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Common principles

Definitions

The keywords "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119].

Throughout this specification we use a list of terms and abbreviations. To avoid misunderstanding we clarify them here.

  1. Dataset - a set of neuroimaging and behavioral data acquired for a purpose of a particular study. A dataset consists of data acquired from one or more subjects, possibly from multiple sessions.

  2. Subject - a person or animal participating in the study. Used interchangeably with term Participant.

  3. Session - a logical grouping of neuroimaging and behavioral data consistent across subjects. Session can (but doesn't have to) be synonymous to a visit in a longitudinal study. In general, subjects will stay in the scanner during one session. However, for example, if a subject has to leave the scanner room and then be re-positioned on the scanner bed, the set of MRI acquisitions will still be considered as a session and match sessions acquired in other subjects. Similarly, in situations where different data types are obtained over several visits (for example fMRI on one day followed by DWI the day after) those can be grouped in one session. Defining multiple sessions is appropriate when several identical or similar data acquisitions are planned and performed on all -or most- subjects, often in the case of some intervention between sessions (e.g., training).

  4. Data acquisition - a continuous uninterrupted block of time during which a brain scanning instrument was acquiring data according to particular scanning sequence/protocol.

  5. Data type - a functional group of different types of data. In BIDS we define eight data types: func (task based and resting state functional MRI), dwi (diffusion weighted imaging), fmap (field inhomogeneity mapping data such as field maps), anat (structural imaging such as T1, T2, etc.), meg (magnetoencephalography), eeg (electroencephalography), ieeg (intracranial electroencephalography), beh (behavioral).

  6. Task - a set of structured activities performed by the participant. Tasks are usually accompanied by stimuli and responses, and can greatly vary in complexity. For the purpose of this specification we consider the so-called "resting state" a task. In the context of brain scanning, a task is always tied to one data acquisition. Therefore, even if during one acquisition the subject performed multiple conceptually different behaviors (with different sets of instructions) they will be considered one (combined) task.

  7. Event - a stimulus or subject response recorded during a task. Each event has an onset time and duration. Note that not all tasks will have recorded events (e.g., “resting state”).

  8. Run - an uninterrupted repetition of data acquisition that has the same acquisition parameters and task (however events can change from run to run due to different subject response or randomized nature of the stimuli). Run is a synonym of a data acquisition.

  9. <index> - a numeric value, possibly prefixed with arbitrary number of 0s for consistent indentation, e.g., it is 01 in run-01 following run-<index> specification.

  10. <label> - an alphanumeric value, possibly prefixed with arbitrary number of 0s for consistent indentation, e.g., it is rest in task-rest following task-<label> specification.

  11. suffix - an alphanumeric value, located after the key-value_ pairs (thus after the final _), right before the File extension, e.g., it is eeg in sub-05_task-matchingpennies_eeg.vhdr.

  12. File extension - a portion of the the file name after the left-most period (.) preceded by any other alphanumeric. For example, .gitignore does not have a file extension, but the file extension of test.nii.gz is .nii.gz. Note that the left-most period is included in the file extension.

Compulsory, optional, and additional data and metadata

The following standard describes a way of arranging data and writing down metadata for a subset of neuroimaging experiments. Some aspects of the standard are compulsory. For example a particular file name format is required when storing structural scans. Some aspects are regulated but optional. For example a T2 volume does not need to be included, but when it is available it should be saved under a particular file name specified in the standard. This standard aspires to describe a majority of datasets, but acknowledges that there will be cases that do not fit. In such cases one can include additional files and subfolders to the existing folder structure following common sense. For example one may want to include eye tracking data in a vendor specific format that is not covered by this standard. The most sensible place to put it is next to the continuous recording file with the same naming scheme but different extensions. The solutions will change from case to case and publicly available datasets will be reviewed to include common data types in the future releases of the BIDS specification.

File name structure

A file name consists of a chain of entities, or key-value pairs, a suffix and an extension. Two prominent examples of entities are subject and session.

For a data file that was collected in a given session from a given subject, the file name will begin with the string sub-<label>_ses-<label>.

Note that sub-<label> corresponds to the subject entity because it has the sub- "key" and<label> "value", where <label> would in a real data file correspond to a unique identifier of that subject, such as 01. The same holds for the session entity with its ses- key and its <label> value.

A chain of entities, followed by a suffix, connected by underscores (_) produces a human readable file name, such as sub-01_task-rest_eeg.edf. It is evident from the file name alone that the file contains resting state data from subject 01. The suffix eeg and the extension .edf depend on the imaging modality and the data format and indicate further details of the file's contents.

A summary of all entities in BIDS and the order in which they MUST be specified is available in the entity table in the appendix.

Source vs. raw vs. derived data

BIDS was originally designed to describe and apply consistent naming conventions to raw (unprocessed or minimally processed due to file format conversion) data. During analysis such data will be transformed and partial as well as final results will be saved. Derivatives of the raw data (other than products of DICOM to NIfTI conversion) MUST be kept separate from the raw data. This way one can protect the raw data from accidental changes by file permissions. In addition it is easy to distinguish partial results from the raw data and share the latter. See Storage of derived datasets for more on organizing derivatives.

Similar rules apply to source data, which is defined as data before harmonization, reconstruction, and/or file format conversion (for example, E-Prime event logs or DICOM files). This specification currently does not go into details of recommending a particular naming scheme for including different types of source data (raw event logs, parameter files, etc. before conversion to BIDS). However, in the case that these data are to be included:

  1. These data MUST be kept in separate sourcedata folder with a similar folder structure as presented below for the BIDS-managed data. For example: sourcedata/sub-01/ses-pre/func/sub-01_ses-pre_task-rest_bold.dicom.tgz or sourcedata/sub-01/ses-pre/func/MyEvent.sce.

  2. A README file SHOULD be found at the root of the sourcedata folder or the derivatives folder, or both. This file should describe the nature of the raw data or the derived data. We RECOMMEND including the PDF print-out with the actual sequence parameters generated by the scanner in the sourcedata folder.

Alternatively one can organize their data in the following way

my_dataset/
  sourcedata/
    ...
  rawdata/
    dataset_description.json
    participants.tsv
    sub-01/
    sub-02/
    ...
  derivatives/
    pipeline_1/
    pipeline_2/
    ...

In this example, where sourcedata and derivatives are not nested inside rawdata, only the rawdata subfolder needs to be a BIDS-compliant dataset. The subfolders of derivatives MAY be BIDS-compliant derivatives datasets (see [Non-compliant derivatives][#non-compliant-derivatives] for further discussion). This specification does not prescribe anything about the contents of sourcedata folders in the above example - nor does it prescribe the sourcedata, derivatives, or rawdata folder names. The above example is just a convention that can be useful for organizing raw, source, and derived data while maintaining BIDS compliancy of the raw data folder. When using this convention it is RECOMMENDED to set the SourceDatasets field in dataset_description.json of each subfolder of derivatives to:

{
  "SourceDatasets": [ {"URL": "file://../../rawdata/"} ]
}

Storage of derived datasets

Derivatives can be stored/distributed in two ways:

  1. Under a derivatives/ subfolder in the root of the source BIDS dataset folder to make a clear distinction between raw data and results of data processing. A data processing pipeline will typically have a dedicated directory under which it stores all of its outputs. Different components of a pipeline can, however, also be stored under different subfolders. There are few restrictions on the directory names; it is RECOMMENDED to use the format <pipeline>-<variant> in cases where it is anticipated that the same pipeline will output more than one variant (e.g., AFNI-blurring, AFNI-noblurring, etc.). For the sake of consistency, the subfolder name SHOULD be the GeneratedBy.Name field in data_description.json, optionally followed by a hyphen and a suffix (see Derived dataset and pipeline description).

    Example of derivatives with one directory per pipeline:

    <dataset>/derivatives/fmriprep-v1.4.1/sub-0001
    <dataset>/derivatives/spm/sub-0001
    <dataset>/derivatives/vbm/sub-0001
    

    Example of a pipeline with split derivative directories:

    <dataset>/derivatives/spm-preproc/sub-0001
    <dataset>/derivatives/spm-stats/sub-0001
    

    Example of a pipeline with nested derivative directories:

    <dataset>/derivatives/spm-preproc/sub-0001
    <dataset>/derivatives/spm-preproc/derivatives/spm-stats/sub-0001
    
  2. As a standalone dataset independent of the source (raw or derived) BIDS dataset. This way of specifying derivatives is particularly useful when the source dataset is provided with read-only access, for publishing derivatives as independent bodies of work, or for describing derivatives that were created from more than one source dataset. The sourcedata/ subdirectory MAY be used to include the source dataset(s) that were used to generate the derivatives. Likewise, any code used to generate the derivatives from the source data MAY be included in the code/ subdirectory.

    Example of a derivative dataset including the raw dataset as source:

    my_processed_data/
      code/
        processing_pipeline-1.0.0.img
        hpc_submitter.sh
        ...
      sourcedata/
        dataset_description.json
        participants.tsv
        sub-01/
        sub-02/
        ...
      dataset_description.json
      sub-01/
      sub-02/
      ...
    

Throughout this specification, if a section applies particularly to derivatives, then Case 1 will be assumed for clarity in templates and examples, but removing /derivatives/<pipeline> from the template name will provide the equivalent for Case 2. In both cases, every derivatives dataset is considered a BIDS dataset and must include a dataset_description.json file at the root level (see Dataset description. Consequently, files should be organized to comply with BIDS to the full extent possible (that is, unless explicitly contradicted for derivatives). Any subject-specific derivatives should be housed within each subject’s directory; if session-specific derivatives are generated, they should be deposited under a session subdirectory within the corresponding subject directory; and so on.

Non-compliant deriatives

Nothing in this specification should be interpreted to disallow the storage/distribution of non-compliant derivatives of BIDS datasets. In particular, if a BIDS dataset contains a derivatives/ sub-directory, the contents of that directory may be a heterogeneous mix of BIDS Derivatives datasets and non-compliant derivatives.

The Inheritance Principle

Any metadata file (.json, .bvec, .tsv, etc.) may be defined at any directory level, but no more than one applicable file may be defined at a given level (Example 1). The values from the top level are inherited by all lower levels unless they are overridden by a file at the lower level. For example, sub-*_task-rest_bold.json may be specified at the participant level, setting TR to a specific value. If one of the runs has a different TR than the one specified in that file, another sub-*_task-rest_bold.json file can be placed within that specific series directory specifying the TR for that specific run. There is no notion of "unsetting" a key/value pair. Once a key/value pair is set in a given level in the dataset, lower down in the hierarchy that key/value pair will always have some assigned value. Files for a particular participant can exist only at participant level directory, i.e /dataset/sub-*[/ses-*]/sub-*_T1w.json. Similarly, any file that is not specific to a participant is to be declared only at top level of dataset for eg: task-sist_bold.json must be placed under /dataset/task-sist_bold.json

Example 1: Two JSON files that are erroneously at the same level

sub-01/
    ses-test/
        sub-01_ses-test_task-overtverbgeneration_bold.json
        sub-01_ses-test_task-overtverbgeneration_run-2_bold.json
        anat/
            sub-01_ses-test_T1w.nii.gz
        func/
            sub-01_ses-test_task-overtverbgeneration_run-1_bold.nii.gz
            sub-01_ses-test_task-overtverbgeneration_run-2_bold.nii.gz

In the above example, two JSON files are listed under sub-01/ses-test/, which are each applicable to sub-01_ses-test_task-overtverbgeneration_run-2_bold.nii.gz, violating the constraint that no more than one file may be defined at a given level of the directory structure. Instead sub-01_ses-test_task-overtverbgeneration_run-2_bold.json should have been under sub-01/ses-test/func/.

Example 2: Multiple runs and recs with same acquisition (acq) parameters

sub-01/
    anat/
    func/
        sub-01_task-xyz_acq-test1_run-1_bold.nii.gz
        sub-01_task-xyz_acq-test1_run-2_bold.nii.gz
        sub-01_task-xyz_acq-test1_rec-recon1_bold.nii.gz
        sub-01_task-xyz_acq-test1_rec-recon2_bold.nii.gz
        sub-01_task-xyz_acq-test1_bold.json

For the above example, all NIfTI files are acquired with same scanning parameters (acq-test1). Hence a JSON file describing the acq parameters will apply to different runs and rec files. Also if the JSON file (task-xyz_acq-test1_bold.json) is defined at dataset top level directory, it will be applicable to all task runs with test1 acquisition parameter.

Example 3: Multiple JSON files at different levels for same task and acquisition parameters

task-xyz_acq-test1_bold.json
sub-01/
    anat/
    func/
        sub-01_task-xyz_acq-test1_run-1_bold.nii.gz
        sub-01_task-xyz_acq-test1_rec-recon1_bold.nii.gz
        sub-01_task-xyz_acq-test1_rec-recon2_bold.nii.gz
        sub-01_task-xyz_acq-test1_bold.json

In the above example, the fields from the task-xyz_acq-test1_bold.json file at the top directory will apply to all bold runs. However, if there is a key with different value in the sub-01/func/sub-01_task-xyz_acq-test1_bold.json file defined at a deeper level, that value will be applicable for that particular run/task NIfTI file/s. In other words, the .json file at the deeper level overrides values that are potentially also defined in the .json at a more shallow level. If the .json file at the more shallow level contains key-value-pairs that are not present in the .json file at the deeper level, these key-value-pairs are inherited by the .json file at the deeper level (but NOT vice versa!).

Good practice recommendations

Try to avoid excessive amount of overrides. Do not specify a field value in the upper levels if lower levels have more or less even distribution of multiple possible values. E.g., if a field X has one value for all ses-01/ and another for all ses-02/ it better not to be defined at all in the .json at the upper level.

File Formation specification

Imaging files

All imaging data MUST be stored using the NIfTI file format. We RECOMMEND using compressed NIfTI files (.nii.gz), either version 1.0 or 2.0. Imaging data SHOULD be converted to the NIfTI format using a tool that provides as much of the NIfTI header information (such as orientation and slice timing information) as possible. Since the NIfTI standard offers limited support for the various image acquisition parameters available in DICOM files, we RECOMMEND that users provide additional meta information extracted from DICOM files in a sidecar JSON file (with the same filename as the .nii[.gz] file, but with a .json extension). Extraction of BIDS compatible metadata can be performed using dcm2niix and dicm2nii DICOM to NIfTI converters. The BIDS-validator will check for conflicts between the JSON file and the data recorded in the NIfTI header.

Tabular files

Tabular data MUST be saved as tab delimited values (.tsv) files, i.e., CSV files where commas are replaced by tabs. Tabs MUST be true tab characters and MUST NOT be a series of space characters. Each TSV file MUST start with a header line listing the names of all columns (with the exception of physiological and other continuous recordings). Names MUST be separated with tabs. It is RECOMMENDED that the column names in the header of the TSV file are written in snake_case with the first letter in lower case (e.g., variable_name, not Variable_name). String values containing tabs MUST be escaped using double quotes. Missing and non-applicable values MUST be coded as n/a. Numerical values MUST employ the dot (.) as decimal separator and MAY be specified in scientific notation, using e or E to separate the significand from the exponent. TSV files MUST be in UTF-8 encoding.

Example:

onset duration  response_time correct stop_trial  go_trial
200 200 0 n/a n/a n/a

Tabular files MAY be optionally accompanied by a simple data dictionary in the form of a JSON object within a JSON file. The JSON files containing the data dictionaries MUST have the same name as their corresponding tabular files but with .json extensions. If a data dictionary is provided, it MAY contain one or more fields describing the columns found in the TSV file (in addition to any other metadata one wishes to include that describe the file as a whole). Note that if a field name included in the data dictionary matches a column name in the TSV file, then that field MUST contain a description of the corresponding column, using an object containing the following fields:

Field name Definition
LongName Long (unabbreviated) name of the column.
Description Description of the column.
Levels For categorical variables: a dictionary of possible values (keys) and their descriptions (values).
Units Measurement units. SI units in CMIXF formatting are RECOMMENDED (see Units).
TermURL URL pointing to a formal definition of this type of data in an ontology available on the web.

Example:

{
  "test": {
    "LongName": "Education level",
    "Description": "Education level, self-rated by participant",
    "Levels": {
      "1": "Finished primary school",
      "2": "Finished secondary school",
      "3": "Student at university",
      "4": "Has degree from university"
    }
  },
  "bmi": {
    "LongName": "Body mass index",
    "Units": "kg/m^2",
    "TermURL": "http://purl.bioontology.org/ontology/SNOMEDCT/60621009"
  }
}

Key/value files (dictionaries)

JavaScript Object Notation (JSON) files MUST be used for storing key/value pairs. JSON files MUST be in UTF-8 encoding. Extensive documentation of the format can be found here: http://json.org/. Several editors have built-in support for JSON syntax highlighting that aids manual creation of such files. An online editor for JSON with built-in validation is available at: http://jsoneditoronline.org.  It is RECOMMENDED that keys in a JSON file are written in CamelCase with the first letter in upper case (e.g., SamplingFrequency, not samplingFrequency). Note however, when a JSON file is used as an accompanying sidecar file for a TSV file, the keys linking a TSV column with their description in the JSON file need to follow the exact formatting as in the TSV file.

Example of a hypothetical *_bold.json file, accompanying a *_bold.nii file:

{
  "RepetitionTime": 3,
  "Instruction": "Lie still and keep your eyes open"
}

Example of a hypothetical *_events.json file, accompanying an *_events.tsv file. Note that the JSON file contains a key describing an arbitrary column stim_presentation_side in the TSV file it accompanies. See task events section for more information.

{
  "stim_presentation_side": {
    "Levels": {
      "1": "stimulus presented on LEFT side",
      "2": "stimulus presented on RIGHT side"
    }
  }
}

Participant names and other labels

BIDS allows for custom user-defined <label>s and <index>es e.g., for naming of participants, sessions, acquisition schemes, etc. Note that they MUST consist only of allowed characters as described in Definitions above. In <index>es we RECOMMEND using zero padding (e.g., 01 instead of 1 if you have more than nine subjects) to make alphabetical sorting more intuitive. Note that zero padding SHOULD NOT be used to merely maintain uniqueness of <index>es.

Please note that a given label or index is distinct from the "prefix" it refers to. For example sub-01 refers to the sub entity (a subject) with the label 01. The sub- prefix is not part of the subject label, but must be included in file names (similarly to other key names).

Units

All units SHOULD be specified as per International System of Units (abbreviated as SI, from the French Système international (d'unités)) and can be SI units or SI derived units. In case there are valid reasons to deviate from SI units or SI derived units, the units MUST be specified in the sidecar JSON file. In case data is expressed in SI units or SI derived units, the units MAY be specified in the sidecar JSON file. In case non-standard prefixes are added to SI or non-SI units, these non-standard prefixed units MUST be specified in the JSON file. See Appendix V for a list of standard units and prefixes. Note also that for the formatting of SI units, the CMIXF-12 convention for encoding units is RECOMMENDED. CMIXF provides a consistent system for all units and prefix symbols with only basic characters, avoiding symbols that can cause text encoding problems; for example the CMIXF formatting for "micro volts" is uV, "degrees Celsius" is oC and "Ohm" is Ohm. See Appendix V for more information.

For additional rules, see below:

  • Elapsed time SHOULD be expressed in seconds. Please note that some DICOM parameters have been traditionally expressed in milliseconds. Those need to be converted to seconds.

  • Frequency SHOULD be expressed in Hertz.

Describing dates and timestamps:

  • Date time information MUST be expressed in the following format YYYY-MM-DDThh:mm:ss[.000000] (year, month, day, hour (24h), minute, second, and optionally fractional second). This is equivalent to the RFC3339 "date-time" format, time zone is always assumed as local time). No specific precision is required for fractional seconds, but the precision SHOULD be consistent across the dataset. For example 2009-06-15T13:45:30

  • Time stamp information MUST be expressed in the following format: 13:45:30[.000000]

  • Dates can be shifted by a random number of days for privacy protection reasons. To distinguish real dates from shifted dates, always use year 1925 or earlier when including shifted years. For longitudinal studies dates MUST be shifted by the same number of days within each subject to maintain the interval information. For example: 1867-06-15T13:45:30

  • Age SHOULD be given as the number of years since birth at the time of scanning (or first scan in case of multi session datasets). Using higher accuracy (weeks) should in general be avoided due to privacy protection, unless when appropriate given the study goals, e.g., when scanning babies.

Directory structure

Single session example

This is an example of the folder and file structure. Because there is only one session, the session level is not required by the format. For details on individual files see descriptions in the next section:

sub-control01/
    anat/
        sub-control01_T1w.nii.gz
        sub-control01_T1w.json
        sub-control01_T2w.nii.gz
        sub-control01_T2w.json
    func/
        sub-control01_task-nback_bold.nii.gz
        sub-control01_task-nback_bold.json
        sub-control01_task-nback_events.tsv
        sub-control01_task-nback_physio.tsv.gz
        sub-control01_task-nback_physio.json
        sub-control01_task-nback_sbref.nii.gz
    dwi/
        sub-control01_dwi.nii.gz
        sub-control01_dwi.bval
        sub-control01_dwi.bvec
    fmap/
        sub-control01_phasediff.nii.gz
        sub-control01_phasediff.json
        sub-control01_magnitude1.nii.gz
        sub-control01_scans.tsv
code/
    deface.py
derivatives/
README
participants.tsv
dataset_description.json
CHANGES

Unspecified data

Additional files and folders containing raw data MAY be added as needed for special cases. All non-standard file entities SHOULD conform to BIDS-style naming conventions, including alphabetic entities and suffixes and alphanumeric labels/indices. Non-standard suffixes SHOULD reflect the nature of the data, and existing entities SHOULD be used when appropriate. For example, an ASSET calibration scan might be named sub-01_acq-ASSET_calibration.nii.gz.

Non-standard files and directories should be named with care. Future BIDS efforts may standardize new entities and suffixes, changing the meaning of file names and setting requirements on their contents or metadata. Validation and parsing tools MAY treat the presence of non-standard files and directories as an error, so consult the details of these tools for mechanisms to suppress warnings or provide interpretations of your file names.