Advanced computing for reproducibility of astronomy Big Data Science, with a showcase of AMIGA and the SKA Science prototype

TL;DR

This paper discusses advancements in computing and data reproducibility for astronomy Big Data, highlighting the AMIGA group's work and the importance of integrating reproducibility standards into the SKA Regional Centre Network to ensure verifiable research.

Contribution

It introduces new semantic data models and analysis services for federated infrastructures, demonstrating their application to enhance transparency and reproducibility in astronomy research.

Findings

Reproducibility techniques are feasible in astronomy Big Data.

Semantic data models improve data sharing and analysis.

Embedding standards in SRCNet is essential for sustainable research.

Abstract

The Square Kilometre Array Observatory (SKAO) faces unprecedented technological challenges due to the vast scale and complexity of its data. This paper provides an overview of research by the AMIGA group to address these computing and reproducibility challenges. We present advancements in semantic data models, analysis services integrated into federated infrastructures, and the application to astronomy studies of techniques that enhance research transparency. By showcasing these astronomy work, we demonstrate that achieving reproducible science in the Big Data era is feasible. However, we conclude that for the SKAO to succeed, the development of the SKA Regional Centre Network (SRCNet) must explicitly incorporate these reproducibility requirements into its fundamental architectural design. Embedding these standards is crucial to enable the global community to conduct verifiable and sustainable research within a federated environment.