# Core F: Training and Quality Assurance Core

> **NIH NIH U19** · UNIVERSITY OF PENNSYLVANIA · 2024 · $244,221

## Abstract

ACAD Training and Quality Assurance Core
ABSTRACT
The ACAD Training and Quality Assurance (TQA) core is tasked with building precise and uniform
procedures in the collection of data and its management. The TQA core emphasizes 1) Quality Assurance
(QA), which concentrates on developing and enforcing measures to prevent errors in data collection and
management and 2) Quality Control (QC), which focuses on building infrastructure to effectively identify
and correct errors. To accomplish its aims, the TQA core will establish QA procedures to educate and train
ACAD team members and develop multiple QC measures such as double scoring and identification of
outliers to identify and rectify errors. By reliably collecting and managing data in a standardized manner,
ACAD will produce high-quality harmonizable data that can generate replicable scientific findings. In
addition, the TQA core intends to investigate population-specific error susceptibilities of ACAD measures
using quantitative and qualitative analyses. Finally, by making ACAD training materials for administration
of cognitive measures available to qualified clinicians and research scientists, the ACAD TQA core will
increase participation of diverse populations in research and reduce healthcare disparities.

## Key facts

- **NIH application ID:** 10878684
- **Project number:** 5U19AG079774-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Boon Lead Tee
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $244,221
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10878684

## Citation

> US National Institutes of Health, RePORTER application 10878684, Core F: Training and Quality Assurance Core (5U19AG079774-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10878684. Licensed CC0.

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