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Sample data
Six raw datasets to learn BIDS Manager on, each with a guide that converts it step by step. The course uses the multimodal sample; the others show one kind of data in depth.
Which sample for what
| Sample | What it holds | Used in | Download |
|---|---|---|---|
| Multimodal | One participant: MRI, MR spectroscopy, PET, EEG, MEG and eye tracking | The course, Multimodal guide, Eye tracking | 126 MB |
| MRI | Two participants on a Siemens scanner, one of them in two sessions | MRI guide | 346 MB |
| MRI, advanced | Two participants, more sequences, spectroscopy and scanner-made maps | Advanced MRI guide | 4.7 GB |
| PET | One FDG phantom scan with blood curves and a dose sheet, and one ECAT scan | PET guide | 9 MB |
| EEG | Two participants, fourteen motor-imagery runs each | EEG guide | 36 MB |
| MEG | Two participants, three recording days, with empty-room recordings | MEG guide | 474 MB |
Each sample is a zip. Unpack it anywhere you like, outside the folder you will create the BIDS dataset in: BIDS Manager reads the raw data and never writes into it.
Double-click the zip in the Finder. Safari may unpack it for you after the download.
Right-click (Control-click on a Mac) the zip, choose Extract All..., and pick a folder. Do not work inside the zip: Windows shows its contents like a folder, but BIDS Manager cannot read from it.
Run unzip <file>.zip in a terminal, or use your file manager's Extract Here.
Multimodal
The course's dataset: one participant's folder with recordings from six kinds of acquisition, so one scan produces a genuinely multimodal scan table.
Download the multimodal sample
raw/sub-001/
├── mri/ Siemens Prisma DICOM: T1w, FLAIR, a resting BOLD run with its
│ single-band reference and physiological log, a fieldmap,
│ a diffusion run and its reversed-phase reference, scouts
├── mrs/ single-voxel spectroscopy and its water reference
├── pet/ GE Advance DICOM, one FDG scan of a phantom
├── eeg/ two EDF recordings: rest and motor imagery
├── meg/ one Elekta Neuromag recording, rest, 30 s
└── eyetracking/ one binocular EyeLink recording
participants.csv the participant's demographics
dose_sheet.csv the PET fields no scanner records
| Size | 126 MB as a zip, 318 MB unpacked |
| Scan | 19 rows, 15 to convert |
| Shows | Reconciling one participant across sources, fieldmaps linked to the runs they correct, physiological recordings split by signal, spectroscopy, PET metadata from a dose sheet, eye tracking |
| Note | A composite: real recordings from different acquisitions in one participant's folder. The PET scan is of a phantom. The 4-D MRI series and the MEG recording are shortened. |
| Sources and licences | MRI and spectroscopy: the neuroimaging unit of the University of Oldenburg; the participant consented to their face being shown. PET: OpenNeuro PET brain phantoms (10.70883/RGN3768), CC BY 4.0. EEG: PhysioNet EEG Motor Movement/Imagery Database, ODC-By 1.0. Eye tracking: the eyelinkio test recording, BSD 3-Clause. The licence files are in the zip. |
MRI
A small Siemens dataset in three folders, one per visit. Two of the visits are the same participant, so the scan proposes two subjects, the first with two sessions.
| Size | 346 MB as a zip, 603 MB unpacked |
| Holds | T1w, T2w, BOLD with its physiological log, diffusion, fieldmaps |
| Shows | Subjects and sessions found from the DICOM headers, physiological recordings, fieldmap links |
| Source | The neuroimaging unit of the University of Oldenburg |
MRI, advanced
A larger Siemens dataset with the sequences that need care: several anatomical contrasts, a BOLD run with its single-band reference, diffusion with the maps the scanner computes from it, fieldmap pairs, spectroscopy, and physiological logs.
Download the advanced MRI sample
| Size | 4.7 GB as a zip, 8.0 GB unpacked. Most of it is one raw spectroscopy series. |
| Holds | T1w, T2w, T2starw, FLAIR, BOLD and SBRef, diffusion and its scanner-made maps, fieldmaps, spectroscopy, physiological logs |
| Shows | What the scan leaves out on its own and why, scanner-made diffusion maps, spectroscopy, the parameter classifier |
| Source | The neuroimaging unit of the University of Oldenburg. One participant consented to their face being shown, and the guide shows only that participant's anatomy. |
PET
One FDG scan of a brain phantom on a GE Advance, with the two blood curves drawn during it and the operator's dose record; and, in extra_ecat/, one scan in the ECAT format from a Siemens HRRT.
| Size | 9 MB as a zip, 29 MB unpacked |
| Holds | 35 DICOM files, two PMOD blood curves (.bld), dose_sheet.csv and dose.json; one ECAT file with its own dose sheet |
| Shows | The PET fields no scanner records and how to supply them, blood data, the ECAT format |
| Sources and licences | Images: OpenNeuro PET brain phantoms (10.70883/RGN3768), CC BY 4.0. Blood curves: the PET2BIDS test data, MIT. |
EEG
Two participants from the PhysioNet EEG Motor Movement/Imagery Database, fourteen EDF runs each: baselines with eyes open and closed, and motor execution and imagery tasks.
| Size | 36 MB as a zip, 67 MB unpacked |
| Holds | S001/ and S002/, each with S00xR01.edf to S00xR14.edf and their event files |
| Shows | File names that do not say the task (R01, R02), and naming the tasks yourself in the table; events, montages and the line frequency |
| Source and licence | PhysioNet, ODC-By 1.0 |
MEG
Elekta Neuromag (MEGIN) recordings of two participants: a driving task in several runs, rest, and empty-room recordings before and after. Every recording is cropped to its first 25 seconds; channels, sampling rate, dates and file names are as recorded.
| Size | 474 MB as a zip, 853 MB unpacked |
| Holds | 23 recordings in three dated folders: sub_us04rt22/220209/, sub_us04rt22/220215/, sub_ye07us06/220221/ |
| Shows | Sessions from the recording dates, empty-room recordings, runs and tasks from the file names |
| Source | The BIDS Manager MEG sample |
Your own data
The guides work the same way on your own data. Keep a copy of the raw folder you scan: BIDS Manager does not change it, but the copy is what you will go back to if a conversion needs repeating.