bids · diff

v1.0 to v1.1

21 added, 541 removed. Audit A to A.

---

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"

+ version: "1.1"

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:

- |--------|-----|---------|--------------|

- | Subject | `sub-` | `sub-01` | All files |

- | Run | `run-` | `run-01` | Multiple identical acquisitions |

- **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](https://github.com/repronim/reproin), conversion is fully automatic — no heuristic file to write:

- ```bash

- # Turnkey conversion: HeuDiConv maps ReproIn protocol names to BIDS automatically

- 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.gz`

- - `func-bold_task-rest` → `sub-XX/func/sub-XX_task-rest_bold.nii.gz`

- - `dwi_dir-AP` → `sub-XX/dwi/sub-XX_dir-AP_dwi.nii.gz`

- - `fmap_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:

- ```bash

- # Step 1: Reconnaissance — discover DICOM series

- heudiconv --files dicom/219/itbs/*/*.dcm -o Nifti/ -f convertall -s 219 -c none

- # This creates .heudiconv/219/info/dicominfo.tsv — inspect it to understand

- # what was acquired and map series to BIDS names.

- # Step 2: Write a heuristic file (see references/conversion_tools.md)

- # Step 3: Convert

- 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 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

- dcm2bids_helper -d /path/to/dicom

- # Step 2: Create config file (dcm2bids_config.json)

- # Step 3: Convert

- 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)**:

- ```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:

- ```

- 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

- ```python

- from bids import BIDSLayout

- from bids.layout import BIDSLayoutIndexer

- # Cache the layout index for faster repeated access

- layout = BIDSLayout("/path/to/dataset", database_path="/path/to/cache.db")

- # Include derivatives

- layout = BIDSLayout(

- "/path/to/dataset",

- derivatives=["/path/to/dataset/derivatives/fmriprep-24.1.0"]

- )

- # Get derivative files

- preproc = layout.get(

- subject="01",

- task="rest",

- desc="preproc",

- suffix="bold",

- space="MNI152NLin2009cAsym",

- extension=".nii.gz",

- return_type="filename"

- )

- # Get confound regressors

- confounds = layout.get(

- subject="01",

- task="rest",

- desc="confounds",

- suffix="timeseries",

- extension=".tsv",

- return_type="filename"

- )

- # Build BIDS path from entities

- 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

- )

- # Get all files for a subject as a DataFrame

- 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:

- ```bash

- # General BIDS-App invocation pattern

- docker run -v /path/to/bids:/data:ro -v /path/to/output:/out \

- <bids-app-image> /data /out participant --participant_label 01

- # Common BIDS-Apps:

- # fMRIPrep - fMRI preprocessing

- docker run nipreps/fmriprep /data /out participant \

- --participant-label 01 --fs-license-file /license.txt

- # MRIQC - MRI quality control

- docker run nipreps/mriqc /data /out participant \

- --participant-label 01

- # QSIPrep - diffusion MRI preprocessing

- docker run pennbbl/qsiprep /data /out participant \

- --participant-label 01

- ```

+ Twelve workflow areas, each with worked code, are documented in

+ [references/core_workflows.md](references/core_workflows.md):

- **BIDS-App interface convention:**

- ```

- bids-app input_dataset output_dir {participant|group} [options]

- ```

+ 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.

- - `participant` level: runs per-subject

- - `group` level: runs across all subjects (aggregation/group stats)

+ 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](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:

```python

layout = BIDSLayout("/data", database_path="/data/.pybids_cache.db")

```

### 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](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):

|-----|-------|---------|--------|

| 004 | Susceptibility Weighted Imaging | raw | Seeking new leader |

| 024 | Computed Tomography scan | raw | Seeking contributors |

| 026 | Microelectrode Recordings | raw | Seeking new leader |

| 032 | Microelectrode electrophysiology | raw | Has PR (#2307), preview available — covers Neuropixels and other extracellular probes; relates to neuropixels-analysis skill |

| 033 | Advanced Diffusion Weighted Imaging | raw | Seeking contributors |

| 036 | Phenotypic Data Guidelines | raw | Community review |

| 037 | Non-Invasive Brain Stimulation | raw | In development |

| 039 | Dimensionality reduction-based networks | raw | In development |

| 040 | Functional Ultrasound | raw | In development |

| 044 | Stimuli | raw | Has PR (#2022), community review |

| 045 | Peripheral Physiological Recordings | raw | Has PR (#2267) |

| 047 | Audio/video recordings for behavioral experiments | raw | Has PR (#2231) |

**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

| Tool | Purpose |

|------|---------|

| **fMRIPrep** | fMRI preprocessing (produces BIDS derivatives) |

| **MRIQC** | MRI quality control (produces BIDS derivatives) |

| **QSIPrep** | Diffusion MRI preprocessing |

| **TemplateFlow** | Neuroimaging templates and atlases with BIDS-like naming |

| **Fitlins** | BIDS Stats Models implementation |

| **DataLad** | Version control for large datasets, integrates with BIDS |

| **OpenNeuro** | Free BIDS dataset repository |

| **DANDI** | Neurophysiology data archive (uses BIDS for some modalities) |

| **HeuDiConv** | DICOM-to-BIDS with heuristic Python files |

| **dcm2bids** | DICOM-to-BIDS with JSON config |

| **BIDScoin** | DICOM-to-BIDS with GUI and YAML config |

| **nwb2bids** | Convert NWB (Neurodata Without Borders) files to BIDS |

| **CuBIDS** | BIDS dataset curation and harmonization |

| **bids2table** | Efficient tabular indexing of BIDS datasets |

| **bids-examples** | Canonical collection of prototypical BIDS datasets for all modalities |

## 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