# Enhancing neuroimaging reusability through semantic enrichment

> **NIH NIH P41** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2022 · $217,306

## Abstract

ReproNim: A Center for Reproducible Neuroimaging Computation - Overall
Summary: Over the last two decades a vast technological, computational and societal infrastructure has
emerged transforming how information is collected and knowledge is gathered in all facets of science.
Neuroimaging, as a discipline, is uniquely poised to exploit these new technologies and infrastructure to
improve the way science is performed. Given the intrinsically large and complex data sets collected in
neuroimaging research, coupled with the extensive array of shared data and tools amassed in the research
community, we need to lower the barriers for efficient: use of data; description of data and process; sharing
and subsequent reuse of the collective ‘big’ data. Aggregation of data and reuse of analytic methods have
become critical in addressing concerns about the replicability and power of many of today’s neuroimaging
studies. The magnitude of this reproducibility issue indicates that a paradigm shift in the way we generate and
report knowledge in this field is in order.
Our BTRC resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to continue
to drive a shift in the way neuroimaging research is performed. Through the coordinated development of
technology and training, (each of which supports a comprehensive set of tools and skills in data management,
analysis and utilization of frameworks in support of both basic research and clinical activities), our overarching
goal is to improve the reproducibility of neuroimaging science and extend the value of our national
investment in neuroimaging research, while making the process easier and more efficient for
investigators. Reproducibility is critical to scientific advancement because the current literature contains large
numbers of erroneous conclusions (due to limited power, publication bias and occasionally mistakes). Given a
neuroimaging study, it is exceedingly difficult to discern between false positive and true positive findings as
data is hard to aggregate, and exact methods are hard to replicate or reuse. In order to advance the field in
terms of analysis and publication in a way that embraces reproducibility, the overall Center will have the
following aims: A) Deliver a reproducible analysis system comprised of components that include data and
software discovery (TR&D 1), implementation of standardized workflow description and development of
machine-readable markup and storage of the results of these workflows (TR&D 2) and development of
execution options that facilitates operation in multiple computational environments and reduces barriers to
scale and reliability (TR&D 3); B) Working with a community of Collaborative and Service users, we deploy,
test and validate the reproducible analysis system with a wide variety of use cases ranging from software
developers to applied scientists that support the archiving and reuse of raw data and the archival and reuse of
derived results to promote ...

## Key facts

- **NIH application ID:** 10609329
- **Project number:** 3P41EB019936-07S1
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** David Nelson Kennedy
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $217,306
- **Award type:** 3
- **Project period:** 2016-04-15 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10609329

## Citation

> US National Institutes of Health, RePORTER application 10609329, Enhancing neuroimaging reusability through semantic enrichment (3P41EB019936-07S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10609329. Licensed CC0.

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