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 m

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 (reviewed against v1.11.2, released 2026 09 29) 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) proposes support for extracellular recordings including Neuropixels probes, bringing BIDS to a prevalent methodology in animal neuroscience research (see also the neuropixels analysis skill). Repository submission requirements vary by modality and archive; check the target archive before preparing a deposit. 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 Workflows Twelve workflow areas, each with worked code, are documented in [references/core workflows.md](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 with the BIDS validator as well as indexing with PyBIDS. Successful indexing is not a full compliance check; record the validator and BIDS schema versions, and inspect warnings and metadata inheritance before analysis. 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 From the skill directory, update schema and BEPs with python scripts/update schema.py . The bundled snapshot is BIDS 1.11.2 / schema 2.0.0; BEP proposals are not adopted requirements. If ReadTheDocs blocks an automated fetch, use the documented versioned GitHub export in the updater help. 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": "1.11.2"} . 2. Inconsistent subjects warning Cause : Not all subjects have the same set of files (some missing sessions, runs, etc.). Fix : Review severity and the exact issue code in the validator JSON report and document missing data in participants.tsv or scans.tsv . The current schema validator does not provide the legacy ignoreSubjectConsistency flag; use a narrowly scoped config only for reviewed exceptions. 3. Missing SliceTiming Cause : dcm2niix couldn't extract slice timing from DICOM headers. Fix : Recover actual slice acquisition offsets from scanner metadata or a verified sequence protocol. Slice order alone does not determine timing, especially with multiband acquisition or dead time. Store offsets in seconds in slice index order, accounting for SliceEncodingDirection ; document missing timing instead of inventing it. 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. Anatomical direction depends on the NIfTI affine and converter orientation; do not infer j or its sign from an AP/PA series label alone. Verify against scanner metadata and the image orientation. 5. PyBIDS is slow on large datasets Cause : Full filesystem indexing on every BIDSLayout() call. Fix : Use database path to cache the index in a directory outside the dataset: 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 are seconds relative to the first stored data point in the corresponding recording. If dummy volumes were discarded before storage, reset time zero to the first retained volume; negative onsets are allowed. 8. TSV files fail validation Cause : Encoding or delimiter issues (spaces instead of tabs, BOM characters). 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 Preserve source DICOMs and conversion provenance in controlled storage; sourcedata/ is excluded from raw BIDS validation, not deidentified. Review identifiers before any sharing. 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: 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, use bids:raw:sub 01/anat/sub 01 T1w.nii.gz for raw sources and define "DatasetLinks": {"raw": "../.."} when the derivative root is raw/derivatives/pipeline/ . bids:: resolves within the current dataset, which in a derivative is the derivative dataset. 10. Prefer community tools Use established BIDS Apps (fMRIPrep, MRIQC, QSIPrep) rather than custom pipelines when possible. Check the chosen release's supported inputs and output conventions; software output still needs validation. 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. Pin an example revision and validator/schema versions; the upstream test suite also tracks expected failures while implementations evolve. 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. The bundled BEP listing was refreshed on 2026 09 30. Read each entry's proposal/PR and status before using draft entities; BEP032 remains a proposal, not part of stable 1.11.2. Use the repository directory listing to discover preview schema paths; do not assume a BEPs/BEP032/schema.json URL exists. 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 BI