bids

name	bids
description	Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
license	https://creativecommons.org/licenses/by/4.0/
metadata	{"version":"1.0","skill-author":"Yaroslav Halchenko"}

Brain Imaging Data Structure (BIDS)

Overview

The Brain Imaging Data Structure (BIDS) is a community standard for organizing and describing neuroscience and biomedical research datasets. It defines a consistent file naming convention, directory hierarchy, and metadata schema so that datasets are immediately understandable by humans and software tools alike. BIDS is governed by the BIDS Specification (currently v1.11.x) and is maintained by the community via the BIDS-Standard GitHub organization.

While BIDS originated for MRI, it has grown well beyond neuroimaging. The specification now covers 11 modalities spanning imaging, electrophysiology, and behavioral data:

• Imaging: MRI (structural, functional, diffusion, fieldmaps, perfusion/ASL), PET, microscopy
• Electrophysiology: EEG, MEG, iEEG (intracranial EEG), EMG
• Other: NIRS (near-infrared spectroscopy), motion capture, behavioral data (without imaging), MR spectroscopy

Active BEPs are extending BIDS further — notably BEP032 (microelectrode electrophysiology) will add support for extracellular recordings including Neuropixels probes, bringing BIDS to a prevalent methodology in animal neuroscience research (see also the neuropixels-analysis skill).

Adoption is required or strongly encouraged by major data repositories (OpenNeuro, DANDI), leading journals (NeuroImage, Human Brain Mapping, Scientific Data), and funding agencies (NIH, ERC).

The Python ecosystem for BIDS centers on PyBIDS (pybids) for querying and indexing BIDS datasets, and the bids-validator (Deno-based, available as PyPI package bids-validator-deno or via Deno directly) for compliance checking. Conversion from DICOM is typically done with HeuDiConv, dcm2bids, or BIDScoin.

When to Use This Skill

Apply this skill when:

• Organizing raw neuroscience data (imaging, electrophysiology, behavioral) into BIDS-compliant directory structures
• Querying an existing BIDS dataset to find specific files by subject, session, task, run, or modality
• Validating a dataset against the BIDS specification before sharing or submission
• Converting DICOM data from scanners into BIDS format
• Writing or editing JSON sidecar metadata files
• Creating BIDS-compliant derivatives (preprocessed data, analysis outputs)
• Setting up a dataset_description.json for a new dataset
• Working with BIDS entities (subject, session, task, acquisition, run, etc.)
• Configuring .bidsignore to exclude files from validation
• Preparing data for upload to OpenNeuro, DANDI, or other BIDS-aware repositories

Installation

# Core BIDS querying library
uv pip install pybids

# BIDS validator (Deno-based, installed via PyPI wrapper)
uv pip install bids-validator-deno
# Alternative: install directly via Deno
# deno install -g -A npm:bids-validator

# DICOM-to-BIDS converters (install as needed)
uv pip install heudiconv       # HeuDiConv - heuristic-based DICOM conversion
uv pip install dcm2bids        # dcm2bids - config-file-based conversion
# BIDScoin: uv pip install bidscoin

# Useful companions
uv pip install nibabel          # NIfTI/other neuroimaging file I/O
uv pip install pydicom          # DICOM file reading (used by converters)

Core Workflows

1. BIDS Directory Structure

A minimal BIDS dataset follows this layout:

my_dataset/
  dataset_description.json      # Required: name, BIDSVersion, etc.
  participants.tsv              # Recommended: subject-level phenotypic data
  participants.json             # Recommended: column descriptions
  README                        # Recommended: dataset documentation
  CHANGES                       # Recommended: version history
  .bidsignore                   # Optional: patterns to exclude from validation
  sub-01/
    anat/
      sub-01_T1w.nii.gz
      sub-01_T1w.json           # Sidecar metadata
    func/
      sub-01_task-rest_bold.nii.gz
      sub-01_task-rest_bold.json
      sub-01_task-rest_events.tsv     # Event timing for task fMRI
      sub-01_task-rest_events.json
    dwi/
      sub-01_dwi.nii.gz
      sub-01_dwi.json
      sub-01_dwi.bvec
      sub-01_dwi.bval
    fmap/
      sub-01_phasediff.nii.gz
      sub-01_phasediff.json
      sub-01_magnitude1.nii.gz
    perf/
      sub-01_asl.nii.gz
      sub-01_asl.json
  sub-01/
    ses-pre/
      anat/
        sub-01_ses-pre_T1w.nii.gz
      func/
        sub-01_ses-pre_task-nback_bold.nii.gz
    ses-post/
      ...

Key points:

• Every NIfTI file should have a corresponding .json sidecar
• File names encode entities: sub-<label>[_ses-<label>][_task-<label>][_acq-<label>][_run-<index>]_<suffix>.<extension>
• Entity order in filenames is fixed by the specification
• Only dataset_description.json is strictly required at the root level

2. Creating dataset_description.json

import json

dataset_description = {
    "Name": "My Neuroimaging Study",
    "BIDSVersion": "1.10.0",
    "DatasetType": "raw",
    "License": "CC0",
    "Authors": ["First Author", "Second Author"],
    "Acknowledgements": "Funded by NIH R01-MH123456",
    "HowToAcknowledge": "Please cite: Author et al. (2025) Journal Name.",
    "Funding": ["NIH R01-MH123456", "NSF BCS-7654321"],
    "ReferencesAndLinks": ["https://doi.org/10.xxxx/xxxxx"],
    "DatasetDOI": "10.18112/openneuro.ds000001.v1.0.0",
    "GeneratedBy": [
        {
            "Name": "HeuDiConv",
            "Version": "1.3.1",
            "CodeURL": "https://github.com/nipy/heudiconv"
        }
    ]
}

with open("dataset_description.json", "w") as f:
    json.dump(dataset_description, f, indent=4)

For derivatives, set "DatasetType": "derivative" and add "GeneratedBy" listing the pipeline:

deriv_description = {
    "Name": "fMRIPrep - fMRI PREProcessing",
    "BIDSVersion": "1.10.0",
    "DatasetType": "derivative",
    "GeneratedBy": [
        {
            "Name": "fMRIPrep",
            "Version": "24.1.0",
            "CodeURL": "https://github.com/nipreps/fmriprep"
        }
    ]
}

3. Querying BIDS Datasets with PyBIDS

from bids import BIDSLayout

# Index a BIDS dataset (validates structure on load)
layout = BIDSLayout("/path/to/bids_dataset")

# Basic queries
subjects = layout.get_subjects()          # ['01', '02', '03', ...]
sessions = layout.get_sessions()          # ['pre', 'post'] or []
tasks = layout.get_tasks()                # ['rest', 'nback']
runs = layout.get_runs()                  # [1, 2] or []

# Find specific files
bold_files = layout.get(
    suffix="bold",
    extension=".nii.gz",
    return_type="filename"
)

# Filter by subject, task, session
nback_sub01 = layout.get(
    subject="01",
    task="nback",
    suffix="bold",
    extension=".nii.gz",
    return_type="filename"
)

# Get metadata from JSON sidecars (automatic inheritance)
metadata = layout.get_metadata("/path/to/sub-01/func/sub-01_task-rest_bold.nii.gz")
tr = metadata["RepetitionTime"]

# Get all entities for a file
entities = layout.get_entities()

# Build a path from entities using BIDSLayout
bids_file = layout.get(subject="01", suffix="T1w", extension=".nii.gz")[0]
print(bids_file.path)
print(bids_file.get_entities())

Key points:

• BIDSLayout indexes the entire dataset on initialization; for large datasets use database_path to cache the index
• Metadata inheritance: a JSON sidecar at a higher level (e.g., root or subject) is inherited by all files below unless overridden
• Use return_type="filename" for paths, return_type="object" (default) for BIDSFile objects

4. Validating BIDS Datasets

Using bids-validator via PyPI (recommended)

The bids-validator-deno PyPI package bundles the Deno-based validator as a standalone CLI:

# Install
uv pip install bids-validator-deno

# Validate a dataset
bids-validator /path/to/bids_dataset

# Ignore specific warnings/errors
bids-validator /path/to/bids_dataset --ignoreNiftiHeaders --ignoreSubjectConsistency

Using bids-validator via Deno directly

If Deno is already available, you can install or run the validator without PyPI:

# Install globally via Deno
deno install -g -A npm:bids-validator

# Or run without installing
deno run -A npm:bids-validator /path/to/bids_dataset

Legacy Node.js validator

The older Node.js-based validator (npm install -g bids-validator) is deprecated in favor of the Deno-based version. The Deno version is the reference implementation for BIDS Specification v1.9+.

Using .bidsignore

Create .bidsignore at the dataset root to exclude files from validation (gitignore syntax):

# Exclude sourcedata and extra files
sourcedata/
extra_data/
*.log
*_sbref.nii.gz
**/.DS_Store

5. BIDS Entities and File Naming

The authoritative, machine-readable source of truth for entities, their ordering, allowed suffixes, and all filename rules is the BIDS Schema — a structured YAML/JSON representation of the specification. A JSON export is shipped with this skill at references/bids_schema.json. The schema is defined in the bids-specification src/schema/ directory and published at https://bids-specification.readthedocs.io/en/stable/schema.json. BEP-specific schema previews are available at https://github.com/bids-standard/bids-schema/tree/main/BEPs.

Run scripts/update_schema.py to refresh the schema and BEPs list from upstream (no dependencies beyond stdlib).

The tables below are a convenient summary; when in doubt, consult the schema.

BIDS filenames are built from ordered key-value entity pairs:

Entity	Key	Example	Required for
Subject	sub-	sub-01	All files
Session	ses-	ses-pre	Multi-session studies
Task	task-	task-rest	func (bold, cbv, phase), eeg, meg
Acquisition	acq-	acq-highres	Distinguishing acquisition parameters
Contrast enhancing agent	ce-	ce-gadolinium	Contrast-enhanced images
Reconstruction	rec-	rec-magnitude	Reconstruction variants
Direction	dir-	dir-AP	Fieldmaps, DWI, phase-encoding
Run	run-	run-01	Multiple identical acquisitions
Echo	echo-	echo-1	Multi-echo sequences
Part	part-	part-mag	Magnitude/phase splits
Space	space-	space-MNI152NLin2009cAsym	Derivatives in template space
Description	desc-	desc-preproc	Derivatives only

Entity ordering in filenames is fixed by the spec (defined in rules.entities in bids_schema.json). See references/bids_specification.md for the complete numbered ordering table. A common subset: sub-<label>[_ses-<label>][_task-<label>][_acq-<label>][_ce-<label>][_rec-<label>][_dir-<label>][_run-<index>][_echo-<index>][_part-<label>][_space-<label>][_desc-<label>]_<suffix>.<extension>

Common suffixes by datatype:

Datatype	Suffixes
anat	T1w, T2w, FLAIR, T2star, T1map, T2map, defacemask
func	bold, cbv, sbref, events, physio, stim
dwi	dwi, sbref
fmap	phasediff, phase1, phase2, magnitude1, magnitude2, fieldmap, epi
perf	asl, m0scan, aslcontext
eeg	eeg, channels, electrodes, events
meg	meg, channels, coordsystem, events
ieeg	ieeg, channels, electrodes, coordsystem, events
pet	pet, blood

6. DICOM to BIDS Conversion

HeuDiConv

HeuDiConv is the most flexible DICOM-to-BIDS converter. It supports three usage modes — from fully automatic to fully custom — and handles duplicates, provenance tracking, and sourcedata archiving out of the box.

Mode 1: ReproIn (turnkey, recommended for new studies)

If scanner protocol names follow the ReproIn naming convention, conversion is fully automatic — no heuristic file to write:

# Turnkey conversion: HeuDiConv maps ReproIn protocol names to BIDS automatically
heudiconv --files dicom/001 -o /path/to/bids -f reproin --bids --minmeta

ReproIn protocol names encode BIDS entities directly:

• anat-T1w → sub-XX/anat/sub-XX_T1w.nii.gz
• func-bold_task-rest → sub-XX/func/sub-XX_task-rest_bold.nii.gz
• dwi_dir-AP → sub-XX/dwi/sub-XX_dir-AP_dwi.nii.gz
• fmap_dir-PA → sub-XX/fmap/sub-XX_dir-PA_epi.nii.gz

Session can be set once on the localizer (e.g., anat-scout_ses-pre) and ReproIn propagates it to all sequences in that Program. Subject ID is extracted from DICOM metadata. Duplicate runs are numbered automatically.

Mode 2: Custom heuristic mapping into ReproIn (for existing data)

If you already have data with non-ReproIn protocol names, you can write a thin heuristic that maps your names into ReproIn conventions, gaining all ReproIn benefits (automatic entity handling, duplicate management, etc.). See https://github.com/repronim/reproin/issues/18 for a HOWTO.

Mode 3: Custom heuristic (full flexibility)

For complex mappings, write a Python heuristic file:

# Step 1: Reconnaissance — discover DICOM series
heudiconv --files dicom/219/itbs/*/*.dcm -o Nifti/ -f convertall -s 219 -c none

# This creates .heudiconv/219/info/dicominfo.tsv — inspect it to understand
# what was acquired and map series to BIDS names.

# Step 2: Write a heuristic file (see references/conversion_tools.md)

# Step 3: Convert
heudiconv --files dicom/219/itbs/*/*.dcm -s 219 -ss itbs \
  -f Nifti/code/heuristic.py -c dcm2niix --bids --minmeta -o Nifti/

See references/conversion_tools.md for complete heuristic file examples.

Key points:

• HeuDiConv wraps dcm2niix for the actual DICOM-to-NIfTI conversion
• --minmeta: always use this flag to prevent excess DICOM metadata from overflowing JSON sidecars (can crash fMRIPrep/MRIQC)
• Duplicate handling: use {item:03d} in templates for auto-numbering when the same protocol is run multiple times; without it, later runs overwrite earlier ones
• .heudiconv/ directory: created alongside output, stores provenance (heuristic used, dicominfo.tsv, conversion records). Keep it with your data for reproducibility
• sourcedata/: HeuDiConv archives original DICOMs as .tgz files under sourcedata/ for reproducibility
• is_motion_corrected filter: use in heuristics to exclude scanner-generated MOCO series (e.g., if not s.is_motion_corrected)
• Both --files (explicit paths) and -d (template with {subject}, {session} placeholders) are supported for specifying DICOM input

dcm2bids (Configuration-file-based)

# Step 1: Generate helper output to inspect series
dcm2bids_helper -d /path/to/dicom

# Step 2: Create config file (dcm2bids_config.json)
# Step 3: Convert
dcm2bids -d /path/to/dicom -p 01 -c dcm2bids_config.json -o /path/to/bids_output

See references/conversion_tools.md for detailed configuration examples.

7. Metadata Sidecars

Every BIDS data file should have a JSON sidecar with acquisition parameters. Metadata fields follow the inheritance principle: a sidecar at a higher directory level applies to all matching files below.

Inheritance example:

my_dataset/
  task-rest_bold.json           # Applies to ALL rest BOLD files
  sub-01/
    func/
      sub-01_task-rest_bold.json  # Overrides/extends for sub-01 only

Critical metadata fields by modality:

For func (BOLD):

{
    "RepetitionTime": 2.0,
    "TaskName": "rest",
    "PhaseEncodingDirection": "j-",
    "TotalReadoutTime": 0.05,
    "SliceTiming": [0, 0.5, 1.0, 1.5],
    "EffectiveEchoSpacing": 0.00058,
    "EchoTime": 0.03
}

For anat:

{
    "MagneticFieldStrength": 3,
    "Manufacturer": "Siemens",
    "ManufacturersModelName": "Prisma",
    "RepetitionTime": 2.3,
    "EchoTime": 0.00293,
    "FlipAngle": 8
}

For DWI:

{
    "PhaseEncodingDirection": "j-",
    "TotalReadoutTime": 0.05,
    "EchoTime": 0.089,
    "RepetitionTime": 3.4,
    "MultipartID": "dwi_1"
}

Key points:

• dcm2niix auto-generates most sidecar fields from DICOM headers
• RepetitionTime and TaskName are required for BOLD
• SliceTiming is essential for slice-timing correction in fMRI preprocessing
• PhaseEncodingDirection and TotalReadoutTime (or EffectiveEchoSpacing) are needed for distortion correction
• See references/metadata_fields.md for comprehensive field reference

8. Events Files for Task fMRI

Task-based fMRI requires _events.tsv files:

onset	duration	trial_type	response_time
0.0	0.5	face	0.435
2.5	0.5	house	0.367
5.0	0.5	face	0.512
7.5	0.5	scrambled	0.298

Required columns:

• onset - onset time in seconds relative to the start of the acquisition
• duration - duration in seconds (use n/a for instantaneous events)

Recommended columns:

• trial_type - categorical label for condition
• response_time - RT in seconds
• Custom columns as needed (with descriptions in corresponding .json sidecar)

9. Participants File

participant_id	age	sex	group	handedness
sub-01	25	M	control	right
sub-02	30	F	patient	left
sub-03	28	M	control	right

The participants.json sidecar describes columns:

{
    "age": {
        "Description": "Age of the participant at time of scanning",
        "Units": "years"
    },
    "sex": {
        "Description": "Biological sex",
        "Levels": {
            "M": "male",
            "F": "female"
        }
    },
    "group": {
        "Description": "Experimental group",
        "Levels": {
            "control": "Healthy control",
            "patient": "Patient group"
        }
    },
    "handedness": {
        "Description": "Dominant hand",
        "Levels": {
            "right": "Right-handed",
            "left": "Left-handed",
            "ambidextrous": "Ambidextrous"
        }
    }
}

10. BIDS Derivatives

Processed outputs go under a derivatives/ directory:

my_dataset/
  derivatives/
    fmriprep-24.1.0/
      dataset_description.json      # DatasetType: "derivative"
      sub-01/
        anat/
          sub-01_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz
          sub-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz
        func/
          sub-01_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
          sub-01_task-rest_desc-confounds_timeseries.tsv
    mriqc-24.0.0/
      dataset_description.json
      sub-01/
        anat/
          sub-01_T1w.html
        func/
          sub-01_task-rest_bold.html
      group_T1w.tsv
      group_bold.tsv

Derivative conventions:

• space-<label> - template/reference space (e.g., MNI152NLin2009cAsym, T1w)
• desc-<label> - description of processing (e.g., preproc, brain, smoothed)
• res-<label> - resolution (e.g., 2 for 2mm isotropic)
• Each pipeline gets its own directory under derivatives/
• Must have its own dataset_description.json with GeneratedBy

11. PyBIDS: Advanced Usage

from bids import BIDSLayout
from bids.layout import BIDSLayoutIndexer

# Cache the layout index for faster repeated access
layout = BIDSLayout("/path/to/dataset", database_path="/path/to/cache.db")

# Include derivatives
layout = BIDSLayout(
    "/path/to/dataset",
    derivatives=["/path/to/dataset/derivatives/fmriprep-24.1.0"]
)

# Get derivative files
preproc = layout.get(
    subject="01",
    task="rest",
    desc="preproc",
    suffix="bold",
    space="MNI152NLin2009cAsym",
    extension=".nii.gz",
    return_type="filename"
)

# Get confound regressors
confounds = layout.get(
    subject="01",
    task="rest",
    desc="confounds",
    suffix="timeseries",
    extension=".tsv",
    return_type="filename"
)

# Build BIDS path from entities
from bids import BIDSLayout
layout = BIDSLayout("/path/to/dataset")
path = layout.build_path(
    {
        "subject": "01",
        "session": "pre",
        "task": "rest",
        "suffix": "bold",
        "extension": ".nii.gz",
        "datatype": "func"
    },
    validate=True
)

# Get all files for a subject as a DataFrame
import pandas as pd
files_df = layout.to_df()
sub01_df = files_df[files_df["subject"] == "01"]

12. BIDS-Apps

BIDS-Apps are containerized analysis pipelines that accept BIDS datasets as input:

# General BIDS-App invocation pattern
docker run -v /path/to/bids:/data:ro -v /path/to/output:/out \
    <bids-app-image> /data /out participant --participant_label 01

# Common BIDS-Apps:
# fMRIPrep - fMRI preprocessing
docker run nipreps/fmriprep /data /out participant \
    --participant-label 01 --fs-license-file /license.txt

# MRIQC - MRI quality control
docker run nipreps/mriqc /data /out participant \
    --participant-label 01

# QSIPrep - diffusion MRI preprocessing
docker run pennbbl/qsiprep /data /out participant \
    --participant-label 01

BIDS-App interface convention:

bids-app input_dataset output_dir {participant|group} [options]

• participant level: runs per-subject
• group level: runs across all subjects (aggregation/group stats)

Reference Materials

This skill includes detailed reference documentation:

• bids_schema.json: Machine-readable BIDS schema (from https://bids-specification.readthedocs.io/en/stable/schema.json). This is the authoritative source for entity definitions, ordering rules, filename templates, allowed suffixes per datatype, and metadata field requirements. BEP-specific schemas are at https://github.com/bids-standard/bids-schema/tree/main/BEPs.
• beps.yml: Current list of all BIDS Extension Proposals with titles, leads, status, and links (from bids-website)
• bids_specification.md: Human-readable summary of the entity table, datatype reference, directory structure rules, template spaces, and specification changelog
• metadata_fields.md: Required and recommended JSON sidecar fields for every BIDS modality (anat, func, dwi, fmap, eeg, meg, pet, etc.)
• conversion_tools.md: Detailed workflows for HeuDiConv, dcm2bids, and BIDScoin including heuristic/config examples and troubleshooting

Update schema and BEPs with: python scripts/update_schema.py

Common Issues and Solutions

1. Validator reports "Not a BIDS dataset"

Cause: Missing dataset_description.json at the root. Fix: Create the file with at minimum {"Name": "...", "BIDSVersion": "1.10.0"}.

2. Inconsistent subjects warning

Cause: Not all subjects have the same set of files (some missing sessions, runs, etc.). Fix: This is a warning, not an error. Use --ignoreSubjectConsistency if intentional. Document missing data in participants.tsv or a scans.tsv.

3. Missing SliceTiming

Cause: dcm2niix couldn't extract slice timing from DICOM headers. Fix: Determine slice order from the scan protocol and add manually to the JSON sidecar. Common patterns: ascending, descending, interleaved (odd-first or even-first).

4. Phase encoding direction confusion

Cause: Axis labels (i/j/k vs x/y/z vs LR/AP/SI) are confusing. Fix: In BIDS, use NIfTI image axes: i=first axis, j=second, k=third. - means negative direction. For standard axial acquisitions: j is typically anterior-posterior. Verify with the acquisition protocol.

5. PyBIDS is slow on large datasets

Cause: Full filesystem indexing on every BIDSLayout() call. Fix: Use database_path to cache the index to an SQLite file:

layout = BIDSLayout("/data", database_path="/data/.pybids_cache.db")

6. Derivatives not found by PyBIDS

Cause: Derivatives directory missing its own dataset_description.json. Fix: Every derivatives directory must have dataset_description.json with "DatasetType": "derivative".

7. Events file timing is off

Cause: onset times are relative to the wrong reference (e.g., trigger time vs first volume). Fix: Onsets must be in seconds relative to the first volume of that run's acquisition. Account for dummy scans if they were discarded.

8. TSV files fail validation

Cause: Encoding or delimiter issues (spaces instead of tabs, BOM characters, Windows line endings). Fix: Ensure tab-separated values with UTF-8 encoding and Unix line endings (\n). Use n/a (not NA, NaN, or empty) for missing values.

Best Practices

1. Validate early and often - Run the BIDS validator after every conversion or modification. Fix errors before they compound.

2. Use metadata inheritance - Place shared metadata (e.g., TaskName, scanner parameters) in top-level sidecar files rather than duplicating in every subject's directory.

3. Keep sourcedata - Store the original DICOM (or other raw) data under sourcedata/ so conversions are reproducible. Add sourcedata/ to .bidsignore.

4. Use consistent naming from the start - Define your BIDS naming scheme before data collection. Use the ReproIn naming convention for scan protocols to enable automatic conversion.

5. Document your dataset - Write a thorough README describing the study design, acquisition parameters, known issues, and any deviations from BIDS.

6. Use scans.tsv for run-level metadata - Record per-run acquisition times and quality notes:

   filename	acq_time	quality
   func/sub-01_task-rest_bold.nii.gz	2025-01-15T10:30:00	good
   

7. Version your dataset - Use CHANGES to document dataset modifications. Consider DataLad for full version control of large datasets.

8. Deface anatomical images - Remove facial features from T1w/T2w images before sharing (e.g., using pydeface, mri_deface, or afni_refacer). Store defaced versions as the primary data or use _defacemask files.

9. Use BIDS URIs for provenance - In derivatives, reference source files using BIDS URIs: bids::sub-01/anat/sub-01_T1w.nii.gz.

10. Prefer community tools - Use established BIDS-Apps (fMRIPrep, MRIQC, QSIPrep) rather than custom pipelines when possible. They handle BIDS I/O correctly and produce BIDS-compliant derivatives.

11. Study bids-examples - The bids-examples repository is the canonical collection of prototypical BIDS datasets covering different modalities and use cases (MRI, fMRI, DWI, EEG, MEG, iEEG, PET, ASL, genetics, derivatives, and more). Use it as a reference when structuring your own dataset, as test data for BIDS tools, or to understand how a specific modality should be organized. Each example passes the BIDS validator.

BIDS Extension Proposals (BEPs)

BEPs are community-driven proposals to extend BIDS to new modalities, derivatives, or metadata. The full list with status, leads, and links is in references/beps.yml (fetched from the bids-website). BEP-specific schema previews are rendered at https://github.com/bids-standard/bids-schema/tree/main/BEPs.

Current BEPs (as of schema update):

BEP	Title	Content	Status
004	Susceptibility Weighted Imaging	raw	Seeking new leader
011	Structural preprocessing derivatives	derivative	Has PR (#518)
012	Functional preprocessing derivatives	derivative	Has PR (#519), schema implemented
014	Affine transforms and nonlinear field warps	derivative	X5 format development
016	Diffusion weighted imaging derivatives	derivative	Has PR (#2211)
017	Generic BIDS connectivity data schema	derivative	In development
021	Common Electrophysiological Derivatives	derivative	In development
023	PET Preprocessing derivatives	derivative	In development
024	Computed Tomography scan	raw	Seeking contributors
026	Microelectrode Recordings	raw	Seeking new leader
028	Provenance	metadata	Has PR (#2099)
032	Microelectrode electrophysiology	raw	Has PR (#2307), preview available — covers Neuropixels and other extracellular probes; relates to neuropixels-analysis skill
033	Advanced Diffusion Weighted Imaging	raw	Seeking contributors
034	Computational modeling	derivative	Has PR (#967)
035	Mega-analyses with non-compliant derivatives	derivative	In development
036	Phenotypic Data Guidelines	raw	Community review
037	Non-Invasive Brain Stimulation	raw	In development
039	Dimensionality reduction-based networks	raw	In development
040	Functional Ultrasound	raw	In development
041	Statistical Model Derivatives	derivative	Collecting feedback
043	BIDS Term Mapping	metadata	Collecting feedback
044	Stimuli	raw	Has PR (#2022), community review
045	Peripheral Physiological Recordings	raw	Has PR (#2267)
046	Diffusion Tractography	derivative	In development
047	Audio/video recordings for behavioral experiments	raw	Has PR (#2231)

Related standards:

• BIDS-Stats Models: JSON specification for defining GLM-based neuroimaging analyses
• BIDS-Derivatives (BEP003): Standard for preprocessed/analysis outputs (partially merged into spec)

Related Tools Ecosystem

Tool	Purpose
fMRIPrep	fMRI preprocessing (produces BIDS derivatives)
MRIQC	MRI quality control (produces BIDS derivatives)
QSIPrep	Diffusion MRI preprocessing
TemplateFlow	Neuroimaging templates and atlases with BIDS-like naming
Fitlins	BIDS Stats Models implementation
DataLad	Version control for large datasets, integrates with BIDS
OpenNeuro	Free BIDS dataset repository
DANDI	Neurophysiology data archive (uses BIDS for some modalities)
HeuDiConv	DICOM-to-BIDS with heuristic Python files
dcm2bids	DICOM-to-BIDS with JSON config
BIDScoin	DICOM-to-BIDS with GUI and YAML config
nwb2bids	Convert NWB (Neurodata Without Borders) files to BIDS
CuBIDS	BIDS dataset curation and harmonization
bids2table	Efficient tabular indexing of BIDS datasets
bids-examples	Canonical collection of prototypical BIDS datasets for all modalities

Documentation

• BIDS Specification: https://bids-specification.readthedocs.io/
• BIDS Website: https://bids.neuroimaging.io/
• PyBIDS Documentation: https://bids-standard.github.io/pybids/
• BIDS Validator: https://github.com/bids-standard/bids-validator
• BIDS Starter Kit: https://bids-standard.github.io/bids-starter-kit/
• BIDS Examples: https://github.com/bids-standard/bids-examples — canonical reference datasets for every BIDS modality; use as templates and test data
• HeuDiConv Docs: https://heudiconv.readthedocs.io/
• Original BIDS paper: Gorgolewski et al. (2016) Scientific Data, doi:10.1038/sdata.2016.44