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arxiv: 2606.09144 · v1 · pith:JXBG25ZEnew · submitted 2026-06-08 · 💻 cs.DB

Containerizing BIDSme : A Reproducible Tool for BIDS Conversion

Bradley Spitz , Antoine Jacquemin , Nikita Beliy , Christophe Phillips This is my paper

Pith reviewed 2026-06-27 14:36 UTC · model grok-4.3

classification 💻 cs.DB
keywords BIDSDockercontainerizationneuroimagingdata conversionreproducibilityBIDSme
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The pith

Containerizing BIDSme with Docker makes the neuroimaging data conversion tool portable and reproducible.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how wrapping the semi-automated BIDSme tool in Docker containers using Docker Compose overcomes its prior limits on portability and accessibility. This packaging allows the tool to convert raw brain imaging data into the BIDS standard more consistently across different computing setups. A sympathetic reader would care because standardized BIDS datasets become easier to create, share, and analyze without installation barriers. The authors describe design choices, refinements, and validation steps that produce a flexible and lightweight containerized version. They also note improved integration with platforms such as Neurodesk.

Core claim

Packaging BIDSme into Docker containers with Docker Compose produces a portable, reproducible, and user-friendly application that preserves the original tool's semi-automated conversion of raw neuroimaging data into BIDS format while enabling easier integration into existing platforms like Neurodesk.

What carries the argument

Docker and Docker Compose containerization, which bundles BIDSme and its dependencies for consistent execution across environments.

If this is right

  • Users can execute BIDSme conversions without complex local software installations.
  • BIDS conversion results become reproducible across different operating systems and hardware.
  • The tool integrates directly into platforms such as Neurodesk for streamlined workflows.
  • Iterative refinements produce a lightweight container that remains flexible for varied use cases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same container approach could extend to other semi-automated neuroimaging conversion tools facing adoption barriers.
  • Standardized container access might reduce variability in how different research groups prepare BIDS datasets.
  • Embedding validation scripts inside the container could enable automated checks of output quality during conversion.

Load-bearing premise

The containerized version preserves the full functionality and exact behavior of the original BIDSme tool without compatibility problems.

What would settle it

Running identical raw neuroimaging datasets through both the original BIDSme and the containerized version, then comparing the resulting BIDS folder structures and metadata files for any differences.

Figures

Figures reproduced from arXiv: 2606.09144 by Antoine Jacquemin, Bradley Spitz, Christophe Phillips, Nikita Beliy.

Figure 1
Figure 1. Figure 1: General workflow of BIDSme : from raw neuroimaging data to a BIDS-compliant dataset. Despite its potential, BIDSme was not initially designed for broad distribution or easy deploy￾ment. It required local installation with multiple dependencies and lacked support for standard￾ized, reproducible environments. This limited its accessibility for end-users, particularly those without technical expertise in Pyth… view at source ↗
Figure 2
Figure 2. Figure 2: The multi-stage Dockerfile to optimize the image building process [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dockerfile’s entry point logic 5.4 Public distribution via GitHub The full containerization project is hosted on GitHub [16], providing open access to the Dockerfile, helper scripts, and example configurations. This ensures transparency, version control, and easy collaboration across users and contributors. The repository also includes documentation to guide new users through installation, setup, and execu… view at source ↗
read the original abstract

The "Brain Imaging Data Structure" (BIDS) has become a widely adopted standard for organizing and sharing neuroimaging datasets of various modalities. However, converting raw brain imaging data into BIDS framework remains a complex and time-consuming task. BIDSme is a semi-automated tool developed to streamline this conversion process, but until recently, it lacked the portability and accessibility needed for widespread adoption. This paper presents the containerization of BIDSme using Docker and Docker Compose, improving usability, reproducibility, and integration into existing platforms like Neurodesk. It also details the design choices, iterative refinements, and validation process that led to a flexible, lightweight, and user-friendly containerized application.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript describes the containerization of BIDSme, a semi-automated tool for converting raw neuroimaging data to the BIDS standard, using Docker and Docker Compose. It covers the design choices, iterative refinements, and validation process that produced a flexible, lightweight containerized application with improved usability, reproducibility, and integration into platforms such as Neurodesk.

Significance. If the containerized version preserves full original functionality while delivering the stated gains in portability and ease of use, the work would lower practical barriers to BIDS adoption in neuroimaging by enabling reproducible data-conversion workflows across diverse computing environments.

major comments (1)
  1. [validation process] The validation process (described in the abstract and presumably detailed in the methods/results sections) is presented only narratively; no quantitative metrics, test-dataset success rates, error analysis, or before/after comparisons are supplied to support the claims of improved usability and reproducibility.
minor comments (2)
  1. Provide explicit links to the public Dockerfiles, compose files, and any test data in the manuscript so that readers can reproduce the container exactly as described.
  2. Clarify the precise host-system prerequisites (e.g., Docker version, GPU support) required for the Neurodesk integration example.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive summary and the recommendation of minor revision. The single major comment is addressed point-by-point below.

read point-by-point responses

  1. Referee: [validation process] The validation process (described in the abstract and presumably detailed in the methods/results sections) is presented only narratively; no quantitative metrics, test-dataset success rates, error analysis, or before/after comparisons are supplied to support the claims of improved usability and reproducibility.

    Authors: We agree that the validation section would be strengthened by quantitative evidence. In the revised manuscript we will add: (i) success rates on a curated set of test neuroimaging datasets (raw DICOM and NIfTI files from multiple modalities), (ii) a brief error analysis of the few conversion failures encountered, and (iii) before/after comparisons of setup time and number of manual steps required. These additions will appear in the Methods and Results sections and will be supported by a supplementary table. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a purely descriptive engineering report on Docker containerization of the existing BIDSme tool. It contains no equations, fitted parameters, predictions, uniqueness theorems, or derivation chains of any kind. The central claim (successful containerization with improved usability and reproducibility) is advanced through narrative description of design choices and iterative validation, with no load-bearing step that reduces to a self-citation, ansatz, or input-by-construction. This is self-contained software packaging work with no opportunity for the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software packaging report containing no mathematical free parameters, axioms, or postulated scientific entities.

pith-pipeline@v0.9.1-grok · 5643 in / 1015 out tokens · 31329 ms · 2026-06-27T14:36:07.814909+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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