Interested in learning how to make your data analysis and other scientific computations reproducible?
The Center for Quantitative Methods and Data Science, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a virtual seminar series on a Wednesday each month from 2:00-3:00PM.
The session on Wednesday, December 16 will feature Karl Broman, PhD. He will give a talk titled 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 and table 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. Dr. Karl Broman will offer some suggestions for the initial steps to take towards making your work reproducible.
Dr. Karl Broman is a Professor in the Department of Biostatistics & Medical Informatics at the University of Wisconsin-Madison. Dr. Broman is an applied statistician working on the genetics of complex diseases in experimental organisms. He develops the R package, R/qtl, has written a number of short tutorials useful for data scientists, and is very keen to develop tools for interactive data visualization (to view an example, click here).
Date: Wednesday, December 16, 2020, 2:00-3:00PM
To attend, please enroll via Tufts CTSI I LEARN here.