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
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/bids
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
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
📊 Stats
Stars31,545
Forks3,146
LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
</> SKILL.md
bids.SKILL.md
---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
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:
| 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:
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](https://github.com/repronim/reproin), conversion is fully automatic — no heuristic file to write:
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:
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)
```bash
# Step 1: Generate helper output to inspect series
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)**:
```json
{
"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**:
```json
{
"MagneticFieldStrength": 3,
"Manufacturer": "Siemens",
"ManufacturersModelName": "Prisma",
"RepetitionTime": 2.3,
"EchoTime": 0.00293,
"FlipAngle": 8
}
```
For **DWI**:
```json
{
"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:
```json
{
"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:
- `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](https://github.com/bids-standard/bids-website/blob/main/data/beps/beps.yml))
- **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:
**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](https://github.com/bids-standard/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](https://github.com/bids-standard/bids-website/blob/main/data/beps/beps.yml)). 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 |
| 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 |
- **BIDS Examples**: https://github.com/bids-standard/bids-examples — canonical reference datasets for every BIDS modality; use as templates and test data
