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BY 4.0 license Open Access Published by De Gruyter October 20, 2021

OMEX metadata specification (version 1.2)

  • John H. Gennari ORCID logo EMAIL logo , Matthias König ORCID logo , Goksel Misirli , Maxwell L. Neal ORCID logo , David P. Nickerson ORCID logo and Dagmar Waltemath ORCID logo


A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. This document details version 1.2 of the specification, which builds on version 1.0 published last year in this journal. In particular, this version includes a set of initial model-level annotations (whereas v 1.0 described exclusively annotations at a smaller scale). Additionally, this version uses best practices for namespaces, and introduces as a common root for all annotations. Distributing modeling projects within an OMEX archive is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model representations. This specification acts as a technical guideline for developing software tools that can support this standard, and thereby encourages broad advances in model reuse, discovery, and semantic analyses.

Corresponding author: John H. Gennari, University of Washington, Seattle, USA, E-mail:

Funding source: National Institute of Biomedical Imaging and Bioengineering (NIH, USA) & Federal Ministry of Education and Research (BMBF, Germany)

Award Identifier / Grant number: P41 GM109824

Award Identifier / Grant number: 031L0054

  1. Author contribution: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  2. Research funding: This work was funded in part by the NIH grant P41 GM109824 (JHG, DPN MLN) and by the Federal Ministry of Education and Research (BMBF, Germany), grant number 031L0054 (KM) and by the DFG (Germany) grant number 436883643 (KM).

  3. Conflict of interest statement: Authors state no conflict of interest.

Received: 2021-08-07
Revised: 2021-08-27
Accepted: 2021-08-27
Published Online: 2021-10-20

© 2021 John H. Gennari et al., published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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