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A set of BIDS compatible datasets with empty raw data files that can be used for writing lightweight software tests.

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bids-examples

This repository contains a set of BIDS-compatible datasets with empty raw data files. These datasets can be useful to:

  1. write lightweight software tests
  2. serve as an example on how a BIDS dataset can be structured

ALL RAW DATA FILES IN THIS REPOSITORY ARE EMPTY!

However for some of the data, the headers containing the metadata are still intact. (For example the NIfTI headers for .nii files, the BrainVision data headers for .vhdr files, or the OME-XML headers for .ome.tif files.)

Headers are intact for the following datasets:

  • synthetic
  • Most EEG or iEEG data in BrainVision format (e.g., eeg_matchingpennies)

Validating BIDS examples

The next three sections mention a few details on how the bids-examples can be validated using bids-validator.

For general information on the bids-validator, including installation and usage, see the bids-validator README file.

Validating individual examples

Since all raw data files in this repository are empty, the bids-validator must to be configured to not report empty data files as errors. (See more on bids-validator configuration in the bids-validator README.)

Just run the validator as follows (using the eeg_matchingpennies dataset as an example, and assuming you are in a command line at the root of the bids-examples repository):

bids-validator eeg_matchingpennies --config.ignore=99

The --config.ignore=99 "flag" tells the bids-validator to ignore empty data files rather than to report the "empty file" error .

For datasets that contain NIfTI .nii files, you also need to add the ignoreNiftiHeaders flag to the bids-validator call, to suppress the issue that NIfTI headers are not found.

For example:

bids-validator ds003 --config.ignore=99 --ignoreNiftiHeaders

Validating all examples

If you want to validate all examples in one go, you can use the run_tests.sh script that is provided in this repository. This script makes use of the bidsconfig.json configuration file for the bids-validator, and appropriately handles some special case examples (see Validator Exceptions).

Simply run bash run_tests.sh in a command line from the root of the bids-examples repository.

Validator exceptions

Some datasets may include a custom .bids-validator-config.json to ignore errors generated from idiosyncracies of the datasets as they existed on creation.

name errors ignored
genetics_ukbb SliceTiming values for tasks is larger than given TR, EchoTime1 and EchoTime2 are not provided for any of the phasediff files.

Other datasets may include a .SKIP_VALIDATION file, to skip the validation with the continuous integration service. This is useful for datasets that cannot pass at the moment due to lack of coverage in the bids-validator.

Note however, that the .SKIP_VALIDATION file only impacts the continuous integration service, or validation when run with the run_tests.sh script (see Validating all examples). This file does not have any effect when running bids-validator from custom scripts, the web-based validator, docker, or from the command line.

name why skipped
ds000001-fmriprep lack of coverage for "derivatives" in bids-validator

Contributing

We are happy to receive contributions in the form of:

  • updates to existing examples, or the dataset index
  • new examples
    • only if they cover aspects that are currently not covered by existing examples
    • only if a maintainer can be found for this dataset
  • suggestions on how to improve the bids-examples repository

For more information, please see our CONTRIBUTING.md file or open a new GitHub Issue and ask us directly.

Dataset index

Below you find several tables with information about the datasets available in bids-examples (in alphabetical order).

EEG datasets

name maintained by description link to full data
eeg_matchingpennies @sappelhoff Offline data of BCI experiment decoding left vs. right hand movement. BrainVision data format (.eeg, .vhdr, .vmrk) https://doi.org/10.17605/OSF.IO/CJ2DR
eeg_rishikesh @arnodelorme Mind wandering experiment. EEG data in Biosemi (.bdf) format https://openneuro.org/datasets/ds001787
eeg_face13 @andesha Deconstructing the early visual electrocortical response to face and house stimuli. EDF format
eeg_ds003654s_hed @VisLab Shows usage of Hierarchical Event Descriptor (HED) in events files https://openneuro.org/datasets/ds003645
eeg_ds003654s_hed_inheritance @VisLab HED annotation with multiple inherited sidecars https://openneuro.org/datasets/ds003645
eeg_ds003654s_hed_longform @VisLab HED annotation using tags in long form. https://openneuro.org/datasets/ds003645
eeg_ds003654s_hed_library @VisLab HED annotation using HED library vocabularies (schema). https://openneuro.org/datasets/ds003645
eeg_cbm @cpernet Rest EEG. European Data Format (.edf)

iEEG datasets

name maintained by description
ieeg_filtered_speech @choldgraf recordings of three seizures
ieeg_motorMiller2007 @dorahermes Cue-based hand & tongue movement data
ieeg_visual @dorahermes Stimulus dependence of gamma oscillations in human visual cortex

MRI datasets

name maintained by description func anat other link to link to full data
7t_trt bold, physio T1w, quantitative T1 maps fmap https://bit.ly/2H0Z6Qt
ds001 single task, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000001/versions/00006
ds002 multiple tasks, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000002/versions/00002
ds003 single task, single run bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000003/versions/00001
ds005 single task, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000005/versions/00001
ds006 single task, multiple sessions, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000006/versions/00001
ds007 single task, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000007/versions/00001
ds008 multiple tasks, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000008/versions/00001
ds009 multiple tasks, multiple runs bold, events T1w, in-plane T2 https://openneuro.org/datasets/ds000009/versions/00002
ds011 multiple tasks, multiple runs bold T1w https://openneuro.org/datasets/ds000011/versions/00001
ds051 multiple tasks, multiple runs bold T1w, in-plane T2 https://openneuro.org/datasets/ds000051/versions/00001
ds052 multiple tasks, multiple runs bold T1w, in-plane T2 https://openneuro.org/datasets/ds000052/versions/00001
ds101 single task, multiple runs bold T1w https://openneuro.org/datasets/ds000101/versions/00004
ds102 single task, multiple runs bold T1w https://openneuro.org/datasets/ds000102/versions/00001
ds105 single task, multiple runs bold T1w https://openneuro.org/datasets/ds000105/versions/00001
ds107 single task, multiple runs bold T1w https://openneuro.org/datasets/ds000107/versions/00001
ds108 single task, multiple runs bold T1w https://openneuro.org/datasets/ds000108/versions/00002
ds109 multiple tasks, multiple runs bold T1w https://openneuro.org/datasets/ds000109/versions/00001
ds110 single task, multiple runs bold T1w, in-plane T2 https://openneuro.org/datasets/ds000110/versions/00001
ds113b forrest gump watching, multiple sessions, multiple runs bold T1w, T2w angiography, dwi, fmap https://openneuro.org/datasets/ds000113/versions/1.3.0
ds114 multiple tasks, multiple runs bold T1w DWI https://openneuro.org/datasets/ds000114/versions/1.0.1
ds116 multiple tasks, multiple runs bold T1w, in-plane T2 https://openneuro.org/datasets/ds000116/versions/00003
ds210 multiple tasks, multiple runs bold T1w https://openneuro.org/datasets/ds000210/versions/00002
hcp_example_bids @robertoostenveld bold T1w https://bit.ly/2H0Z6Qt
synthetic @effigies A synthetic dataset bold T1w n/a
ds000001-fmriprep @effigies Common derivatives example bold T1w https://openneuro.org/datasets/ds000001/versions/1.0.0

ASL datasets

name maintained by description link to link to full data
asl001 @patsycle T1w, asl (GE, PCASL, 3D_SPIRAL), m0scan within timeseries https://osf.io/yru2q/
asl002 @patsycle T1w, asl (Philips, PCASL, 2D_EPI), m0scan as separate scan https://osf.io/yru2q/
asl003 @patsycle T1w, asl (Siemens, PASL, multiTI), M0scan as separate scan https://osf.io/yru2q/
asl004 @patsycle T1w, asl (Siemens, PCASL, multiPLD with pepolar), m0scan separate scans with pepolar appraoch https://osf.io/yru2q/
asl005 @patsycle T1w, asl (Siemens, PCASL, singleTI, 3D_GRASE), m0scan as separate scan https://osf.io/yru2q/

qMRI datasets

name maintained by description link to link to full data
qmri_mp2rage @Gilles86 MP2RAGE for T1 mapping https://osf.io/k4bs5/
qmri_mp2rageme @Gilles86 Multi-echo MP2RAGE https://osf.io/k4bs5/
qmri_mpm @ChristophePhillips Multi-parametric mapping for R1, R2star, MTsat and PD mapping https://osf.io/k4bs5/
qmri_mtsat @agahkarakuzu Example dataset for T1 and MTsat mapping. Includes a double-angle B1+ mapping example. https://osf.io/k4bs5/
qmri_qsm @agahkarakuzu Chimap using fast QSM not publicly availabe
qmri_sa2rage @agahkarakuzu Fast B1+ mapping using SA2RAGE not publicly availabe
qmri_vfa @agahkarakuzu Variable Flip Angle T1 mapping. Includes an Actual Flip Angle (AFI) B1+ mapping example. https://osf.io/k4bs5/
qmri_irt1 @agahkarakuzu Inversion Recovery T1 mapping not publicly availabe
qmri_mese @agahkarakuzu Multi-Echo Spin-Echo for T2 or Myelin Water Fraction (MWF) mapping. not publicly availabe
qmri_megre @agahkarakuzu Multi-Echo Gradient-Echo for T2star mapping. not publicly availabe
qmri_tb1tfl @agahkarakuzu B1+ mapping with TurboFLASH readout. not publicly availabe

PET datasets

name maintained by description link to full data
pet001 @mnoergaard T1w, PET, blood
pet002 @mnoergaard T1w, PET https://openneuro.org/datasets/ds001420/
pet003 @mnoergaard T1w, PET, blood
pet004 @mnoergaard PET, blood
pet005 @mnoergaard T1w, PET

Microscopy datasets

name maintained by description
micr_SEM @jcohenadad Example SEM dataset in PNG format with 1 sample imaged over 2 sessions
micr_SEMzarr @TheChymera Example SEM dataset in PNG and OME-ZARR format with 1 sample imaged over 2 sessions
micr_SPIM @jcohenadad Example SPIM dataset in OME-TIFF format with 2 samples from the same subject with 4 chunks each

fNIRS datasets

name maintained by description link to full data
fnirs_tapping @rob_luke Example fNIRS measurement with three conditions from five subjects https://doi.org/10.5281/zenodo.5529797
fnirs_automaticiy @robertoostenveld 24 subjects performing (non-)automatic finger tapping and foot stepping https://doi.org/10.34973/vesb-mh30

Multimodal datasets

name maintained by description mri meg eeg ieeg genetics link to full data
ds000117 @RikHenson A multi-subject, multi-modal human neuroimaging dataset of 19 subjects on a MEG visual task anat, dwi, func, fmap meg eeg https://openneuro.org/datasets/ds000117/
ds000246 @guiomar Auditory dataset used for Brainstorm’s general online tutorial anat meg https://openneuro.org/datasets/ds000246/versions/00001
ds000247 @guiomar Five minutes, eyes-open, resting-state MEG data from 5 subjects. This is a sample from The Open MEG Archive (OMEGA). anat meg https://openneuro.org/datasets/ds000247/versions/00001
ds000248 @agramfort MNE sample data: Data with visual and auditory stimuli anat meg https://openneuro.org/datasets/ds000248/versions/00001
eeg_ds000117 @robertoostenveld Multimodal (fMRI, MEG, EEG) stripped down to EEG with MRI anatomical scan and electrode coordinates. EEGLAB data format (.set, .fdt) anat eeg https://openneuro.org/datasets/ds000117/
eeg_rest_fmri @cpernet Resting state with simultaneous fMRI. BrainVision data format (.eeg, .vhdr, .vmrk) anat, dwi, func eeg
ieeg_epilepsy @ftadel multiple sessions, tutorial anat ieeg https://neuroimage.usc.edu/bst/getupdate.php?s=tutorial_epimap_bids
ieeg_epilepsy_ecog @ftadel multiple sessions, tutorial anat ieeg https://neuroimage.usc.edu/bst/getupdate.php?s=sample_ecog
ieeg_visual_multimodal @irisgroen anat, fmap, func ieeg
genetics_ukbb @cpernet multiple tasks, T1w, DTI, BOLD, genetic info anat, dwi, func, fmap genetics

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A set of BIDS compatible datasets with empty raw data files that can be used for writing lightweight software tests.

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