CONP Portal | Dataset


BigBrain dataset
Creators: BigBrain project
Contact: info@bigbrainproject.org
Licenses: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
Version: 1.0
Modalities: histology
Formats: MINC NIfTI
Size: 112.5 GB
No of Files: 316
No of Subjects: 1
Primary Publication: BigBrain: an ultrahigh-resolution 3D human brain model. Science.
Project Landing Page: https://bigbrainproject.org
Metadata file: DATS.json
Is About: Homo sapiens, adult
Description:
The BigBrain dataset is a digitized reconstruction of high-resolution histological sections of the brain of a 65 years old man with no history of neurological or psychiatric diseases. This dataset is the result of a collaborative effort between the teams of Dr. Katrin Amunts and Dr. Karl Zilles (Forschungszentrum Jülich) and Dr. Alan Evans (Montreal Neurological Institute). For more information please visit the BigBrain Project website [https://bigbrainproject.org]. This dataset contains the 3D 40 microns blocks of the BigBrain as well as the 3D histological volumes at different isotropic resolutions (100 to 400 microns) in MNI space and histological space.

Dataset README information

README.md

BigBrain dataset

BigBrain

The BigBrain is the brain of a 65 years old man with no neurological or psychiatric
diseases in clinical records at time of death. The brain was embedded in parafin and
sectioned in 7404 coronal histological sections (20 microns), stained for cell bodies.
The BigBrain is the digitized reconstruction of the hi-res histological sections
(20 microns isotropic).

Dataset content

This dataset contains the 3D blocks and 3D volumes of the BigBrain release 2015
published in the BigBrain Project website.

The 40 microns blocks divide the full BigBrain volume in 5x5x5 sub-volumes,
with small overlap. The legend volume indicates in whic block a voxel at
(x, y, z) lies in.

The 3D histological volumes (Merker stain) are also available at different
isotropic resolutions (100, 200, 300, 400 microns) in MNI space and
histological space, with and without optical balancing. The MNI ICBM152
2009b symmetric model used for the stereotaxic registration of the BigBrain
can be found at http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009.

All files are available in MINC and NIfTI formats.

Reference and more information

The BigBrain dataset is the result of a collaborative effort between the
teams of Dr. Katrin Amunts and Dr. Karl Zilles (Forschungszentrum Jülich)
and Dr. Alan Evans (Montreal Neurological Institute).

Amunts, K. et al.: "BigBrain: An Ultrahigh-Resolution 3D Human
Brain Model", Science (2013) 340 no. 6139 1472-1475, June 2013
https://www.sciencemag.org/content/340/6139/1472.abstract

For more information please visit the BigBrain Project website.

Download Using DataLad

CircleCI status

The following instructions require a basic understanding of UNIX/LINUX command lines. A subset of open datasets on the Portal are also available through a browser-based download button. The instructions below regard dataset download with the use of DataLad. To install DataLad on your system, please refer to the install section of the DataLad Handbook .

Note: For maximum compatibility with conp-dataset, the CONP recommends versions 3.12+ of Python, 10.20241202+ of git-annex, and 1.1.4+ of datalad.

1) Initiate the CONP dataset

Run the following command in the directory where you want the CONP dataset (conp-dataset) to be installed:

2) Install the BigBrain dataset

To install the BigBrain dataset, run the following commands to move into the "projects" subdirectory under the "conp-dataset" directory (created in the previous step) and run datalad install:

3) Download data from the BigBrain dataset

Now that the dataset has been installed, go into the BigBrain dataset directory.

The files visible after installing the dataset but before downloading (in the next step) are symbolic links and need to be downloaded manually using the datalad get command:

If you run datalad get * command, all the files available in the dataset directory will be downloaded.


For more information on how DataLad works, please visit the DataLad Handbook documentation.