ROOM: Room 10 – Anasazi
Reproducibility is a cornerstone of science. Unfortunately, several resent papers have highlighted significant failures to reproduce key findings in a number of high-profile journals. In response to this crisis, many journals now encourage authors to archive their data when submitting papers for publication, and we expect a similar trend to develop with respect to computer code. This workshop will provide a unique opportunity for researchers to get advice and hands-on experience working with tools related to data and project management, data analysis, and archiving. Topics to be covered include best practices for writing computer code to manage and analyze data, version control, and archiving. We will highlight RStudio and the knitr/ezknitr libraries for producing reproducible reports, provide a tutorial on using GitHub for version control, and discuss how documenting and archiving data can increase your research impact. We will illustrate concepts using Program R, so a familiarity with R will be beneficial though not strictly necessary.
Organizers: John Fieberg, University of Minnesota, St. Paul, MN; Althea ArchMiller, University of Minnesota, St. Paul, MN
Supported by: TWS Biometrics Working Group