# A FAIR Data and Metadata Foundation for Reproducible Research

> **NIH NIH P41** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2021 · $305,076

## Abstract

TR&D Project 1: A FAIR Data and Metadata Foundation for Reproducible Research (DISCOVER)
SUMMARY
Our NCBIB resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to
continue to drive a shift in the way neuroimaging research is performed and reported to improve the
reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging
research. In this Technology Research and Development Project, TR&D 1 - A FAIR Data and Metadata
Foundation for Reproducible Research, we focus on the necessary tools and best practices to enable the
efficient annotation of scientific data and the effective search for and discovery of this data and its associated
workflows and software. During the current period, we have developed robust data annotation tools for raw and
derived data and associated tools for discovery. The data annotation tools are supported by an infrastructure
for managing the necessary terminologies required for annotation. Our tools and procedures support the “FAIR
Data Principles” which describe a set of key principles that will ensure data’s value to the research community
such that the data are Findable (with sufficient explicit metadata), Accessible (for humans and machines),
Interoperable (using standard definitions and Common Data Elements), and Reusable (meeting community
standards, and sufficiently documented). The Office of Data Science at NIH has endorsed these principles and
NIH has recently incorporated them in their most recent policy for data management and sharing
(NOT-OD-21-013) that requires the preservation and sharing of scientific data from all research, funded or
conducted in whole or in part by NIH. The tools and services provided by TR&D1 will therefore not only assist
researchers in performing reproducible neuroimaging, but also in the utilization of the increased amounts of
data being made available as part of this data sharing policy. Support for researchers will be accomplished via
two specific aims: 1) Production of FAIR data through metadata annotation and alignment allowing for the
sharing and publication of these data; and 2) Enabling data discovery and cohort generation for researchers to
be able to effectively re-use FAIR data for re-analysis or re-execution. These two complementary aims will be
supported by a third aim focused on support and training: 3) Extend and harden existing ReproNim software
for FAIR data publication and discovery in coordination with the community. This aim will ensure that the tools
we develop will be more accessible to those who have limited technical experience and will be complemented
by training modules and support for different user experience levels and use-cases. This suite of tools, part of
the larger ReproNim toolset, enables researchers to work within a FAIR data ecosystem. We will carry out this
work in collaboration with the other ReproNim technology research and development projects and our
Collaborative and Service pro...

## Key facts

- **NIH application ID:** 10334135
- **Project number:** 2P41EB019936-06A1
- **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:** 2021
- **Award amount:** $305,076
- **Award type:** 2
- **Project period:** 2016-04-15 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10334135

## Citation

> US National Institutes of Health, RePORTER application 10334135, A FAIR Data and Metadata Foundation for Reproducible Research (2P41EB019936-06A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10334135. Licensed CC0.

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