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 (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 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 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: 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: 7. Version your dataset Use CHANGES to document dataset modifications. Consider DataLad for full version control of large datasets. 8. Deface anatomical images Remove facial features from T1w/T2w images before sharing (e.g., using pydeface , mri deface , or afni refacer ). Store defaced versions as the primary data or use defacemask files. 9. Use BIDS URIs for provenance In derivatives, reference source files using BIDS URIs: bids::sub 01/anat/sub 01 T1w.nii.gz . 10. Prefer community tools Use established BIDS Apps (fMRIPrep, MRIQC, QSIPrep) rather than custom pipelines when possible. They handle BIDS I/O correctly and produce BIDS compliant derivatives. 11. Study bids examples The [bids examples](https://github.com/bids standard/bids examples) repository is the canonical collection of prototypical BIDS datasets covering different modalities and use cases (MRI, fMRI, DWI, EEG, MEG, iEEG, PET, ASL, genetics, derivatives, and more). Use it as a reference when structuring your own dataset, as test data for BIDS tools, or to understand how a specific modality should be organized. Each example passes the BIDS validator. BIDS Extension Proposals (BEPs) BEPs are community driven proposals to extend BIDS to new modalities, derivatives, or metadata. The full list with status, leads, and links is in references/beps.yml (fetched from the [bids website](https://github.com/bids standard/bids website/blob/main/data/beps/beps.yml)). BEP specific schema previews are rendered at https://github.com/bids standard/bids schema/tree/main/BEPs. Current BEPs (as of schema update): BEP Title Content Status 004 Susceptibility Weighted Imaging raw Seeking new leader 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 (p