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Modality agnostic files

Dataset description


  • dataset_description.json


The file dataset_description.json is a JSON file describing the dataset. Every dataset MUST include this file with the following fields:

Field name Definition
Name REQUIRED. Name of the dataset.
BIDSVersion REQUIRED. The version of the BIDS standard that was used.
DatasetType RECOMMENDED. The interpretaton of the dataset. MUST be one of "raw" or "derivative". For backwards compatibility, the default value is "raw".
License RECOMMENDED. The license for the dataset. The use of license name abbreviations is RECOMMENDED for specifying a license (see Appendix II). The corresponding full license text MAY be specified in an additional LICENSE file.
Authors OPTIONAL. List of individuals who contributed to the creation/curation of the dataset.
Acknowledgements OPTIONAL. Text acknowledging contributions of individuals or institutions beyond those listed in Authors or Funding.
HowToAcknowledge OPTIONAL. Text containing instructions on how researchers using this dataset should acknowledge the original authors. This field can also be used to define a publication that should be cited in publications that use the dataset.
Funding OPTIONAL. List of sources of funding (grant numbers).
EthicsApprovals OPTIONAL. List of ethics committee approvals of the research protocols and/or protocol identifiers.
ReferencesAndLinks OPTIONAL. List of references to publication that contain information on the dataset, or links.
DatasetDOI OPTIONAL. The Document Object Identifier of the dataset (not the corresponding paper).


  "Name": "The mother of all experiments",
  "BIDSVersion": "1.4.0",
  "DatasetType": "raw",
  "License": "CC0",
  "Authors": [
    "Paul Broca",
    "Carl Wernicke"
  "Acknowledgements": "Special thanks to Korbinian Brodmann for help in formatting this dataset in BIDS. We thank Alan Lloyd Hodgkin and Andrew Huxley for helpful comments and discussions about the experiment and manuscript; Hermann Ludwig Helmholtz for administrative support; and Claudius Galenus for providing data for the medial-to-lateral index analysis.",
  "HowToAcknowledge": "Please cite this paper:",
  "Funding": [
    "National Institute of Neuroscience Grant F378236MFH1",
    "National Institute of Neuroscience Grant 5RMZ0023106"
  "EthicsApprovals": [
    "Army Human Research Protections Office (Protocol ARL-20098-10051, ARL 12-040, and ARL 12-041)"
  "ReferencesAndLinks": [
    "Alzheimer A., & Kraepelin, E. (2015). Neural correlates of presenile dementia in humans. Journal of Neuroscientific Data, 2, 234001."
  "DatasetDOI": ""

Derived dataset and pipeline description

As for any BIDS dataset, a dataset_description.json file MUST be found at the top level of the a derived dataset: <dataset>/derivatives/<pipeline_name>/dataset_description.json

In addition to the keys for raw BIDS datasets, derived BIDS datasets include the following REQUIRED and RECOMMENDED dataset_description.json keys:

Key name Description
GeneratedBy REQUIRED. List of [objects][object] with at least one element.
SourceDatasets RECOMMENDED. A list of [objects][object] specifying the locations and relevant attributes of all source datasets. Valid fields in each object include URL, DOI, and Version.

Each object in the GeneratedBy list includes the following REQUIRED, RECOMMENDED and OPTIONAL keys:

Key name Description
Name REQUIRED. Name of the pipeline or process that generated the outputs. Use "Manual" to indicate the derivatives were generated by hand, or adjusted manually after an initial run of an automated pipeline.
Version RECOMMENDED. Version of the pipeline.
Description OPTIONAL. Plain-text description of the pipeline or process that generated the outputs. RECOMMENDED if Name is "Manual".
CodeURL OPTIONAL. URL where the code used to generate the derivatives may be found.
Container OPTIONAL. [Object][object] specifying the location and relevant attributes of software container image used to produce the derivative. Valid fields in this object include Type, Tag and URI.


  "Name": "FMRIPREP Outputs",
  "BIDSVersion": "1.4.0",
  "DatasetType": "derivative",
  "GeneratedBy": [
      "Name": "fmriprep",
      "Version": "1.4.1",
      "Container": {
        "Type": "docker",
        "Tag": "poldracklab/fmriprep:1.4.1"
      "Name": "Manual",
      "Description": "Re-added RepetitionTime metadata to bold.json files"
  "SourceDatasets": [
      "DOI": "10.18112/openneuro.ds000114.v1.0.1",
      "URL": "",
      "Version": "1.0.1"

If a derived dataset is stored as a subfolder of the raw dataset, then the Name field of the first GeneratedBy object MUST be a substring of the derived dataset folder name. That is, in a directory <dataset>/derivatives/<pipeline>[-<variant>]/, the first GeneratedBy object should have a Name of <pipeline>.


In addition a free form text file (README) describing the dataset in more details SHOULD be provided. The README file MUST be either in ASCII or UTF-8 encoding.


Version history of the dataset (describing changes, updates and corrections) MAY be provided in the form of a CHANGES text file. This file MUST follow the CPAN Changelog convention: The CHANGES file MUST be either in ASCII or UTF-8 encoding.


1.0.1 2015-08-27
  - Fixed slice timing information.

1.0.0 2015-08-17
  - Initial release.


A LICENSE file MAY be provided in addition to the short specification of the used license in the dataset_description.json "License" field. The "License" field and LICENSE file MUST correspond. The LICENSE file MUST be either in ASCII or UTF-8 encoding.

Participants file



The purpose of this RECOMMENDED file is to describe properties of participants such as age, sex, handedness etc. In case of single-session studies, this file has one compulsory column participant_id that consists of sub-<label>, followed by a list of optional columns describing participants. Each participant MUST be described by one and only one row.

Commonly used optional columns in participant.tsv files are age, sex, and handedness. We RECOMMEND to make use of these columns, and in case that you do use them, we RECOMMEND to use the following values for them:

  • age: numeric value in years (float or integer value)

  • sex: string value indicating phenotypical sex, one of "male", "female", "other"

    • for "male", use one of these values: male, m, M, MALE, Male

    • for "female", use one of these values: female, f, F, FEMALE, Female

    • for "other", use one of these values: other, o, O, OTHER, Other

  • handedness: string value indicating one of "left", "right", "ambidextrous"

    • for "left", use one of these values: left, l, L, LEFT, Left

    • for "right", use one of these values: right, r, R, RIGHT, Right

    • for "ambidextrous", use one of these values: ambidextrous, a, A, AMBIDEXTROUS, Ambidextrous

Throughout BIDS you can indicate missing values with n/a (i.e., "not available").

participants.tsv example:

participant_id age sex handedness group
sub-01 34 M right read
sub-02 12 F right write
sub-03 33 F n/a read

It is RECOMMENDED to accompany each participants.tsv file with a sidecar participants.json file to describe the TSV column names and properties of their values (see also the section on tabular files). Such sidecar files are needed to interpret the data, especially so when optional columns are defined beyond age, sex, and handedness, such as group in this example, or when a different age unit is needed (e.g., gestational weeks). If no units is provided for age, it will be assumed to be in years relative to date of birth.

participants.json example:

    "age": {
        "Description": "age of the participant",
        "Units": "years"
    "sex": {
        "Description": "sex of the participant as reported by the participant",
        "Levels": {
            "M": "male",
            "F": "female"
    "handedness": {
        "Description": "handedness of the participant as reported by the participant",
        "Levels": {
            "left": "left",
            "right": "right"
    "group": {
        "Description": "experimental group the participant belonged to",
        "Levels": {
            "read": "participants who read an inspirational text before the experiment",
            "write": "participants who wrote an inspirational text before the experiment"

Phenotypic and assessment data



Optional: Yes

If the dataset includes multiple sets of participant level measurements (for example responses from multiple questionnaires) they can be split into individual files separate from participants.tsv.

Each of the measurement files MUST be kept in a /phenotype directory placed at the root of the BIDS dataset and MUST end with the .tsv extension. File names SHOULD be chosen to reflect the contents of the file. For example, the "Adult ADHD Clinical Diagnostic Scale" could be saved in a file called /phenotype/acds_adult.tsv.

The files can include an arbitrary set of columns, but one of them MUST be participant_id and the entries of that column MUST correspond to the subjects in the BIDS dataset and participants.tsv file.

As with all other tabular data, the additional phenotypic information files MAY be accompanied by a JSON file describing the columns in detail (see Tabular files). In addition to the column description, a section describing the measurement tool (as a whole) MAY be added under the name MeasurementToolMetadata. This section consists of two keys:

  • Description: A free text description of the measurement tool
  • TermURL: A link to an entity in an ontology corresponding to this tool.

As an example, consider the contents of a file called phenotype/acds_adult.json:

  "MeasurementToolMetadata": {
    "Description": "Adult ADHD Clinical Diagnostic Scale V1.2",
    "TermURL": ""
  "adhd_b": {
    "Description": "B. CHILDHOOD ONSET OF ADHD (PRIOR TO AGE 7)",
    "Levels": {
      "1": "YES",
      "2": "NO"
  "adhd_c_dx": {
    "Description": "As child met A, B, C, D, E and F diagnostic criteria",
    "Levels": {
      "1": "YES",
      "2": "NO"

Please note that in this example MeasurementToolMetadata includes information about the questionnaire and adhd_b and adhd_c_dx correspond to individual columns.

In addition to the keys available to describe columns in all tabular files (LongName, Description, Levels, Units, and TermURL) the participants.json file as well as phenotypic files can also include column descriptions with a Derivative field that, when set to true, indicates that values in the corresponding column is a transformation of values from other columns (for example a summary score based on a subset of items in a questionnaire).

Scans file



Optional: Yes

The purpose of this file is to describe timing and other properties of each imaging acquisition sequence (each run .nii[.gz] file) within one session. Each .nii[.gz] file should be described by at most one row. Relative paths to files should be used under a compulsory filename header. If acquisition time is included it should be under acq_time header. Datetime should be expressed as described in Units. For anonymization purposes all dates within one subject should be shifted by a randomly chosen (but consistent across all runs etc.) number of days. This way relative timing would be preserved, but chances of identifying a person based on the date and time of their scan would be decreased. Dates that are shifted for anonymization purposes should be set to a year 1925 or earlier to clearly distinguish them from unmodified data. Shifting dates is RECOMMENDED, but not required.

Additional fields can include external behavioral measures relevant to the scan. For example vigilance questionnaire score administered after a resting state scan.


filename  acq_time
func/sub-control01_task-nback_bold.nii.gz 1877-06-15T13:45:30
func/sub-control01_task-motor_bold.nii.gz 1877-06-15T13:55:33


Template: code/*

Source code of scripts that were used to prepare the dataset (for example if it was anonymized or defaced) MAY be stored here.1 Extra care should be taken to avoid including original IDs or any identifiable information with the source code. There are no limitations or recommendations on the language and/or code organization of these scripts at the moment.

1Storing actual source files with the data is preferred over links to external source repositories to maximize long term preservation (which would suffer if an external repository would not be available anymore).