The goal of this pipeline is to standardize the data cleaning, feature extraction, analysis, and evaluation of mobile sensing projects. It leverages Conda_, Cookiecutter_, SciPy_, Snakemake_, Sphinx_, and R_ 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_ and Fitbit_. However, it can be easily extended 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 are files, which can be manipulated using any programming language (although Snakemake_ has wrappers for Julia_, Python_, and R_ that can make development slightly more comfortable). Snakemake_ also allows the pipeline rules to be executed in parallel on multiple cores without any code changes. This can drastically reduce the time needed to complete an analysis.
