# TR&D2: Ontology and software tools describing reproducibility, credibility, and biosimulation

> **NIH NIH P41** · UNIVERSITY OF WASHINGTON · 2024 · $332,423

## Abstract

TECHNOLOGY RESEARCH & DEVELOPMENT 2: Project Summary
Biosimulation model repositories continue to grow, but unfortunately, the value of these models is impeded by
persistent challenges with reproducibility and credibility of models. Although we know how to make models
more reproducible, the community needs improved repositories and tools to capitalize and implement these
ideas. This technology development project will provide key standards and tools for semantic annotation.
Clear, unambiguous annotation of models is a key technology in making models more understandable,
findable, and reusable.
The goals of this project include: (1) Standards and ontologies that assist with model management. These
capture the semantics behind model evolution and versioning, allowing users to understand which version of
which models might be appropriate. (2) Standards and ontologies for model composition and decomposition,
even when models are developed with different mathematical paradigms. (3) Tool support for automatic model
annotation recommendations, to reduce the cost and burden of semantic annotation. (4) Standards and
repositories that capture model execution, including new development of the SED-ML standard.

## Key facts

- **NIH application ID:** 10780532
- **Project number:** 2P41EB023912-06
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** JOHN H. GENNARI
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $332,423
- **Award type:** 2
- **Project period:** 2018-06-13 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10780532

## Citation

> US National Institutes of Health, RePORTER application 10780532, TR&D2: Ontology and software tools describing reproducibility, credibility, and biosimulation (2P41EB023912-06). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10780532. Licensed CC0.

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