What it does. 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. Part of K-Dense-AI/scientific-agent-skills (AI Scientist skills) (K-Dense-AI/scientific-agent-skills).

Upstream	K-Dense-AI/scientific-agent-skills
Skill file	skills/bids/SKILL.md
License	MIT
Author	K-Dense Inc.
Fetched	2026-09-10

Install

• npx skills add K-Dense-AI/scientific-agent-skills --skill bids, or copy the skill folder into ~/.claude/skills/bids/.
• Raw file: curl -sL https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/bids/SKILL.md

SKILL.md (verbatim)

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: "1.1"
  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

# 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

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:

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 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):

BEP	Title	Content	Status
004	Susceptibility Weighted Imaging	raw	Seeking new leader
011	Structural preprocessing derivatives	derivative	Has PR (#518)
012	Functional preprocessing derivatives	derivative	Has PR (#519), schema implemented
014	Affine transforms and nonlinear field warps	derivative	X5 format development
016	Diffusion weighted imaging derivatives	derivative	Has PR (#2211)
017	Generic BIDS connectivity data schema	derivative	In development
021	Common Electrophysiological Derivatives	derivative	In development
023	PET Preprocessing derivatives	derivative	In development
024	Computed Tomography scan	raw	Seeking contributors
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
033	Advanced Diffusion Weighted Imaging	raw	Seeking contributors
034	Computational modeling	derivative	Has PR (#967)
035	Mega-analyses with non-compliant derivatives	derivative	In development
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
041	Statistical Model Derivatives	derivative	Collecting feedback
043	BIDS Term Mapping	metadata	Collecting feedback
044	Stimuli	raw	Has PR (#2022), community review
045	Peripheral Physiological Recordings	raw	Has PR (#2267)
046	Diffusion Tractography	derivative	In development
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

Other files in this skill

• references/beps.yml
• references/bids_schema.json
• references/bids_specification.md
• references/conversion_tools.md
• references/core_workflows.md
• references/metadata_fields.md
• scripts/update_schema.py

references/bids_specification.md (verbatim)

BIDS Specification Reference

Note: The canonical, machine-readable source of truth is bids_schema.json (in this directory), exported from the BIDS Schema. The tables below are a human-readable summary. When the two disagree, trust the schema.

Entity Table

Complete list of BIDS entities, their keys, and where they apply. Rows are listed in the required filename ordering — entities must appear in this order in BIDS filenames. This order is defined in the schema at rules.entities (see bids_schema.json).

#	Entity	Key	Format	Applies to
1	Subject	sub-	<label> (alphanumeric)	All files (required)
2	Template	tpl-	<label>	derivatives (template-based)
3	Session	ses-	<label>	All datatypes
4	Cohort	cohort-	<label>	derivatives (template cohorts)
5	Sample	sample-	<label>	microscopy
6	Task	task-	<label>	func, eeg, meg, ieeg, beh, pet, nirs, motion
7	Tracking system	tracksys-	<label>	motion
8	Acquisition	acq-	<label>	All datatypes
9	Nucleus	nuc-	<label>	MR spectroscopy
10	Volume	voi-	<label>	MR spectroscopy
11	Contrast enhancing agent	ce-	<label>	anat
12	Tracer	trc-	<label>	pet
13	Stain	stain-	<label>	microscopy
14	Reconstruction	rec-	<label>	anat, func, pet
15	Direction	dir-	<label>	fmap, dwi, perf, func
16	Run	run-	<index> (integer)	All datatypes
17	Modality	mod-	<label>	fieldmaps
18	Echo	echo-	<index>	func, fmap
19	Flip	flip-	<index>	anat (quantitative MRI)
20	Inversion	inv-	<index>	anat (quantitative MRI)
21	Magnetization transfer	mt-	on/off	anat (quantitative MRI)
22	Part	part-	mag/phase/real/imag	anat, func
23	Processing	proc-	<label>	eeg, meg, ieeg
24	Hemisphere	hemi-	L/R	derivatives (surface data)
25	Space	space-	<label>	derivatives
26	Split	split-	<index>	func, dwi, eeg, meg, ieeg
27	Recording	recording-	<label>	physio, stim, eeg, meg
28	Chunk	chunk-	<index>	large files split across chunks
29	Atlas	atlas-	<label>	derivatives (atlas-based)
30	Segmentation	seg-	<label>	derivatives
31	Scale	scale-	<label>	derivatives
32	Resolution	res-	<label>	derivatives
33	Density	den-	<label>	derivatives (surface meshes)
34	Label	label-	<label>	derivatives (segmentation labels)
35	Description	desc-	<label>	derivatives only

Datatypes (Top-Level Directories)

Datatype	Description	Common Suffixes
anat	Structural MRI	T1w, T2w, FLAIR, T2star, inplaneT1, inplaneT2, PDw, T1map, T2map, T1rho, UNIT1, MP2RAGE, MTR, MTS
func	Functional MRI	bold, cbv, sbref
dwi	Diffusion-weighted imaging	dwi, sbref
fmap	Fieldmaps	phasediff, phase1, phase2, magnitude1, magnitude2, fieldmap, epi
perf	Perfusion imaging (ASL)	asl, m0scan, aslcontext
eeg	Electroencephalography	eeg, channels, electrodes, events, coordsystem
meg	Magnetoencephalography	meg, channels, coordsystem, events, headshape
ieeg	Intracranial EEG	ieeg, channels, electrodes, events, coordsystem
pet	Positron Emission Tomography	pet, blood
micr	Microscopy	2PE, BF, CARS, CONF, DIC, DF, FLUO, MPE, NLO, OCT, PC, PLI, SRS, TL
beh	Behavioral data (no imaging)	events, beh, physio, stim
motion	Motion capture	motion, channels, events
nirs	Near-infrared spectroscopy	nirs, channels, optodes, coordsystem, events

File Extensions

Extension	Description
.nii.gz	Compressed NIfTI (standard for MRI/fMRI/DWI)
.nii	Uncompressed NIfTI
.json	JSON sidecar metadata
.tsv	Tab-separated values (events, participants, etc.)
.bvec	b-vectors (DWI gradient directions)
.bval	b-values (DWI gradient strengths)
.edf	European Data Format (EEG)
.bdf	BioSemi Data Format (EEG)
.vhdr/.vmrk/.eeg	BrainVision format (EEG)
.set	EEGLAB format (EEG)
.fif	Elekta/MEGIN format (MEG)
.ds	CTF dataset (MEG)
.sqd/.con	KIT/Yokogawa (MEG)

Required Files

Dataset-level (always required)

• dataset_description.json

Dataset-level (recommended)

• README or README.md
• CHANGES
• participants.tsv + participants.json
• LICENSE

Run-level (recommended)

• sub-<label>/[ses-<label>/]sub-<label>[_ses-<label>]_scans.tsv - per-run acquisition metadata

Modality-specific required files

• func/bold: corresponding _events.tsv for task data; TaskName in JSON sidecar
• dwi: .bvec and .bval files
• eeg/meg/ieeg: _channels.tsv, _events.tsv
• perf/asl: _aslcontext.tsv

Directory Structure Rules

1. Subject directories are named sub-<label> and sit at dataset root
2. Session directories ses-<label> are optional; if used, must be used for ALL subjects
3. Datatype directories (anat/, func/, etc.) sit inside subject (or session) directories
4. sourcedata/ stores raw unprocessed data (DICOM, etc.) - not validated
5. derivatives/ stores processed outputs - each pipeline in its own subdirectory
6. code/ stores analysis scripts
7. stimuli/ stores stimulus files used during acquisition
8. phenotype/ stores questionnaire/behavioral data not tied to specific imaging

Metadata Inheritance

JSON metadata cascades from higher to lower directories. If the same key appears at multiple levels, the most specific (closest to the data file) wins.

Resolution order (highest priority first):

1. File-level sidecar: sub-01/func/sub-01_task-rest_bold.json
2. Subject-level sidecar: sub-01/sub-01_task-rest_bold.json
3. Dataset-level sidecar: task-rest_bold.json

This avoids duplicating metadata that is constant across subjects (e.g., RepetitionTime, TaskName).

Standard Template Spaces

Common space- values used in derivatives:

Space Label	Description
MNI152NLin2009cAsym	MNI 2009c nonlinear asymmetric (fMRIPrep default)
MNI152NLin6Asym	MNI 6th-generation nonlinear asymmetric (FSL default)
MNI152Lin	MNI linear registration
MNIPediatricAsym	Pediatric MNI templates
T1w	Individual subject's T1w native space
fsnative	FreeSurfer individual surface space
fsaverage	FreeSurfer average surface (164k vertices)
fsaverage5	FreeSurfer average surface (10k vertices)
fsaverage6	FreeSurfer average surface (40k vertices)
fsLR	HCP fs_LR surface space
OASIS30ANTs	OASIS-30 ANTs template
UNCInfant	UNC infant templates

Full list managed by TemplateFlow: https://www.templateflow.org/

Specification Changelog (Selected)

Version	Key Changes
1.10.0	Motion capture modality; refined derivative entity rules
1.9.0	NIRS modality; Python-based validator reference implementation
1.8.0	Microscopy modality; chunk- entity for large files
1.7.0	PET modality fully specified
1.6.0	EEG/MEG/iEEG matured; _coordsystem.json
1.5.0	Genetic descriptors; ASL perfusion
1.4.0	dataset_description.json expanded; derivatives framework
1.0.0	Initial release: MRI only (anat, func, dwi, fmap)

Entity Label Rules

• Labels (<label>): alphanumeric only, no special characters, no leading zeros (except run-)
• Indices (<index>): non-negative integers, zero-padded to equal width within a dataset (e.g., run-01, run-02)
• Subject labels: typically numeric (01, 02) but can be alphanumeric (CON01, PAT01)
• Session labels: descriptive (pre, post, baseline, followup) or numeric
• Task labels: brief, descriptive, no spaces (rest, nback, faces, gonogo)

references/conversion_tools.md (verbatim)

BIDS Conversion Tools Reference

This reference covers detailed workflows for converting DICOM and other raw data formats to BIDS using the three main conversion tools.

HeuDiConv

HeuDiConv is the most flexible DICOM-to-BIDS converter. It supports three usage modes — from fully automatic turnkey conversion to fully custom heuristics — and handles duplicates, provenance tracking, and sourcedata archiving out of the box.

Repository: https://github.com/nipy/heudiconv Docs: https://heudiconv.readthedocs.io/ Tutorials: https://heudiconv.readthedocs.io/en/latest/tutorials.html

Installation

uv pip install heudiconv

# HeuDiConv wraps dcm2niix for the actual conversion
# dcm2niix is usually installed as a dependency, but can also be installed via:
# conda install -c conda-forge dcm2niix
# or: apt-get install dcm2niix

Mode 1: ReproIn (Turnkey Conversion — Recommended for New Studies)

If scanner protocol names follow the ReproIn naming convention, conversion is fully automatic with no heuristic file to write. ReproIn is a setup for automatic generation of sharable, version-controlled BIDS datasets directly from MR scanners.

# Turnkey conversion — just point at DICOMs, HeuDiConv does the rest
heudiconv --files dicom/001 -o data -f reproin --bids --minmeta

ReproIn Protocol Naming Rules

Protocol names encode BIDS entities directly. Format: <seqtype>[-<suffix>][_<entity>-<label>]...

Protocol name at scanner	BIDS output
anat-T1w or just anat	sub-XX/anat/sub-XX_T1w.nii.gz
func-bold_task-rest or func_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 or fmap-epi_dir-PA	sub-XX/fmap/sub-XX_dir-PA_epi.nii.gz
fmap_acq-4mm	sub-XX/fmap/sub-XX_acq-4mm_epi.nii.gz

Key features:

• Default suffixes: anat defaults to T1w, func to bold, fmap to epi — so they can be omitted
• Subject ID: extracted automatically from DICOM metadata (Patient ID)
• Session: set once on any sequence (e.g., anat-scout_ses-pre) and ReproIn propagates it to all sequences in that scanner Program/Patient
• Duplicate runs: automatically numbered (run-01, run-02, ...) when the same protocol is run multiple times
• Locator hierarchy: output is nested under Region/Exam from the scanner's Study Description (customizable with --locator)
• sourcedata: original DICOMs are archived as .tgz files under sourcedata/ for reproducibility
• Dashes in names: scanners may strip dashes from protocol names during DICOM export — ReproIn handles this gracefully

ReproIn Overview

See also:

• ReproIn Walkthrough for scanner setup
• ReproNim Webinar slides and recording on HeuDiConv + ReproIn

Mode 2: Custom Heuristic Mapping into ReproIn (For Existing Data)

If you already have collected data with non-ReproIn protocol names (or cannot control scanner naming), you can write a thin heuristic that maps your protocol names into ReproIn conventions. This gives you all ReproIn benefits (automatic entity handling, duplicate management, sourcedata archiving) while accommodating arbitrary scanner naming.

See https://github.com/repronim/reproin/issues/18 for a brief HOWTO on this approach.

The idea is to write a heuristic whose infotodict returns keys that follow ReproIn naming patterns, so the ReproIn machinery handles the rest.

Mode 3: Custom Heuristic (Full Flexibility)

For studies with complex mappings or non-standard requirements, write a full Python heuristic file. This is the most common workflow for retrospective conversion of existing datasets.

Step 1: Reconnaissance — Discover DICOM series

# -f convertall: built-in heuristic that lists all series without converting
# -c none: don't convert, just generate dicominfo.tsv
heudiconv \
    --files dicom/219/itbs/*/*.dcm \
    -s 219 \
    -f convertall \
    -c none \
    -o Nifti/

This creates .heudiconv/219/info/dicominfo.tsv containing one row per DICOM series with columns:

• series_id, sequence_name, protocol_name, series_description
• dim1-dim4 (image dimensions), TR, TE, image_type
• is_derived, is_motion_corrected — important for filtering

Review this TSV (open in a spreadsheet) to understand what was acquired and plan the mapping to BIDS names. Step 1 only needs to be done once per project.

Step 2: Write a heuristic file

"""HeuDiConv heuristic for a typical fMRI study.

Study design:
- T1w MPRAGE anatomical
- Resting-state BOLD
- Task BOLD (n-back working memory)
- DWI with two phase-encoding directions
- Fieldmap (phase-difference)
"""

def create_key(template, outtype=('nii.gz',), annotation_classes=None):
    if template is None or not template:
        raise ValueError('Template must be a valid format string')
    return template, outtype, annotation_classes


def infotodict(seqinfo):
    """Heuristic evaluator for determining which runs belong where.

    Parameters
    ----------
    seqinfo : list of namedtuples
        Each namedtuple has fields: .series_id, .sequence_name,
        .protocol_name, .series_description, .dim1, .dim2, .dim3, .dim4,
        .TR, .TE, .is_derived, .is_motion_corrected, .image_type, etc.

    Returns
    -------
    info : dict
        Keys are tuples from create_key(), values are lists of series_id
    """
    # Define BIDS output templates
    t1w = create_key(
        'sub-{subject}/{session}/anat/sub-{subject}_{session}_T1w'
    )
    rest_bold = create_key(
        'sub-{subject}/{session}/func/sub-{subject}_{session}_task-rest_bold'
    )
    # {item:02d} auto-numbers runs when the same protocol is run multiple times
    nback_bold = create_key(
        'sub-{subject}/{session}/func/sub-{subject}_{session}_task-nback_run-{item:02d}_bold'
    )
    dwi_AP = create_key(
        'sub-{subject}/{session}/dwi/sub-{subject}_{session}_dir-AP_dwi'
    )
    dwi_PA = create_key(
        'sub-{subject}/{session}/dwi/sub-{subject}_{session}_dir-PA_dwi'
    )
    fmap_phasediff = create_key(
        'sub-{subject}/{session}/fmap/sub-{subject}_{session}_phasediff'
    )
    fmap_mag1 = create_key(
        'sub-{subject}/{session}/fmap/sub-{subject}_{session}_magnitude1'
    )
    fmap_mag2 = create_key(
        'sub-{subject}/{session}/fmap/sub-{subject}_{session}_magnitude2'
    )

    info = {
        t1w: [], rest_bold: [], nback_bold: [],
        dwi_AP: [], dwi_PA: [],
        fmap_phasediff: [], fmap_mag1: [], fmap_mag2: [],
    }

    for s in seqinfo:
        protocol = s.protocol_name.lower()
        series_desc = s.series_description.lower() if s.series_description else ''

        # Anatomical — filter by dim3 to exclude localizers
        if ('mprage' in protocol or 't1w' in protocol) and s.dim3 > 100:
            info[t1w].append(s.series_id)

        # Functional — filter by dim4 and exclude MOCO series
        elif 'rest' in protocol and s.dim4 > 10 and not s.is_motion_corrected:
            info[rest_bold].append(s.series_id)
        elif 'nback' in protocol and s.dim4 > 10 and not s.is_motion_corrected:
            info[nback_bold].append(s.series_id)

        # Diffusion
        elif ('dti' in protocol or 'dwi' in protocol) and s.dim4 > 1:
            if 'ap' in protocol or 'ap' in series_desc:
                info[dwi_AP].append(s.series_id)
            elif 'pa' in protocol or 'pa' in series_desc:
                info[dwi_PA].append(s.series_id)

        # Fieldmaps
        elif 'field' in protocol or 'fmap' in protocol:
            if 'ph' in s.image_type_text.lower():
                info[fmap_phasediff].append(s.series_id)
            elif s.series_description and 'e1' in s.series_description.lower():
                info[fmap_mag1].append(s.series_id)
            elif s.series_description and 'e2' in s.series_description.lower():
                info[fmap_mag2].append(s.series_id)

    return info

Step 3: Convert

# Convert with custom heuristic
heudiconv \
    --files dicom/219/itbs/*/*.dcm \
    -s 219 \
    -ss itbs \
    -f Nifti/code/heuristic.py \
    -c dcm2niix \
    --bids \
    --minmeta \
    -o Nifti/

# Or using -d template for batch conversion of multiple subjects
heudiconv \
    -d /path/to/dicoms/{subject}/*/*/*.dcm \
    -s 01 02 03 04 05 \
    -f my_heuristic.py \
    -c dcm2niix \
    --bids \
    --minmeta \
    -o /path/to/bids_output

# Key flags:
# --files : point to specific DICOM files/directories
# -d : DICOM path template ({subject}, {session} are replaced)
# -s : subject label(s)
# -ss : session label
# -f : heuristic file path, or built-in name (reproin, convertall)
# -c : converter (dcm2niix, none)
# --bids / -b : output BIDS structure (creates JSON sidecars, etc.)
# --minmeta : prevent excess DICOM metadata from overflowing JSON sidecars
# -o : output directory
# --overwrite : re-run conversion overwriting existing files

The .heudiconv Directory

Every conversion creates/updates a .heudiconv/ hidden directory alongside the output:

• .heudiconv/<subject>/info/dicominfo.tsv — DICOM series metadata
• .heudiconv/<subject>/info/<heuristic>.py — copy of the heuristic used
• Conversion records for each subject/session

Important: If you re-run conversion for a subject/session that was already processed, HeuDiConv silently reuses cached conversion info from .heudiconv/. If troubleshooting, delete the subject's entry from .heudiconv/ (or the whole directory) and re-run.

Keep .heudiconv/ with your data — together with code/ it provides valuable provenance information.

HeuDiConv Tips

1. Always use --minmeta to prevent excess DICOM metadata from overflowing JSON sidecars — fMRIPrep and MRIQC may crash on bloated JSON files
2. Use {item:02d} in templates for auto-numbering runs: if multiple series match, they get run-01, run-02, etc. Without this, later runs silently overwrite earlier ones
3. Filter by dim3/dim4 to exclude localizers (small dim3) and single-volume scouts (dim4 == 1)
4. Check s.is_motion_corrected to exclude scanner-generated MOCO series (e.g., if not s.is_motion_corrected)
5. Check s.is_derived to skip other derived/processed series
6. Store heuristic with dataset under code/ for reproducibility
7. Use --files when DICOM organization doesn't follow a clean {subject} template pattern
8. For new studies: prefer ReproIn protocol naming from the start — it eliminates the need for custom heuristics entirely
9. For existing data with arbitrary names: consider the "map into reproin" approach rather than writing a fully custom heuristic — you get duplicate handling, session propagation, and other ReproIn features for free

dcm2bids (Configuration-File-Based)

dcm2bids uses JSON configuration files instead of Python heuristics. Simpler for straightforward datasets.

Repository: https://github.com/UNFmontreal/Dcm2Bids Docs: https://unfmontreal.github.io/Dcm2Bids/

Installation

uv pip install dcm2bids
# Also installs dcm2niix

Workflow

Step 1: Scaffold a BIDS directory

dcm2bids_scaffold -o /path/to/bids_output

Creates the basic BIDS structure with dataset_description.json, README, .bidsignore, etc.

Step 2: Run helper to inspect DICOM metadata

dcm2bids_helper -d /path/to/dicom_dir -o /path/to/bids_output

Creates tmp_dcm2bids/helper/ with converted NIfTI files and JSON sidecars. Review the JSON files to find distinguishing metadata fields.

Step 3: Write configuration file

{
    "descriptions": [
        {
            "id": "id_t1w",
            "datatype": "anat",
            "suffix": "T1w",
            "criteria": {
                "SeriesDescription": "*MPRAGE*",
                "ImageType": ["ORIGINAL", "PRIMARY", "M", "ND", "NORM"]
            }
        },
        {
            "id": "id_bold_rest",
            "datatype": "func",
            "suffix": "bold",
            "custom_entities": "task-rest",
            "criteria": {
                "SeriesDescription": "*REST*BOLD*",
                "ImageType": ["ORIGINAL", "PRIMARY", "M", "ND", "MOSAIC"]
            },
            "sidecar_changes": {
                "TaskName": "rest"
            }
        },
        {
            "id": "id_bold_nback",
            "datatype": "func",
            "suffix": "bold",
            "custom_entities": "task-nback",
            "criteria": {
                "SeriesDescription": "*NBACK*",
                "EchoTime": 0.03
            },
            "sidecar_changes": {
                "TaskName": "nback"
            }
        },
        {
            "id": "id_dwi",
            "datatype": "dwi",
            "suffix": "dwi",
            "custom_entities": "dir-AP",
            "criteria": {
                "SeriesDescription": "*DTI*AP*"
            }
        },
        {
            "id": "id_fmap_phasediff",
            "datatype": "fmap",
            "suffix": "phasediff",
            "criteria": {
                "SeriesDescription": "*field*map*",
                "EchoTime1": 0.00492,
                "EchoTime2": 0.00738
            },
            "sidecar_changes": {
                "IntendedFor": [
                    "bids::sub-{subject}/func/sub-{subject}_task-rest_bold.nii.gz",
                    "bids::sub-{subject}/func/sub-{subject}_task-nback_bold.nii.gz"
                ]
            }
        }
    ]
}

Configuration file fields:

• datatype: BIDS datatype (anat, func, dwi, fmap, etc.)
• suffix: BIDS suffix (T1w, bold, dwi, etc.)
• custom_entities: additional BIDS entities (task-rest, dir-AP, acq-highres, etc.)
• criteria: dictionary of DICOM/JSON metadata fields to match (supports wildcards *)
• sidecar_changes: fields to add/modify in the output JSON sidecar
• id: arbitrary identifier for the description (for logging)

Step 4: Convert

# Single subject
dcm2bids -d /path/to/dicom_dir -p 01 -c dcm2bids_config.json -o /path/to/bids_output

# With session
dcm2bids -d /path/to/dicom_dir -p 01 -s pre -c dcm2bids_config.json -o /path/to/bids_output

# Flags:
# -d : DICOM source directory
# -p : participant label
# -s : session label (optional)
# -c : configuration file
# -o : output BIDS directory
# --auto_extract_entities : auto-detect run numbers from DICOM
# --force_dcm2bids : overwrite existing conversions

dcm2bids Tips

1. Use dcm2bids_helper first to see exactly what metadata dcm2niix extracts
2. Criteria matching uses wildcards (*) and is case-sensitive
3. Multiple criteria are ANDed together; use the most specific combination
4. sidecar_changes can inject any BIDS metadata (useful for TaskName, IntendedFor)
5. Store config file under code/dcm2bids_config.json for reproducibility

BIDScoin (GUI + YAML Configuration)

BIDScoin provides a graphical interface and YAML-based configuration. Good for users who prefer visual mapping.

Repository: https://github.com/Donders-Institute/bidscoin Docs: https://bidscoin.readthedocs.io/

Installation

uv pip install bidscoin
# Optional: install with all plugin dependencies
uv pip install "bidscoin[all]"

Workflow

# Step 1: Create a bidsmap template by scanning DICOMs
bidsmapper /path/to/raw /path/to/bids

# Step 2: Edit the bidsmap (launches GUI)
bidseditor /path/to/bids

# Step 3: Convert using the finalized bidsmap
bidscoiner /path/to/raw /path/to/bids

BIDScoin Tips

1. GUI-based editing is BIDScoin's strength - the bidseditor shows DICOM metadata alongside BIDS mapping
2. YAML bidsmap can be edited manually if preferred
3. Plugin architecture supports custom conversion backends beyond dcm2niix
4. Good for multi-site studies where protocol names vary - visual mapping makes differences obvious

Comparison

Feature	HeuDiConv	dcm2bids	BIDScoin
Configuration	Python heuristic	JSON config	YAML + GUI
Flexibility	Highest (full Python)	Medium (criteria matching)	Medium (plugin system)
Learning curve	Steeper (Python)	Moderate	Gentlest (GUI)
Batch processing	Excellent	Good	Good
ReproIn support	Built-in	No	No
DataLad integration	Built-in	No	No
Best for	Complex studies, automation	Simple-to-moderate studies	Visual learners, multi-site
Active development	Yes	Yes	Yes

Post-Conversion Checklist

After converting DICOM to BIDS with any tool:

1. Run the BIDS validator: bids-validator /path/to/bids_output
2. Check JSON sidecars for critical fields (RepetitionTime, TaskName, SliceTiming, PhaseEncodingDirection)
3. Verify NIfTI headers match expectations (dimensions, voxel sizes, orientation)
4. Add missing metadata that dcm2niix couldn't extract from DICOM
5. Create participants.tsv with demographic data
6. Write events files for task fMRI
7. Write README describing the dataset
8. Deface anatomical images if sharing data
9. Run bids-validator again after any manual modifications

Common DICOM-to-BIDS Pitfalls

Multiband/SMS sequences

• dcm2niix may split slices incorrectly for multiband data
• Check dim4 (number of volumes) matches expectations
• Verify SliceTiming is correct for the multiband factor

Dual-echo fieldmaps

• Siemens stores both echoes in one series; dcm2niix splits them
• GE/Philips may store them as separate series
• Verify EchoTime1 < EchoTime2 in the phasediff sidecar

Phase encoding direction

• DICOM InPlanePhaseEncodingDirection → BIDS PhaseEncodingDirection
• Mapping depends on acquisition orientation and NIfTI axis conventions
• Always verify by checking the actual distortion pattern in the images

Multi-run numbering

• Ensure runs are numbered sequentially (run-01, run-02)
• HeuDiConv: use {item:02d} placeholder
• dcm2bids: use --auto_extract_entities or manually specify runs

Derived/processed series

• Scanners may export inline-processed data (e.g., motion-corrected, distortion-corrected)
• These should NOT be converted to BIDS raw data
• Filter by ImageType containing DERIVED or is_derived flag in HeuDiConv

references/metadata_fields.md (verbatim)

BIDS Metadata Fields Reference

This reference lists the required and recommended JSON sidecar fields for each BIDS modality.

Legend:

• R = Required
• REC = Recommended
• OPT = Optional

Common MRI Fields (All MRI Modalities)

Field	Status	Type	Description
MagneticFieldStrength	REC	number	Field strength in Tesla
Manufacturer	REC	string	Scanner manufacturer
ManufacturersModelName	REC	string	Scanner model
DeviceSerialNumber	REC	string	Scanner serial number
StationName	REC	string	Scanner station name
SoftwareVersions	REC	string	Scanner software version
InstitutionName	REC	string	Name of institution
InstitutionAddress	REC	string	Address of institution
InstitutionalDepartmentName	REC	string	Department name

Anatomical MRI (anat/)

T1w, T2w, FLAIR, T2star, PDw

Field	Status	Type	Description
RepetitionTime	REC	number	TR in seconds
EchoTime	REC	number	TE in seconds
InversionTime	REC	number	TI in seconds (if applicable)
FlipAngle	REC	number	Flip angle in degrees
SequenceName	REC	string	Pulse sequence name
SequenceVariant	REC	string	Variant of the sequence
ScanningSequence	REC	string	General description
PulseSequenceType	REC	string	Type of pulse sequence
NonlinearGradientCorrection	REC	boolean	Whether applied
ParallelReductionFactorInPlane	REC	number	iPAT/GRAPPA factor
ContrastBolusIngredient	REC	string	Active contrast ingredient

Quantitative MRI (T1map, T2map, etc.)

Field	Status	Type	Description
RepetitionTimeExcitation	R	number	Excitation TR in seconds
RepetitionTimePrepration	R	number	Preparation TR in seconds
FlipAngle	R	number/array	Flip angle(s) in degrees
MTState	R	boolean	Magnetization transfer on/off
SpoilingState	REC	boolean	Whether RF spoiling applied
SpoilingType	REC	string	RF, GRADIENT, or COMBINED
SpoilingRFPhaseIncrement	REC	number	Phase increment in degrees

Functional MRI (func/)

BOLD

Field	Status	Type	Description
RepetitionTime	R	number	TR in seconds (volume acquisition time)
TaskName	R	string	Name of the task (must match task-<label>)
SliceTiming	REC	array	Time each slice was acquired, in seconds
EchoTime	REC	number	TE in seconds
FlipAngle	REC	number	Flip angle in degrees
PhaseEncodingDirection	REC	string	i, i-, j, j-, k, k-
EffectiveEchoSpacing	REC	number	Effective echo spacing in seconds
TotalReadoutTime	REC	number	Total readout time in seconds
MultibandAccelerationFactor	REC	number	Multiband/SMS factor
NumberOfVolumesDiscardedByScanner	REC	integer	Dummy scans removed
NumberOfVolumesDiscardedByUser	REC	integer	Volumes removed post-hoc
TaskDescription	REC	string	Longer description of the task
CogAtlasID	REC	string	Cognitive Atlas ID for the task
CogPOID	REC	string	Cognitive Paradigm Ontology ID
Instructions	REC	string	Instructions given to participants

Multi-echo BOLD

Field	Status	Type	Description
EchoTime	R	number	TE for this echo (each echo in separate file)
EchoTime1, EchoTime2	-	-	NOT used; use echo-<index> entity

BOLD Timing Details

SliceTiming - Array of times (in seconds) at which each slice was acquired relative to the start of volume acquisition. Length must equal the number of slices.

Example for ascending sequential (3 slices, TR=2s):

{"SliceTiming": [0.0, 0.667, 1.333]}

Example for interleaved (odd-first, 6 slices, TR=2s):

{"SliceTiming": [0.0, 0.667, 1.333, 0.333, 1.0, 1.667]}

PhaseEncodingDirection values:

• i / i- : along first image axis (typically left-right)
• j / j- : along second image axis (typically anterior-posterior)
• k / k- : along third image axis (typically inferior-superior)
• The - suffix indicates the negative direction along that axis

Diffusion-Weighted Imaging (dwi/)

Field	Status	Type	Description
PhaseEncodingDirection	R	string	Phase encoding direction
TotalReadoutTime	R	number	Total readout time in seconds
EchoTime	REC	number	TE in seconds
RepetitionTime	REC	number	TR in seconds
FlipAngle	REC	number	Flip angle in degrees
EffectiveEchoSpacing	REC	number	Effective echo spacing in seconds
MultibandAccelerationFactor	REC	number	SMS/multiband factor
SliceTiming	REC	array	Slice timing

DWI Gradient Files

.bvec file (3 rows x N columns, N = number of volumes):

0 0.707 -0.707 0 0.577
0 0.707 0.707 0 0.577
0 0 0 1 0.577

.bval file (1 row x N columns):

0 1000 1000 1000 2000

• b=0 volumes have zero-vectors in .bvec
• Gradient directions are in the image coordinate system
• Values are space-separated (not tab-separated)
• Number of columns must match number of volumes in the NIfTI

Fieldmaps (fmap/)

Case 1: Phase-difference map (_phasediff)

Field	Status	Type	Description
EchoTime1	R	number	TE of the first echo (shorter)
EchoTime2	R	number	TE of the second echo (longer)
IntendedFor	R	string/array	BIDS URI(s) of files to correct
B0FieldIdentifier	REC	string	Identifier for this B0 field

Case 2: Two phase maps (_phase1, _phase2)

Field	Status	Type	Description
EchoTime	R	number	TE for this phase image
IntendedFor	R	string/array	Files to correct

Case 3: Direct fieldmap (_fieldmap)

Field	Status	Type	Description
Units	R	string	Must be Hz or rad/s
IntendedFor	R	string/array	Files to correct

Case 4: "Pepolar" fieldmaps (_epi)

Field	Status	Type	Description
PhaseEncodingDirection	R	string	PE direction for this image
TotalReadoutTime	R	number	Total readout time
IntendedFor	R	string/array	Files to correct
B0FieldIdentifier	REC	string	Identifier for this B0 field
B0FieldSource	REC	string	Which B0 field to use

IntendedFor Syntax

BIDS URI format (recommended, v1.7+):

{
    "IntendedFor": [
        "bids::sub-01/func/sub-01_task-rest_bold.nii.gz",
        "bids::sub-01/dwi/sub-01_dwi.nii.gz"
    ]
}

Relative path format (legacy):

{
    "IntendedFor": [
        "func/sub-01_task-rest_bold.nii.gz",
        "dwi/sub-01_dwi.nii.gz"
    ]
}

B0FieldIdentifier/B0FieldSource (preferred in v1.9+):

// In the fieldmap sidecar
{"B0FieldIdentifier": "pepolar_fmap0"}

// In the BOLD sidecar
{"B0FieldSource": "pepolar_fmap0"}

Perfusion Imaging (perf/)

ASL

Field	Status	Type	Description
ArterialSpinLabelingType	R	string	CASL, PCASL, or PASL
PostLabelingDelay	R	number/array	PLD in seconds
BackgroundSuppression	R	boolean	Whether applied
MagneticFieldStrength	R	number	In Tesla
M0Type	R	string	Separate, Included, Estimate, Absent
RepetitionTimePreparation	R	number	Time between ASL pulses
LabelingDuration	R	number	Duration of labeling pulse
BackgroundSuppressionNumberPulses	REC	integer	Number of suppression pulses
BackgroundSuppressionPulseTime	REC	array	Timing of suppression pulses
VascularCrushing	REC	boolean	Whether applied
LabelingOrientation	REC	string	Orientation of labeling plane
LabelingDistance	REC	number	Distance from isocenter (mm)
BolusCutOffFlag	R (PASL)	boolean	Whether QUIPSS applied
BolusCutOffTimingSequence	R (PASL)	string	QUIPSS sequence type
BolusCutOffDelayTime	R (PASL)	number	QUIPSS delay time

aslcontext.tsv

Required file listing the order of volumes (label/control/m0scan):

volume_type
control
label
control
label
m0scan

EEG (eeg/)

Field	Status	Type	Description
TaskName	R	string	Name of the task
SamplingFrequency	R	number	In Hz
EEGReference	R	string	Reference electrode(s)
PowerLineFrequency	R	number	50 or 60 Hz (or n/a)
SoftwareFilters	R	object	Online filters applied
EEGPlacementScheme	REC	string	e.g., 10-20, 10-10
CapManufacturer	REC	string	Cap manufacturer
CapManufacturersModelName	REC	string	Cap model
EEGChannelCount	REC	integer	Number of EEG channels
EOGChannelCount	REC	integer	Number of EOG channels
ECGChannelCount	REC	integer	Number of ECG channels
EMGChannelCount	REC	integer	Number of EMG channels
MiscChannelCount	REC	integer	Number of misc channels
TriggerChannelCount	REC	integer	Number of trigger channels
RecordingDuration	REC	number	In seconds
RecordingType	REC	string	continuous, epoched, discontinuous

channels.tsv (EEG)

Column	Status	Description
name	R	Channel name
type	R	EEG, EOG, ECG, EMG, MISC, TRIG, etc.
units	R	V, mV, uV
sampling_frequency	OPT	Per-channel if different
low_cutoff	REC	High-pass filter frequency (Hz)
high_cutoff	REC	Low-pass filter frequency (Hz)
notch	REC	Notch filter frequency (Hz)
reference	REC	Reference electrode name
status	REC	good or bad
status_description	OPT	Reason for bad status

electrodes.tsv (EEG)

Column	Status	Description
name	R	Electrode name
x	R	X coordinate
y	R	Y coordinate
z	R	Z coordinate
type	OPT	Electrode type
material	OPT	Electrode material
impedance	OPT	Impedance in kOhm

MEG (meg/)

Field	Status	Type	Description
TaskName	R	string	Name of the task
SamplingFrequency	R	number	In Hz
PowerLineFrequency	R	number	50 or 60 Hz
DewarPosition	R	string	Position of the dewar
SoftwareFilters	R	object	Online filters
DigitizedLandmarks	R	boolean	Fiducials digitized
DigitizedHeadPoints	R	boolean	Head shape digitized
MEGChannelCount	REC	integer	Number of MEG channels
MEGREFChannelCount	REC	integer	Reference channels
ContinuousHeadLocalization	REC	boolean	HPI on
HeadCoilFrequency	REC	array	HPI coil frequencies
InstitutionName	REC	string	Institution name

PET (pet/)

Field	Status	Type	Description
TracerName	R	string	Name of the radiotracer
TracerRadionuclide	R	string	e.g., C11, F18, O15
InjectedRadioactivity	R	number	In MBq
InjectedRadioactivityUnits	R	string	Must be MBq
InjectedMass	R	number	Mass of tracer injected
InjectedMassUnits	R	string	e.g., ug
ModeOfAdministration	R	string	bolus, infusion, bolus-infusion
TimeZero	R	string	Time of injection (HH:MM:SS)
ScanStart	R	number	Start time relative to TimeZero
InjectionStart	R	number	Injection time relative to TimeZero
FrameTimesStart	R	array	Frame start times in seconds
FrameDuration	R	array	Frame durations in seconds
Units	R	string	Unit of voxel values (e.g., Bq/mL)
TracerRadLex	REC	string	RadLex ID for tracer
BodyWeight	REC	number	In kg
BodyPart	REC	string	Imaged body part
AttenuationCorrection	REC	string	Method description
ReconMethodName	REC	string	Reconstruction method
ReconMethodParameterLabels	REC	array	Parameter names
ReconMethodParameterValues	REC	array	Parameter values
ReconFilterType	REC	string	Post-recon filter type
ReconFilterSize	REC	number	Filter FWHM in mm

Microscopy (micr/)

Field	Status	Type	Description
Manufacturer	R	string	Microscope manufacturer
ManufacturersModelName	R	string	Microscope model
PixelSize	R	array	[X, Y] or [X, Y, Z] in micrometers
PixelSizeUnits	R	string	um (micrometers)
Magnification	REC	number	Objective magnification
SampleEnvironment	R	string	in vivo, ex vivo, in vitro
SampleFixation	REC	string	Fixation method
SampleStaining	REC	string	Staining protocol
SliceThickness	REC	number	In micrometers
TissueDeformationScaling	REC	number	Scaling factor

NIRS (nirs/)

Field	Status	Type	Description
TaskName	R	string	Name of the task
SamplingFrequency	R	number	In Hz
NIRSSourceOptodeCount	R	integer	Number of sources
NIRSDetectorOptodeCount	R	integer	Number of detectors
ACCELChannelCount	REC	integer	Accelerometer channels
NIRSPlacementScheme	REC	string	e.g., 10-20

Motion (motion/)

Field	Status	Type	Description
TaskName	R	string	Name of the task
SamplingFrequency	R	number	In Hz
TrackingSystemName	R	string	Name of tracking system
ACCELChannelCount	REC	integer	Accelerometer channels
GYROChannelCount	REC	integer	Gyroscope channels
MAGNChannelCount	REC	integer	Magnetometer channels
RotationOrder	REC	string	e.g., XYZ
RotationRule	REC	string	left-hand or right-hand
SpatialAxes	REC	string	e.g., ALS

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