Metadata and file formats#

Metadata are stored in .json and .tsv files. These files are language-agnostic, meaning you can work with them in, for example: Python, Matlab, or R. This page covers common ways to read/write these files in common languages for neuroscience analysis.

More extensive example templates can be found here, well as MATLAB / Octave code and Python code to help you generate some of those files.

JSON Files#

JSON files are text files that take the following structure:

    "key": "value",
    "key2": "value2",
    "key3": {
        "subkey1": "subvalue1"

Note that they can be nested (curly brackets within curly brackets). Here are some common ways to read / write these files.


To read/write JSON online, we recommend the following website:

Matlab / Octave#

There are many toolboxes in Matlab for reading / writing JSON files.

Since MATLAB R2016b, you can use the built-in functions jsonencode (to write) and jsondecode (to read) JSON files. Hopefully they should be available in Octave 6.1 next year.

The JSONio library will allow you to read and write JSON files with matlab and octave (see examples below to use jsonwrite and jsonread).

SPM12 uses the JSONio library by calling spm_jsonwrite and spm_jsonread and it has other interesting functions to help you with BIDS.

bids-matlab has 2 functions (bids.util.jsonencode and bids.util.jsondecode) that act as wrappers and will use whatever implementation (SPM, JSONio, MATLAB) is available.

The examples below are for the JSONio library:

Reading a .json file#


Writing a .json file#

root_dir = './';
project = 'temp';
sub_id = '01';
ses_id = '01';
acquisition = 'anat';

anat_json_name = fullfile(root_dir,project,...
                            ['sub-' sub_id],...
                            ['ses-' ses_id],...
                            ['sub-' sub_id '_ses-' ses_id '_T1W.json']);

% Assign the fields in the Matlab structure that can be saved as a json:
anat_json.Manufacturer = 'GE';
anat_json.ManufacturersModelName =  'Discovery MR750';
anat_json.MagneticFieldStrength = 3;
anat_json.PulseSequence = 'T1 weighted SPGR';

json_options.indent = '    '; % this makes the json look pretier when opened in a txt editor


In Python, JSON support is built into the core library, meaning you don’t need to install anything to read/write JSON files. In addition, the structure of JSON is almost identical to that of Python dictionaries (assuming you are only storing text / numbers in the dictionary). To that extent.

Reading a .json file#

import json
with open('myfile.json', 'r') as ff:
    data = json.load(ff)

Writing a .json file#

import json
data = {'field1': 'value1', 'field2': 3, 'field3': 'field3'}
with open('my_output_file.json', 'w') as ff:
    json.dump(data, ff)


There is a new package to help intract with BIDS datasets: bbuchsbaum/bidser

There are several packages for reading and writing JSON files from R. In this example, we will be using jsonlite. Remember to install and call a package before using it.


Installing required package#


Reading a .json file:#

    data = fromJSON('myfile.json', pretty=TRUE)

Writing a .json file:#

    data = '{"field1": "value1", "field2": 3, "field3": "field3"}'
    writeLines(data, file="myData.json")

Interoperability issues#

Many parts of JSON files are often loaded as structures by MATLAB / Octave, where a key in a JSON file becomes fieldname in that structure.

Here is an example with a simple example.json

    "key": "value"

loaded with bids-matlab

>> json_content = bids.util.jsondecode('example.json')

json_content =

  struct with fields:

    key: 'value'

There are however some strict rules for what makes a valid fieldname in MATLAB and octave.

Fieldnames must:

  • start with a letter, otherwise assigning to that field will error

  • contain only letters, numbers, and/or the underscore character, otherwise assigning to that field will error, and

  • must be no longer than namelengthmax (currently 63) characters, otherwise you will receive a warning and the field name will be truncated

If there are keys in your JSON that do not comply to those rules, they keys will be renamed when loading which can lead to some headaches down the line.

For example when loading the bad_keys.json

    "@foo": "@foo",
    "1": "1",
    "x1": "x1",
    "x_1": "x_1",
    "/t": "/t",
    "%f": "%f"

We get some quite different fieldnames when read with matlab:

>> jsondecode(fileread('bad_keys.json'))

ans =

  struct with fields:

    x_foo: '@foo'
       x1: '1'
     x1_1: 'x1'
      x_1: 'x_1'
      x_t: '/t'
      x_f: '%f'

or with JSONio for Octave (though at least here we get a warning):

>> jsonread('bad_keys.json')
Warning: Duplicate key.

ans =

  struct with fields:

    x_foo: '@foo'
       x1: 'x1'
      x_1: 'x_1'
      x_t: '/t'
      x_f: '%f'

This can lead to some unexpected behavior if you did not know about this.

If you load this collision.json

    "1": "1",
    "x1": "x1",
    "x_1": "x_1"

and try to retrieve the value associated to the key x1, you will in fact be getting the value for the key 1.

>> json_content = bids.util.jsondecode('collision.json');
>> json_content.x1

       x1: '1'

Why and when does this matter for BIDS?

In most cases this will not be an issue, but this could be problem if in your events.tsv you have named some of your trial_type things like 1_face, 2_sound and then want to annotate those events in a side car JSON file like this.

    "trial_type": {
        "LongName": "",
        "Description": "image type",
        "Levels": {
            "1_face": "A face is displayed",
            "2_sound": "A sound is played"

If you do this, it will be much harder to work with that JSON file for anyone who uses MATLAB or Octave.


So in general here are some suggestions on how to name your events:

  • start with a letter

  • make sure they contain only letters, numbers, and/or the underscore character

  • make sure they are must be no longer than currently 63 characters

TSV files#

A Tab-Separate Values (TSV) file is a text file where tab characters (\t) separate fields that are in the file. It is structured as a table, with each column representing a field of interest, and each row representing a single datapoint.

Below are ways to read / write TSV files in common languages.


Reading a .tsv file:#

table_content = readtable(filename, ...
                            'FileType', 'text', ...
                            'Delimiter', '\t', ...
                            'TreatAsEmpty', {'N/A','n/a'});

Writing a .tsv file:#


root_dir = pwd;
bidsProject = 'temp';
mkdir(fullfile(root_dir, bidsProject));
bids_participants_name = 'participants.tsv';

participant_id = ['sub-01'; 'sub-02'];
age = [20 30]';
sex = ['m';'f'];

t = table(participant_id,age,sex);
writetable(t, fullfile(root_dir, bidsProject, bids_participants_name), ...
              'FileType', 'text', ...
              'Delimiter', '\t');


The writetable function is not implemented in older version of Octave (e.g 4.2.2) and the table function differs from its matlab counterpart. These are still in development for future releases so some of the scripts provided in the BIDS starter-kit repository in the matlab code folder to create .tsv might not work with octave because of that reason.


In Python, the easiest way to work with TSV files is to use the Pandas library. This provides a high-level structure to organize, manipulate, clean, and visualize tabular data. You can install pandas with the following command:

pip install pandas

Reading a .tsv file#

There are many ways to read a .tsv file in Pandas. One option is the following:

import pandas as pd
pd.read_csv('./ds001/participants.tsv', delimiter='\t')

Note that this function will default to using , as a delimiter, so we explicitly give it the tab character.

Writing a .tsv file#

You can write to a .tsv file using the to_csv method of a pandas DataFrame:

import pandas as pd
df = pd.read_csv('./ds001/participants.tsv', delimiter='\t')

# Add an extra column for demonstration
df['subject_id'] = range(len(df))

# Show contents of the dataframe
          participant_id sex  age  subject_id
    0         sub-01   F   26           0
    1         sub-02   M   24           1
    2         sub-03   F   27           2
    3         sub-04   F   20           3
    4         sub-05   M   22           4

# Save as a .tsv file
df.to_csv('my_new_file.tsv', sep='\t')


  • Create a file with the following columns (at least, for other values see paragraph the BIDS spec)

    • participant_id

    • age

    • sex

  • Save as tab separated .txt and change extension to .tsv


Reading and writing tab separated files comes natively in R, no need for extra packages.

Reading a .tsv file:#

In this example, we assume the .tsv includes column names (headers), and explicitly set column separator (delimiter) to tab (‘\t’)

data = read.table('myFile.tsv', header=TRUE, sep='\t')

Writing a .tsv file:#

When writing files, column and row names are always saved, we remove row names, and quotes from the outpur explicitly by setting them to FALSE.

data =
  participant_id = c('sub-01', 'sub-02'),
  age = c(20,30),
  sex = c('m','f'))

write.table(data, file='myData.tsv',sep='\t',
  row.names = FALSE, quote = FALSE)