# Data Analysis Core

> **NIH NIH U54** · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · 2022 · $334,409

## Abstract

The KAPP-Sen Data Analysis core will curate, process, share, and analyze the rich expression
and imaging datasets from the KAPP-Sen projects on kidney, fat, pancreas, and placenta tissues.
Our team (led by Drs. Ucar and Chuang) has broad and extensive expertise in management,
integration, and interpretation of complex high-resolution cellular datasets which will allow us to
centralize the output of the core teams.
The core will be in charge of processing of datasets, analyses and integration, including the
construction of senescence maps.
The Data Analysis Core will interact closely with the Biospecimen Core and the Biological
Analysis Core to systematically receive, assess, organize, and share data from the contributing
sites to ensure FAIR (findability, accessibility, interoperability, and reusability) principles
throughout this project. Pipelines and analysis methods will be developed with a focus on
reproducibility of workflows and robustness of results.
Our team has broad and extensive expertise in management, integration, and interpretation of
complex high-resolution cellular datasets which will allow us to centralize the output of the core
teams. This centralization will maximize the value of the data generated for the KAPP-Sen, for
the overall SenNet, and for the broader research community.

## Key facts

- **NIH application ID:** 10492646
- **Project number:** 5U54AG075941-02
- **Recipient organization:** UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
- **Principal Investigator:** GEORGE A KUCHEL
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $334,409
- **Award type:** 5
- **Project period:** 2021-09-30 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10492646

## Citation

> US National Institutes of Health, RePORTER application 10492646, Data Analysis Core (5U54AG075941-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10492646. Licensed CC0.

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