Benefits
By using this standard you will benefit in the following ways:
- It will be easy for another researcher to work on your data. To understand the organization of the files and their format you will only need to refer them to this document. This is especially important if you are running your own lab and anticipate more than one person working on the same data over time. By using BIDS you will save time trying to understand and reuse data acquired by a graduate student or postdoc that has already left the lab.
- There is a growing number of data analysis software packages that can understand data organized according to BIDS.
- Databases such as OpenNeuro.org, LORIS, COINS, XNAT, SciTran, and others will accept and export datasets organized according to BIDS. If you ever plan to share your data publicly (nowadays some journals require this) you can speed up the curation process by using BIDS.
- There are validation tools (also available online) that can check your dataset integrity and let you easily spot missing values.
Converters
MRI and PET converters
Name | Expected input | Language | Distribution | Comment | Updated |
---|---|---|---|---|---|
bidsify |
|
|
Tool to convert source MRI datasets to BIDS-compatible datasets. |
|
|
bidskit |
|
|
Utility functions for working with DICOM and BIDS neuroimaging data. |
|
|
BMAT |
|
|
|
||
Data2Bids |
|
|
Converts MRI files from extension supported by nibabel into NIfTI and convert them to BIDS |
|
|
Dcm2Bids |
|
|
converts DICOM files using dcm2niix into BIDS |
|
|
HeuDiConv |
|
|
A flexible DICOM converter for organizing brain imaging data into structured directory layouts |
|
|
OpenfMRI2BIDS |
|
|
|
Convert OpenfMRI dataset to BIDS. (Possibly deprecated due to openfmri.org moving to openneuro.org). |
|
ReproIn |
|
|
HeuDiConv-based turnkey solution: a setup for automatic generation of shareable, version-controlled BIDS datasets from MR scanners. |
|
|
XNAT2BIDS |
|
|
|
Simple xnat pipeline to convert DICOM scans to BIDS-compatible output (nii+json). |
|
Horos (Osirix) export plugin |
|
|
|
Horos plugin for BIDS output. |
|
BIDScoin |
|
|
BIDScoin converts your source-level neuroimaging data to BIDS |
|
|
BIDSme |
|
|
|
|
|
BrkRaw |
|
|
|
For a preclinical Bruker MRI scanner |
|
Clinica |
|
|
|
|
|
niix2bids |
|
|
|
Use this package as a command line to organize your Nifti dataset into BIDS. |
|
dac2bids |
|
|
|
Create a BIDS structure for a DICOM folder. |
|
Autobids |
|
|
|
Automated Dicom to BIDS and pipelines using compute canada. From the Center for Functional and Metabolic Mapping (CFMM) at Western’s Robarts Research Institute. |
|
PET2BIDS |
|
|
|
Helps you convert your PET data! raw PET scanner files (for example ecat, dicom) and additional side file like excel sheets. |
|
Explore ASL |
|
|
|
Convert DICOM and NIFTI data to the ASL-BIDS format. |
|
SAMRI |
|
|
|
Full stack Small Animal MRI data analysis package, including the `bru2bids` repositing pipeline, which can convert Bruker archives to the BIDS format. From the ETH and University of Zurich, with collaboration from MIT and Dartmouth College. |
|
BIDSconvertR |
|
|
|
The BIDSconvertR R package provides a user-friendly workflow with graphical user interfaces. It consists of the following steps: (i) convert DICOM data to NIfTI data using dcm2niix (ii) structure this data according to the BIDS specification (iii) provide the papayaWidget viewer for inspecting the images |
|
ezBIDS |
|
|
|
A web-based BIDS conversion tool with four unique features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options, including download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. |
|
mercure-dcm2bids |
|
|
A containerized app that can be used to perform BIDS conversion of DICOM studies sent directly to mercure from a scanner or PACS. mercure is an open-source DICOM orchestration platform that can integrate containerized apps into clinical workflows. It has a graphical user interface making it easy to setup and manage BIDS configurations for multiple protocols. The Dcm2Bids tool is used for conversion. |
|
EEG, MEG, iEEG converters
Name | Expected input | Language | Distribution | Comment | Updated |
---|---|---|---|---|---|
BIDSme |
|
|
|
|
|
MNE-BIDS |
|
|
MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python. |
|
|
EEGLAB |
|
|
|
See plugins |
|
FieldTrip - data2bids |
|
|
|
|
|
Biscuit |
|
|
|
GUI for easy MEG to BIDS conversion |
|
sovabids |
|
|
|
A Python package for the automatic conversion of EEG datasets to the BIDS standard, with a focus on making the most out of metadata. |
|
EEG2BIDS |
|
|
|
A tool for converting raw EEG and iEEG data into the BIDS standard data structure, prepared for LORIS (Longitudinal Online Research and Imaging System). |
|
'From BIDS' converters
Converters that take a BIDS dataset as input to convert it into something else. Not mentioned here are the many software that can import a BIDS data as data structure they are more familiar with.
Name | Expected input | Language | Distribution | Comment | Updated |
---|---|---|---|---|---|
BIDS2ISATab |
|
|
|
Extract ISA-Tab compatible metadata from BIDS |
|
BIDSto3col |
|
|
|
Reads BidsTSV and then creates 3 column event files, one per event type if a "trial_type" column is found. |
|
BIDS2NDA |
|
|
|
Extract NIHM Data Archive compatible metadata from BIDS |
|
bids2xar - for XNAT import |
|
|
|
Convert BIDS data set into XNAT XAR bundles |
|
BIDS2NIDM |
|
|
|
This program will convert a NIDM-Experiment RDF document to a BIDS dataset. |
|
AFNI BIDS-tools |
|
|
|
ARCHIVED - Scripts, tools, and documents on creating, parsing, and working with BIDS-structured data sets. |
|
Physiological data converters
Name | Expected input | Language | Distribution | Comment | Updated |
---|---|---|---|---|---|
BIDScoin |
|
|
BIDScoin converts your source-level neuroimaging data to BIDS |
|
|
phys2bids |
|
|
Python3 library to format physiological files in BIDS. |
|
|
bidsphysio |
|
|
Converts physio data (CMRR, AcqKnowledge, Siemens PMU) to BIDS physiological recording |
|
Miscellaneous
Not exactly BIDS converters but are common tools that can used by other BIDS converters.
Name | Expected input | Language | Distribution | Comment | Updated |
---|---|---|---|---|---|
convert-eprime |
|
|
|
Python functions to convert E-Prime files to csvs. Not currently being developed. |
|
DCM2NIIx |
|
|
|
dcm2nii DICOM to NIfTI converter |
|
DICM2NII |
|
|
|
dcm2nii DICOM to NIfTI converter |
|
sim2bids |
|
|
|
GUI to easily convert simulation results to BIDS format, according to BEP 34. |
|
Software currently supporting BIDS:
- BIDS Apps (a growing set of portable containerized data processing pipelines that understand BIDS datasets)
A description of how to build containerized apps supporting BIDS inputs can be found in the paper published in PLOS Computational Biology.
Other Tools
- babs:
BIDS App Bootstrap (BABS) is a reproducible, generalizable, and scalable Python package for BIDS App analysis of large datasets. It uses DataLad and adopts FAIRly big framework.
- BIDSHandler:
Python module allowing complete manipulation of BIDS data
- bids-cfood:
a module to handle BIDS dataset for the caosDB data crawler
- bids2cite:
package to interactively update
dataset_description.json
and generate citation files (for exampledatacite.yml
) for BIDS datasets. - bids-matlab:
MATLAB/Octave tools to interact with datasets conforming to the BIDS format
- BIDS-pydantic:
Pulls a specified version of the BIDS schema and creates corresponding Pydantic models, which will provide BIDS data validation using Python type annotations. See also BIDS-pydantic-models.
- bidser:
Working with Brain Imaging Data Structure in R
- bids stats model:
Validate BIDS statistical model. To learn more the BIDS stats model website
- Hierarchical Event Descriptors (HED) online tools:
Online tools for annotation, validation, summary, and assembly of event file contents and annotations.
- Hierarchical Event Descriptors (HED) python tools:
HED libraries supporting schema development as well as annotation, validation, and analysis.
- Brainstorm:
MEG/EEG analysis package
- clpipe:
streamlined processing pipeline for MRI data centered around BIDS
- cuBIDS:
a Python package designed to facilitate reproducible curation of neuroimaging BIDS datasets
- File mapper:
An easy tool to copy/move/symlink files from one directory to the other! Can be used to “convert” dataset to be BIDS compliant.
- GUI dataset description generator:
GUI form that generates
dataset_description.json
- Lead-DBS:
A toolbox facilitating Deep Brain Stimulation electrode reconstructions and computer simulations supports BIDS conversion and ingestion of BIDS datasets.
- mne-bids:
collection of tools for converting magnetoencephalography (MEG) data into BIDS format, as well as some helper functions for creating the folders and metadata needed for a BIDS dataset.
- neurobagel annotate:
This tool allows you to create a machine readable data dictionary in .json format for a tabular phenotypic file in .tsv format (“Data table”).
- neurobagel query:
Neurobagel’s query tool is a web interface for searching across a BIDS datasets based on various subject clinical-demographic and imaging parameters.
- nipopy:
Lightweight neuroimaging workflow manager to help with DICOM to BIDS conversion and running BIDS apps.
- OpenNeuro:
A free and open platform for validating and sharing BIDS-compliant data.
- PRFmodel:
a set of tools to fit population receptive field models to BIDS datasets
- PyBIDS:
Python package to quickly parse / search the components of a BIDS dataset. It also contains functionality for running analyses on your data.
- rbids:
aims to make BIDS datasets more easily accessible for packages written in R
- spm_2_bids:
a tool convert SPM preprocessed output to BIDS derivatives (trying to follow BEP12)
- psychopy-bids:
A psychopy plugin to help easily output a BIDS dataset, including
events.tsv
andbeh.tsv
files when running experiments with psychopy.