OpenTox Principles and Best Practices for Trusted Reproducible In Silico Methods Supporting Research and Regulatory Applications in Toxicological Science

Barry Hardy, Daniel Bachler, Joh Dokler, Thomas Exner, Connor Hardy, Weida Tong, Daniel Burgwinkel, Richard Bergström

Research output: Chapter in Book/Report/Conference proceedingChapter


Our aim in this work and initiative is to establish a practice and guidance for tracking and reporting modern in silico data analyses in a reproducible manner. The recommended reproducible principle supports the concept that data analyses, and more generally, scientific claims and regulatory evidence, are published with their raw data and software code so that others may verify the findings and build upon them. We discuss here how we are demonstrating implementations of trusted reproducible in silico evidence workflows and are enhancing their acceptance with an open knowledge community approach supported within OpenTox and OpenRiskNet. The general principle discussed in this article can be applied in regulatory settings.
Original languageEnglish
Title of host publicationAdvances in Computational Toxicology
Subtitle of host publicationMethodologies and Applications in Regulatory Science
ISBN (Electronic)978-3-030-16443-0
ISBN (Print)978-3-030-16442-3
Publication statusPublished - 2019

Publication series

NameChallenges and Advances in Computational Chemistry and Physics
PublisherSpringer Nature

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