  Managing data and code in open scientific research is complicated by two key
problems: large datasets often cannot be stored alongside code in repository
platforms like GitHub, and iterative analysis can lead to unnoticed changes to
data, increasing the risk that analyses are based on older versions of data.
Here, I introduce SciDataFlow: a fast, concurrent command-line tool paired with
a simple Data Manifest specification. SciDataFlow streamlines tracking data
changes, uploading data to remote repositories, and pulling in all data
necessary to reproduce a computational analysis.
