Reproducible Experiment Platform

TL;DR

The paper introduces the Reproducible Experiment Platform (REP), a Python-based infrastructure designed to facilitate collaborative, repeatable, and consistent data analysis in computational science, addressing challenges of data volume, technique variety, and team collaboration.

Contribution

It presents a novel software infrastructure, REP, that enables reproducible experiments and collaborative analysis for large-scale scientific research.

Findings

REP supports shared datasets and repeatable experiments.

Case studies demonstrate REP's effectiveness in physics analysis.

REP enhances collaboration and consistency in scientific workflows.

Abstract

Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries. At the same time we can see that complexity of those analyses increases fast due to a)~enormous volumes of datasets being analyzed, b)~variety of techniques and algorithms one have to check inside a single analysis, c)~distributed nature of research teams that requires special communication media for knowledge and information exchange between individual researchers. There is a lot of resemblance between techniques and problems arising in the areas of industrial information retrieval and particle physics. To address those problems we propose Reproducible Experiment Platform (REP), a software infrastructure to support collaborative ecosystem for computational science. It is a Python based solution for research teams that allows running computational experiments on shared datasets, obtaining repeatable results, and consistent comparisons of the obtained results. We present some key features of REP based on case studies which include trigger optimization and physics analysis studies at the LHCb experiment.