Guidance for sharing computational models of neural stimulation: from project planning to publication

Abstract

Abstract Objective. Sharing computational models offers many benefits, including increased scientific rigor during project execution, readership of the associated paper, resource usage efficiency, replicability, and reusability. In recognition of the growing practice and requirement of sharing models, code, and data, herein, we provide guidance to facilitate sharing of computational models by providing an accessible resource for regular reference throughout a project’s stages. Approach. We synthesized literature on good practices in scientific computing and on code and data sharing with our experience in developing, sharing, and using models of neural stimulation, although the guidance will also apply well to most other types of computational models. Main results. We first describe the ‘6 R’ characteristics of shared models, leaning on prior scientific computing literature, which enforce accountability and enable advancement: re-runnability, repeatability, replicability, reproducibility, reusability, and readability. We then summarize action items associated with good practices in scientific computing, including selection of computational tools during project planning, code and documentation design during development, and user instructions for deployment. We provide a detailed checklist of the contents of shared models and associated materials, including the model itself, code for reproducing published figures, documentation, and supporting datasets. We describe code, model, and data repositories, including a list of characteristics to consider when selecting a platform for sharing. We describe intellectual property (IP) considerations to balance permissive, open-source licenses versus software patents and bespoke licenses that govern and incentivize commercialization. Finally, we exemplify these practices with our ASCENT pipeline for modeling peripheral nerve stimulation. Significance. We hope that this paper will serve as an important and actionable reference for scientists who develop models—from project planning through publication—as well as for model users, institutions, IP experts, journals, funding sources, and repository platform developers.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 4 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.