| 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
uv pip install pybids
uv pip install bids-validator-deno
uv pip install heudiconv
uv pip install dcm2bids
uv pip install nibabel
uv pip install pydicom
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
layout = BIDSLayout("/path/to/bids_dataset")
subjects = layout.get_subjects()
sessions = layout.get_sessions()
tasks = layout.get_tasks()
runs = layout.get_runs()
bold_files = layout.get(
suffix="bold",
extension=".nii.gz",
return_type="filename"
)
nback_sub01 = layout.get(
subject="01",
task="nback",
suffix="bold",
extension=".nii.gz",
return_type="filename"
)
metadata = layout.get_metadata("/path/to/sub-01/func/sub-01_task-rest_bold.nii.gz")
tr = metadata["RepetitionTime"]
entities = layout.get_entities()
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:
uv pip install bids-validator-deno
bids-validator /path/to/bids_dataset
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:
deno install -g -A npm:bids-validator
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:
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.gzfunc-bold_task-rest → sub-XX/func/sub-XX_task-rest_bold.nii.gzdwi_dir-AP → sub-XX/dwi/sub-XX_dir-AP_dwi.nii.gzfmap_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:
heudiconv --files dicom/219/itbs/*/*.dcm -o Nifti/ -f convertall -s 219 -c none
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 reproducibilitysourcedata/: HeuDiConv archives original DICOMs as .tgz files under sourcedata/ for reproducibilityis_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)
dcm2bids_helper -d /path/to/dicom
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 headersRepetitionTime and TaskName are required for BOLDSliceTiming is essential for slice-timing correction in fMRI preprocessingPhaseEncodingDirection 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 acquisitionduration - duration in seconds (use n/a for instantaneous events)
Recommended columns:
trial_type - categorical label for conditionresponse_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
layout = BIDSLayout("/path/to/dataset", database_path="/path/to/cache.db")
layout = BIDSLayout(
"/path/to/dataset",
derivatives=["/path/to/dataset/derivatives/fmriprep-24.1.0"]
)
preproc = layout.get(
subject="01",
task="rest",
desc="preproc",
suffix="bold",
space="MNI152NLin2009cAsym",
extension=".nii.gz",
return_type="filename"
)
confounds = layout.get(
subject="01",
task="rest",
desc="confounds",
suffix="timeseries",
extension=".tsv",
return_type="filename"
)
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
)
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:
docker run -v /path/to/bids:/data:ro -v /path/to/output:/out \
<bids-app-image> /data /out participant --participant_label 01
docker run nipreps/fmriprep /data /out participant \
--participant-label 01 --fs-license-file /license.txt
docker run nipreps/mriqc /data /out participant \
--participant-label 01
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-subjectgroup 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 `BIDSLay
