As the data collection ability of nearly every area of science has ballooned, so has the potential for problematic research practices that can lead to irreproducible results. I will discuss a set of approaches that we are developing to address this reproducibility crisis in the context of human neuroimaging research. These include an integrated platform for the analysis and open sharing of neuroimaging data, frameworks for the description of data and metadata, and the use of software containers and virtual machines to enhance computational reproducibility. I will show how these approaches have the potential to enable a new era of reproducibility in science. Bio:
|
|