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 (reviewed against v1.11.2, released 2026-09-29) 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) proposes support for extracellular recordings including Neuropixels probes, bringing BIDS to a prevalent methodology in animal neuroscience research (see also the neuropixels-analysis skill).

Repository submission requirements vary by modality and archive; check the target archive before preparing a deposit.

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 -ERWN -g -n bids-validator jsr:@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

Twelve workflow areas, each with worked code, are documented in references/core_workflows.md:

1. BIDS directory structure — the required layout and where each modality belongs.
2. **dataset_description.json** — the required fields and how to generate it.
3. Querying with PyBIDS — BIDSLayout, entity filters, sidecar metadata with automatic inheritance, and building paths from entities.
4. Validation — bids-validator via the PyPI wrapper (recommended), via Deno directly, the legacy Node validator, and using .bidsignore to exclude files.
5. Entities and file naming — the entity order and naming grammar.
6. DICOM to BIDS conversion — HeuDiConv (including the turnkey ReproIn path and the reconnaissance → heuristic → convert sequence) and dcm2bids (config-file based).
7. Metadata sidecars — required and recommended JSON fields per modality.
8. Events files — task fMRI event timing and column conventions.
9. Participants file — participants.tsv and its data dictionary.
10. Derivatives — the derivatives layout and its dataset_description.json.
11. Advanced PyBIDS — index caching, including derivatives, confound regressors, and DataFrame output.
12. BIDS-Apps — the standard invocation pattern, and fMRIPrep, MRIQC, and QSIPrep.

Validate with the BIDS validator as well as indexing with PyBIDS. Successful indexing is not a full compliance check; record the validator and BIDS schema versions, and inspect warnings and metadata inheritance before analysis.

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

From the skill directory, update schema and BEPs with python scripts/update_schema.py. The bundled snapshot is BIDS 1.11.2 / schema 2.0.0; BEP proposals are not adopted requirements. If ReadTheDocs blocks an automated fetch, use the documented versioned GitHub export in the updater help.

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": "1.11.2"}.

2. Inconsistent subjects warning

Cause: Not all subjects have the same set of files (some missing sessions, runs, etc.). Fix: Review severity and the exact issue code in the validator JSON report and document missing data in participants.tsv or scans.tsv. The current schema validator does not provide the legacy --ignoreSubjectConsistency flag; use a narrowly scoped --config only for reviewed exceptions.

3. Missing SliceTiming

Cause: dcm2niix couldn't extract slice timing from DICOM headers. Fix: Recover actual slice acquisition offsets from scanner metadata or a verified sequence protocol. Slice order alone does not determine timing, especially with multiband acquisition or dead time. Store offsets in seconds in slice-index order, accounting for SliceEncodingDirection; document missing timing instead of inventing it.

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. Anatomical direction depends on the NIfTI affine and converter orientation; do not infer j or its sign from an AP/PA series label alone. Verify against scanner metadata and the image orientation.

5. PyBIDS is slow on large datasets

Cause: Full filesystem indexing on every BIDSLayout() call. Fix: Use database_path to cache the index in a directory outside the dataset:

layout = BIDSLayout("/data", database_path="/cache/pybids")
# After dataset changes, rebuild with reset_database=True.

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 are seconds relative to the first stored data point in the corresponding recording. If dummy volumes were discarded before storage, reset time zero to the first retained volume; negative onsets are allowed.

8. TSV files fail validation

Cause: Encoding or delimiter issues (spaces instead of tabs, BOM characters). 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.

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

1. Keep sourcedata - Preserve source DICOMs and conversion provenance in controlled storage; sourcedata/ is excluded from raw BIDS validation, not deidentified. Review identifiers before any sharing.

1. 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.

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

1. 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

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

1. 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.

1. Use BIDS URIs for provenance - In derivatives, use bids:raw:sub-01/anat/sub-01_T1w.nii.gz for raw sources and define "DatasetLinks": {"raw": "../.."} when the derivative root is raw/derivatives/pipeline/. bids:: resolves within the current dataset, which in a derivative is the derivative dataset.

1. Prefer community tools - Use established BIDS-Apps (fMRIPrep, MRIQC, QSIPrep) rather than custom pipelines when possible. Check the chosen release's supported inputs and output conventions; software output still needs validation.

1. 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. Pin an example revision and validator/schema versions; the upstream test suite also tracks expected failures while implementations evolve.

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.

The bundled BEP listing was refreshed on 2026-09-30. Read each entry's proposal/PR and status before using draft entities; BEP032 remains a proposal, not part of stable 1.11.2. Use the repository directory listing to discover preview schema paths; do not assume a BEPs/BEP032/schema.json URL exists.

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

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