Sim2Ls: FAIR simulation workflows and data

Martin Hunt; Steven Clark; Daniel Mejia; Saaketh Desai; Alejandro Strachan

doi:10.1371/journal.pone.0264492

Sim2Ls: FAIR simulation workflows and data

PLoS One. 2022 Mar 10;17(3):e0264492. doi: 10.1371/journal.pone.0264492. eCollection 2022.

Authors

Martin Hunt¹, Steven Clark², Daniel Mejia³, Saaketh Desai⁴, Alejandro Strachan^{3

5}

Affiliations

¹ Halıcıoğlu Data Science Institute, University of California at San Diego, La Jolla, California, United States of America.
² San Diego Supercomputer Center, University of California at San Diego, La Jolla, California, United States of America.
³ Network for Computational Nanotechnology, Purdue University, West Lafayette, Indiana, United States of America.
⁴ Center for Integrated Nanotechnologies, Sandia National Laboratories, Albuquerque, New Mexico, United States of America.
⁵ School of Materials Engineering, Purdue University, West Lafayette, Indiana, United States of America.

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Computer Simulation
Data Management*
Ecosystem*
Reproducibility of Results
Software
Workflow

Grants and funding

This work was partially supported by the Network for Computational Nanotechnology, a project of the US National Science Foundation, through a grant awarded to MH, SC, DM, SD, and AS (EEC-1227110). This work was also partially supported by Sandia National Laboratories, a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration through a contract awarded to SD (DE-NA0003525). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.