# Efficient and reproducible execution from data collection to processing

> **NIH NIH P41** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2022 · $295,679

## Abstract

TR&D Project 3: Efficient and reproducible execution from data collection to processing (DO)
SUMMARY: The ReproNim project seeks to transform neuroimaging practice, to make research more efficient
and effective in such a way that also makes it reproducible as a result. As more data, metadata, and computing
resources become available to the neuroimaging community, tools and frameworks for managing data and
processing workflows that ensure consistent control over all of the digital objects of science become
increasingly important. Such tools should assist in obtaining valid results while establishing their provenance
and minimizing the need for manual curation and intervention; they should not get in the way of doing
research. In this Technology Research and Development Project, TR&D 3, we establish new approaches, as
well as adopt and contribute back to existing tools, to automate many stages of data collection and analysis,
making efficient use of local or remote computing resources that are available to the researchers. In particular,
we aim to 1) Automate “Doing (execution of) an experiment” through collection and representation of data,
metadata, and provenance across all stages of a neuroimaging acquisition, including all the data types that
could be important for quality assurance and proper accounting for possible confounding factors, such as
audio/video stimuli, physiological recordings, details of the experimental design. Automated integration of
imaging and non-imaging data not only makes research more efficient and labor saving, it also makes
collected and shared data more comprehensive, accurate, and reproducible. 2) Make computational resources
(GPUs, local High Performance Computing centers, and cloud computing resources) conveniently and
efficiently available to researchers to perform execution of needed data transformations (conversion, analysis,
etc.). While orchestrating execution we will record detailed provenance information, sufficient for re-execution
of any stage of the research process, and make it available to the researcher alongside with the produced
results. Efficient use of computational resources and collection of detailed provenance will facilitate
experimentation and application of bleeding edge analysis workflows, while reducing necessary technological
know-how. 3) Maintain, support, and extend existing ReproNim and related software and data resources that
we and our partners have made available openly to the community. This effort will be complemented by
training modules and support for different user experience levels and use cases. Ensuring such continuity in
availability and robust operation of tools, computing environments, and data resources is essential for any
effort aiming to support efficient and reproducible computation. We will carry out this work in collaboration with
the other ReproNim technology research and development projects, our collaborative and service projects, and
the neuroimaging community ...

## Key facts

- **NIH application ID:** 10482426
- **Project number:** 5P41EB019936-07
- **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:** $295,679
- **Award type:** 5
- **Project period:** 2016-04-15 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10482426

## Citation

> US National Institutes of Health, RePORTER application 10482426, Efficient and reproducible execution from data collection to processing (5P41EB019936-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10482426. Licensed CC0.

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