The goal of this pipeline is to standardize the data cleaning, featuring extraction, analysis, and evaluation of mobile sensing projects. It leverages Cookiecutter_, Snakemake_, Sphinx_, Scypy_, R_, and Conda_ to create an end-to-end reproducible environment that can be published along with research papers.
At the moment, mobile data can be collected using different sensing frameworks (AWARE_, Beiwe_) and hardware (Fitbit_). The pipeline is agnostic to these data sources and can unify their analysis. The current implementation only handles data collected with AWARE_. However, it should be easy to extend it to other providers.
We recommend reading Snakemake_ docs, but the main idea behind the pipeline is that every link in the analysis chain is a rule with an input and an output. Input and output (generally) are files, and these files can be manipulated using any programming language (although Snakemake_ has wrappers for Python_, R_, and Julia_ that can make development slightly more comfortable). Snakemake_ also allows us to spread the execution of rules over multiple cores, which means that a single analysis pipeline can be executed in parallel for all participants in a study without any code changes.