Research group

Nuclear Physics Methods Laboratory

DataForge

Alexander NozikActive

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 outputting the results. As the amount of data grows, so grows the need for automation of the process. Proper automation requires not only to improveme existing frameworks, but also to search for new ways to organize data processing so that it could be automated and parallelized. DataForge experimental framework solves some problems by making the analysis configuration declarative instead of imperative.

A more detailed description is available at the project site.

Repository with the current version: https://github.com/mipt-npm/dataforge-core.

Participants

Publications

Declarative analysis in “Troitsk nu-mass” experiment

April 2020

Read more

Shape-based event pileup separation in Troitsk nu-mass experiment

August 2019

Read more

DataForge: Modular platform for data storage and analysis

January 2018

Read more