Towards FAIR Astrophysical Simulations

TL;DR

This paper discusses the challenges and proposes practical steps to implement FAIR data principles in astrophysical simulations, emphasizing HPC-related obstacles and community-driven solutions.

Contribution

It provides a high-level overview of obstacles to FAIR astrophysics data and suggests actionable, low-threshold methods and tools for improving data and code sharing.

Findings

Identifies main obstacles to FAIR astrophysics data

Proposes low-threshold methods for FAIR data management

Suggests tools for data and code publication

Abstract

Reproducibility is a cornerstone of science. FAIR (findable, accessible, interoperable, and reusable) data is often a vital step towards testing the reproducibility of results. The implementation of FAIR principles in the astrophysical simulation community is still varied. We approach the discussion of this topic mainly from a high-performance computing (HPC) point of view. We identify the main obstacles to FAIR astrophysics simulations: First, the vast datasets created in simulations on HPC facilities complicate FAIR data management. Second, missing incentives to fully share codes, results, and diagnostic data. Third, a lack of workflows that include data publication and technical support. Therefore, particularly smaller research groups struggle due to the unavailability of dedicated personnel and time in their efforts towards FAIR and open simulations. We propose actionable steps towards achieving ``FAIRer'' data and open source publication standards in numerical astrophysics. Our suggestions include low-threshold methods to fulfil the basic FAIR requirements as well as basic tools for FAIR (meta-)data generation and data/code publication. This work is a high-level overview intended to initiate discussions within the community, offering initial solutions to these challenges.