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fmriprep

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software/fmriprep

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
active
Maturity
deployed
Organization
nipreps
Country
unknown
Homepage
fmriprep.org
Documentation
unknown
Tags
bids · brain-imaging · fmri · fmri-preprocessing · image-processing · neuroimaging
Regulatory
unknown
built by · 6

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • ezbidsbids · brain-imaging · neuroimaging

    A web service for semi-automated conversion of raw imaging data to BIDS

  • nilearnbrain-imaging · fmri · neuroimaging

    Machine learning for NeuroImaging in Python

  • tedanabrain-imaging · fmri · neuroimaging

    TE-dependent analysis of multi-echo fMRI

  • bids-toolsbids · fmri · neuroimaging

    Tools for dealing with neuroimaging data in the BIDS structure

  • fmriflowsbids · fmri · neuroimaging

    fmriflows is a consortium of many (dependent) fMRI analysis pipelines, including anatomical and functional pre-processing, univariate 1st and 2nd-level analysis, as well as multivariate pattern…

  • SAMRIbids · fmri · neuroimaging

    Small Animal Magnetic Resonance Imaging via Python.

sources
  1. api.github.com/repos/nipreps/fmriprep
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-08-03, 742 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

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machine-readable

/v1/entries/30.json→ .entries["fmriprep"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.