Enhancing reproducibility in neuroimaging with containerized archival.

Overview

1. Department of Computer Science, Vanderbilt University,Nashville, TN USA
2. Department of Electrical and Computer Engineering, Vanderbilt University,Nashville, TN USA
3. Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center,Nashville, TN USA
4. Vanderbilt University Institute of Imaging Science,Nashville, TN USA
5. Department of Biomedical Engineering, Vanderbilt University,Nashville, TN USA
6. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center,Nashville, TN USA

Abstract

Ensuring reproducibility in scientific research is crucial for validating findings, advancing knowledge, and fostering trust within the scientific community. Containerization of code is a widespread practice to help ensure reproducibility in neuroimaging research. However, there exists no universal method of sharing containers, nor is it feasible to broadly enforce rules due to the diversity of research practices. We posit that container sharing should include 1.) public availability, 2.) self-contained documentation, and 3.) procedures to ensure consistency of the container and its expected outputs. We demonstrate an approach for publicly releasing three separate containers using Zenodo. Our proposed method fulfills the design criteria for container sharing while incurring minimal overhead. As there are several tools available to distribute and maintain containers already, we discuss available alternatives.

Supplementary Information: The online version contains supplementary material available at 10.1007/s11682-026-01191-1.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Tracing map

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Data

Datasets cited

• openneuro:ds003097, at OpenNeuro; found in the references
• openneuro:ds004856, at OpenNeuro; found in “Data availability”

Data availability

Data used for the supplemental analysis come from the Dallas Lifespan Brain Study, freely available for download from OpenNeuro at https://openneuro.org/datasets/ds004856/versions/1.2.0.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 3 MeSH terms, 1 funder, 36 references.

Cite

This paper

Kim, M. E., Kanakaraj, P., Liu, Y., Schwartz, T., Li, Z., Newlin, N. R., Gao, C., Schilling, K. G., Bao, S., Rogers, B. P., Landman, B. A., & Ramadass, K. (2026). Enhancing reproducibility in neuroimaging with containerized archival. Brain imaging and behavior, 20(5), 123. https://doi.org/10.1007/s11682-026-01191-1

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