Sim2Ls: FAIR simulation workflows and data

TL;DR

The paper presents Sim2Ls, a Python library and platform that enable creation, sharing, and verification of FAIR-compliant simulation workflows to improve reproducibility and accessibility in scientific research.

Contribution

Introduction of Sim2Ls and the nanoHUB platform to facilitate FAIR simulation workflows, making them discoverable, verifiable, and reusable in scientific research.

Findings

Sim2Ls enable end-to-end reproducible workflows.

The platform verifies inputs and outputs automatically.

Results are stored in a global simulation cache.

Abstract

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data.