Talk by Karl Broman
We are very excited to have Professor Karl Broman of the University of Winscosin deliver a talk for us at 4/11, 17:30 at LGRT 1634.
Title: Steps toward reproducible research
A minimal standard for data analysis and other scientific computations is that they be reproducible: that the code and data are assembled in a way so that another group can re-create all of the results (e.g., the figures in a paper). Adopting a workflow that will make your results reproducible will ultimately make your life easier; if a problem (or question) arises somewhere down the line, it will be much easier to correct (or explain). But organizing analyses so that they are reproducible is not easy. It requires diligence and a considerable investment of time: to learn new computational tools, and to organize and document analyses as you go. Nevertheless, partially reproducible is better than not at all reproducible. Just try to make your next paper or project better organized than the last. There are many paths toward reproducible research, and you shouldn’t try to change all aspects of your current practices all at once. Identify one weakness, adopt an improved approach, refine that a bit, and then move on to the next thing. I’ll offer some suggestions for the initial steps to take towards making your work reproducible.
About the speaker
Karl Broman received a PhD in Statistics from the University of California, Berkeley; he was Terry Speed’s student. He was a postdoctoral fellow with Jim Weber, a geneticist, in Marshfield, Wisconsin, where he developed the Marshfield human genetic maps and studied recombination variation and crossover interference. He was a faculty member in the Department of Biostatistics at Johns Hopkins University, and then moved to the University of Wisconsin-Madison, where he is now professor. He is an applied statistician working on problems in genetics and genomics, with a particular focus on the genetic dissection of complex traits in experimental organisms. He develops the R package, R/qtl.
A minimal standard for data analysis and other scientific computations is that they be reproducible: that the code and data are assembled in a way so that another group can re-create all of the results (e.g., the figures in a paper). I will discuss my personal struggles to make my work reproducible and will present a series of suggested steps on the path towards reproducibility (see http://kbroman.org/steps2rr).comments powered by Disqus