What is it about?
Open science practices can help make computational models more impactful. However, there are few standards to follow at the intersection of open science and modelling. This paper presents ten practical rules that will help modellers make the most of learnings from open science.
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Why is it important?
We suggest practices for computational models to be of value to their very diverse stakeholders, including publishers, policy-makers, researchers from different disciplines, and fellow modellers. The ten rules also apply the FAIR Principles (findability, accessibility, interoperability and reusability) to a new domain: computational modelling. The ten rules are also designed to be used by many different practitioners, whether or not in machine learning, whether involved in larger- or smaller-scale modelling, and for both individuals and organisations.
Perspectives
This paper is the result of a huge collaboration across 22 institutions and 3 continents. It was really important to Chris Erdmann, Sandra Gesing and me —as co-chairs of the Open Modelling Foundation’s Certification Working Group— to lead by example and engage with many stakeholders to develop meaningful insights. I am deeply grateful for the opportunity to lead on this paper, to all the co-authors for their time and efforts, and to Chris and Sandra for their continued support.
Ismael Kherroubi Garcia
Kairoi
Read the Original
This page is a summary of: Ten simple rules for good model-sharing practices, PLoS Computational Biology, January 2025, PLOS,
DOI: 10.1371/journal.pcbi.1012702.
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