Phase Transitions of Gaussian Random Matrices", and what is truly amazing is that this paper was done through "crowd sourcing"; guess who is the "crowd"? His Stat330/CME362 students (as well as the TA)! They used Dropbox, GIT, runmycode and clusters. The three big points:
- Math as science: mathmaticians should learn science and scientific publication (emphasis on publishing empirical results)
- Research as teaching: teaching can be turned into research; see the above paper
- Code development as science: I especially resonate with this point -- code development actually has mature models and practices for a long time, which should be the ideal (or standard) paradigm of doing science; it is rare to see an open source software package published with one single final version; instead we often see versions (version control and semantic versioning) which mark the progress of the package, and we have a full history of how it was written, but papers almost always only have one version

- I learned HOL light from Tom Hales' talk, which is a computer program for proving theorems (does it help me write my PhD thesis?)
- David Bailey talked about High-Precision Computation and Reproducibility; I'm not familiar with this area but the talk is very interesting, e.g. a change in the float-point library can lead to different observations of particles in physics (some particles might have "gone" after you replace the library); I did not realize numeric precision has such a profound influence

Keep an eye on the workshop website if you are interested.

BTW, to follow up David's crowd sourcing, my advisor Di Cook did something similar earlier this year but less seriously: the students who took Stat585 at Iowa State collaborated on Github for a fiction in statistics when we were learning GIT in that class, which was actually a lot of fun...

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**Yihui Xie**.

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