# Yadage and Packtivity - analysis preservation using parametrized workflows

## TL;DR

This paper introduces a framework for preserving and executing complex data analysis workflows at the LHC using declarative, parametrized descriptions and containerization, enhancing reproducibility and reusability.

## Contribution

It proposes a novel declarative approach with JSON schemas and an implementation called yadage for executing analysis workflows preserved via Linux containers.

## Key findings

- JSON schemas enable standardized workflow descriptions
- Yadage executes parametrized workflows reliably
- Framework improves reproducibility of LHC analyses

## Abstract

Preserving data analyses produced by the collaborations at LHC in a parametrized fashion is crucial in order to maintain reproducibility and re-usability. We argue for a declarative description in terms of individual processing steps - packtivities - linked through a dynamic directed acyclic graph (DAG) and present an initial set of JSON schemas for such a description and an implementation - yadage - capable of executing workflows of analysis preserved via Linux containers.