bids — quality + safety report

---
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

```bash
# 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

```python
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:

```python
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

```python
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:

```bash
# 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:

```bash
# 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/`](https://github.com/bids-standard/bids-specification/tree/master/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` |

… (truncated)