bids

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

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 early and often: PyBIDS validates structure when it indexes a dataset, so an indexing failure usually means a naming or metadata problem rather than a code bug.

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:

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):

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