# Data Science and Applied Technology Core (RC4)

> **NIH NIH P30** · UNIVERSITY OF FLORIDA · 2020 · $161,488

## Abstract

ABSTRACT
Data Science and Applied Technology (DSAT) Core (RC4), a recent addition to the University of Florida (UF)
Older Americans Independence Center (OAIC), provides an interactive data and technology ecosystem aimed
at preserving mobility and preventing disability. Big data initiatives, applied technologies, and new
methodological approaches for data science have grown rapidly in many various environments, and the world
is moving toward a connected system of computing and sensing components. The broadly used term “Internet
of Things” refers to an environment in which detailed data are collected on health, activity, location, and other
aspects of the participating entities. Flexible control of the different interconnected and frequently
communicating components can provide a rich set of applications that can adapt dynamically to their
environment. Additionally, mobile health (mHealth, smartphones and smartwatches) technologies are changing
the landscape for how patients and research participants communicate about their health in real time. These
possibilities have led the NIH to put forth large initiatives (Big Data to Knowledge (BD2K) and The Precision
Medicine Initiative Cohort Program) for meeting this new demand for knowledge. DSAT investigators provide
OAIC leadership to assure that researchers in Geriatrics in general, mobility and disability are prepared for the
rapid advances in these expanding technologies.
The RC4 provides many unique attributes, such as: developing software for interactive mobile technology (e.g.,
wearable sensors that are programmable in real time); validating new sensing technology; warehousing data;
repurposing data; and applying machine learning techniques to domain problems. DSAT provides a central
hub of expertise in computer science, biomedical engineering, biomedical informatics, data science, applied
technology, epidemiology, and content expertise in the assessment of mobility to:
 • Support OAIC cores, train Junior Scholars, and provide outreach to researchers and practitioners;
 • Advance interactive monitoring for assessing mobility phenotypes;
 • Warehouse and integrate multimodal data;
 • Conduct machine-learning and pattern-discovery analyses;
 • Harvest electronic health record (EHR) data to identify and recruit participants;
 • Repurpose high-resolution biomedical data and physiological signals to derive mobility phenotypes; and
 • Enhance externally supported projects.
There is a growing demand for data science and applied technology for meeting the challenge of preserving
mobility and preventing disability. The DSAT Core adds a highly innovative aspect to this challenge that will
lead it into the future of connected systems of computing, sensing and biomedical informatics.

## Key facts

- **NIH application ID:** 9899187
- **Project number:** 5P30AG028740-14
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Todd Manini
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $161,488
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9899187

## Citation

> US National Institutes of Health, RePORTER application 9899187, Data Science and Applied Technology Core (RC4) (5P30AG028740-14). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9899187. Licensed CC0.

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