The modern scientific data processing is not only a collection of powerful algorithms but also a whole infrastructure of facilities for data reading, processing, and results output. As the amount of data grows, so grows a need for automation of the process. The proper automation requires not only improvement of existing frameworks, but also a search of new ways to organize data processing in a way it could be automated and parallelized. DataForge experimental framework solves some problems by making the analysis configuration declarative instead of imperative.
More detailed description is available at project site.
The development is done at https://github.com/mipt-npm/dataforge-core.