Reproducible research

• Git tutorial by John McDonnel
• Introduction to Version Control with a focus on Git by Dan McGlinn
• Reproducible research in computational science by Karthik Ram
• knitr and RStudio by Yihui Xie

Sources for this presentationa available at

• Reproducible research: Concepts and tools

"Opening up access to the data and software, not just the final publication, is one of goals of the open science movement"
(Ram, 2013)

• With replication, independent investigators address a scientific hypothesis and build up evidence for or against it (Peng, 2011)
• Allows others to build upon existing work and use it to test new ideas and develop methods (Ram, 2013)
• While currently there is unilateral emphasis on "first" discoveries, there should be as much emphasis on replication of discoveries (Ioannidis, 2005)

Wolkovich et al. 2012

Peng 2011

Sirkiä et al. 2012

• Automatically regenerate documents when code, data, or assumptions change
• Eliminate transposition errors that occur when copying results into documents
• Preserve contextual narrative about why analysis was performed in a certain fashion
• Documentation for the analytic and computational processes from which conclusions are drawn

Xie, 2012

Adapted from Markus Kainu

# Quick summary
library(ggplot2)
summary(cars)

##      speed           dist    
##  Min.   : 4.0   Min.   :  2  
##  1st Qu.:12.0   1st Qu.: 26  
##  Median :15.0   Median : 36  
##  Mean   :15.4   Mean   : 43  
##  3rd Qu.:19.0   3rd Qu.: 56  
##  Max.   :25.0   Max.   :120

# Quick plot of the data
qplot(speed, dist, data = cars) + geom_smooth()

• It stores snapshots of your projects
• ...It also stores the relationships between those snapshots

McDonnel, 2012

1. Lab notebook
2. Facilitating collaboration
3. Backup and failsafe against data loss
4. Freedom to explore new ideas and methods
5. Mechanism to solicit feedback and reviews
6. Increase transparency and verifiability
7. Managing large data
8. Lowering barriers to reuse

Ram, 2013