# Archiving and Sharing Skeletal Phenotyping Data

> **NIH NIH R24** · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · 2024 · $522,334

## Abstract

Abstract
 Our objective is to build a data repository focused on collecting and sharing bone phenotyping data
performed on rodent animal models. Over the last ten years, our group has been actively involved in the bone
phenotyping of over 200 mutant mouse lines. Working through the organization, storage, and viewing of
datasets generated from this project made us recognize the extreme value that a skeletal repository would
have for the entire bone community. We now propose to dramatically expand this effort and build a
computational infrastructure, global in-scale, to collect and share bone phenotyping data generated by national
and international researchers across the field.
 While significant limitations exist in the skeletal phenotyping of human patients, rodents remain the
animal model of choice to study skeletal biology and model human skeletal diseases, where extensive
phenotyping is routinely performed. Because the experimental animals are genetically homogeneous, the data
captured is quantitative and, when coupled with experimental metadata, provides valuable insight into the
mechanistic regulation of bone tissue. The magnitude of experimental questions being tested in rodent animal
models to determine a skeletal phenotype is highly relevant to human disease, making for a rich data source
that that if properly archived and shared can be exploited to understand mechanisms of skeletal regulation for
rodents and humans. Unfortunately, existing rodent databases that collect genotype-phenotype data are
extremely broad in scope and are not built to ingest bone phenotyping data. Taken together, this project
addresses an unmet need in the skeletal field and will provide a highly valued resource that investigators can
computationally mine to develop superior therapeutic approaches to treat skeletal disease.
 Here we have assembled a talented multidisciplinary team of bone biologists and computer scientists
reinforced by the resources provided by UConn’s High Performance Computing Facility and Digital Experience
Group to build a sustainable skeletal phenotyping repository. Our resource development plan has three major
goals: 1) Develop a data ingestion method rich in the acquisition of metadata along with raw experimental data.
2) Develop a database system that incorporates the use of identifiers and ontologies to maximize data access
and interoperability with outside databases. 3) Develop a web portal that maximizes FAIR (Findability,
Accessibility, Interoperability, and Reuse) principles and becomes the go-to resource for investigators to
enhance their research. A vital component of this resource plan also involves the use of scientific emissaries
based in North America, Europe, and Asia that will engage with members of the bone community and be
responsive to their needs.

## Key facts

- **NIH application ID:** 10975740
- **Project number:** 1R24AR084725-01
- **Recipient organization:** UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
- **Principal Investigator:** PETER MAYE
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $522,334
- **Award type:** 1
- **Project period:** 2024-09-19 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10975740

## Citation

> US National Institutes of Health, RePORTER application 10975740, Archiving and Sharing Skeletal Phenotyping Data (1R24AR084725-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10975740. Licensed CC0.

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