cMadan / Openmorph
Curated list of open-access databases with human structural MRI data
Stars: ✭ 108
Labels
Projects that are alternatives of or similar to Openmorph
Ml Ai Experiments
All my experiments with AI and ML
Stars: ✭ 107 (-0.93%)
Mutual labels: jupyter-notebook
Py Wsi
Python package for dealing with whole slide images (.svs) for machine learning, particularly for fast prototyping. Includes patch sampling and storing using OpenSlide. Patches may be stored in LMDB, HDF5 files, or to disk. It is highly recommended to fork and download this repository so that personal customisations can be made for your work.
Stars: ✭ 107 (-0.93%)
Mutual labels: jupyter-notebook
Ganotebooks
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
Stars: ✭ 1,446 (+1238.89%)
Mutual labels: jupyter-notebook
Tensorflow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Stars: ✭ 41,480 (+38307.41%)
Mutual labels: jupyter-notebook
Robustness applications
Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
Isl Python
Porting the R code in ISL to python. Labs and exercises
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
Getting Started With Google Bert
Build and train state-of-the-art natural language processing models using BERT
Stars: ✭ 107 (-0.93%)
Mutual labels: jupyter-notebook
Ml Demos
Python code examples for the feedly Machine Learning blog (https://blog.feedly.com/category/all/Machine-Learning/)
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
Pyldavis
Python library for interactive topic model visualization. Port of the R LDAvis package.
Stars: ✭ 1,550 (+1335.19%)
Mutual labels: jupyter-notebook
Kalman And Bayesian Filters In Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Stars: ✭ 11,233 (+10300.93%)
Mutual labels: jupyter-notebook
Learning Vis Tools
Learning Vis Tools: Tutorial materials for Data Visualization course at HKUST
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
Facemaskdetection
开源人脸口罩检测模型和数据 Detect faces and determine whether people are wearing mask.
Stars: ✭ 1,677 (+1452.78%)
Mutual labels: jupyter-notebook
Aa228 Notebook
IJulia notebooks for AA228/CS238 Decision Making Under Uncertainty course at Stanford University
Stars: ✭ 107 (-0.93%)
Mutual labels: jupyter-notebook
Python Machine Learning
Tous les codes utilisés dans la série YouTube Python Spécial Machine Learning !
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
Psgan
Periodic Spatial Generative Adversarial Networks
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
Ultra96 Pynq
Board files to build Ultra 96 PYNQ image
Stars: ✭ 108 (+0%)
Mutual labels: jupyter-notebook
openMorph
Curated list of open-access databases with human structural MRI data.
Feel free to make additions/updates via pull requests!
For an overview of benefits and considerations related to using open-access data for brain morphology research, see Madan (2017) Front Hum Neurosci [10.3389/fnhum.2017.00405].
ADNI
- Alzheimer’s Disease Neuroimaging Initiative
-
http://adni.loni.usc.edu
- older adults; dementia; longitudinal
- T1, T2, DTI, ASL, rs-fMRI
- Mueller et al. (2005) Alzheimers Dement [10.1016/j.jalz.2005.06.003]
- Jack et al. (2008) J Magn Reson Imaging [10.1002/jmri.21049]
ABIDE
- Autism Brain Imaging Data Exchange
-
http://fcon_1000.projects.nitrc.org/indi/abide/
- N=1112
- developmental; autism
- T1, rs-fMRI
- Di Martino et al. (2014) Mol Psychiatry [10.1038/mp.2013.78]
ADHD-200
-
http://fcon_1000.projects.nitrc.org/indi/adhd200/
- N=973
- developmental; ADHD
- T1, rs-fMRI
- ADHD-200 Consortium (2012) Front Syst Neurosci [10.3389/fnsys.2012.00062]
- Bellec et al. (2017) NeuroImage [10.1016/j.neuroimage.2016.06.034]
Age-ility
-
http://www.nitrc.org/projects/age-ility/
- N=131
- T1, dMRI, rs-fMRI, EEG
- Karayanidis et al. (2016) [10.1016/j.neuroimage.2015.04.047]
AIBL
- Australian Imaging, Biomarkers and Lifestyle
-
http://www.aibl.csiro.au
- N=1100
- older adults; dementia; longitudinal
- T1, PD, T2, DWI, FLAIR, SWI
- Ellis et al. (2009) Int Psychogeriatr [10.1017/S1041610209009405]
BRAINS
- Brain Images of Normal Subjects
-
http://www.brainsimagebank.ac.uk
- lifespan
- Job et al. (2017) NeuroImage [10.1016/j.neuroimage.2016.01.027]
CamCAN
- Cambridge Centre for Ageing and Neuroscience
-
https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/
- N=653
- lifespan; behavior
- T1, T2, DTI, rs-fMRI, task fMRI
- Shafto et al. (2014) BMC Neurol [10.1186/s12883-014-0204-1]
- Taylor et al. (2017) NeuroImage [10.1016/j.neuroimage.2015.09.018]
CMI-HBN
- Child Mind Institute Healthy Brain Network
-
http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/
- N=664 (goal of 10,000)
- developmental (aged 5-21)
- T1, T2, DKI, rs-fMRI, EEG
- Alexander et al. (2017) bioRxiv [10.1101/149369]
COBRE
- Center for Biomedical Research Excellence
- http://cobre.mrn.org
-
http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html
- N=72 patients, 75 healthy controls
- schizophrenia; lifespan
- T1; rs-fMRI
CoRR
- Consortium for Reliability and Reproducibility
-
http://fcon_1000.projects.nitrc.org/indi/CoRR/html/
- N=1629
- young adults; test-retest
- T1, rs-fMRI, some DTI
- Zuo et al. (2014) Sci Data [10.1038/sdata.2014.49]
DLBS
- Dallas Lifespan Brain Study
-
http://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html
- N=315
- lifespan; behavior
- T1, PET
fBIRN
- Function Biomedical Informatics Research Network
-
http://www.birncommunity.org
- schizophrenia; test-retest; traveling subjects
- Keator et al. (2016) NeuroImage [10.1016/j.neuroimage.2015.09.003]
GSP
- Brain Genome Superstruct Project
-
http://neuroinformatics.harvard.edu/gsp/
- N=1570
- young adults; test-retest; behavior
- T1, rs-fMRI
- Holmes et al. (2015) Sci Data [10.1038/sdata.2015.31]
HCP
- Human Connectome Project
-
http://humanconnectome.org
- N=1200
- young adults; behavior
- T1, T2, rs-fMRI, task fMRI, Q-Ball
- Van Essen et al. (2013) NeuroImage [10.1016/j.neuroimage.2013.05.041]
- Glasser et al. (2016) Nat Neurosci [10.1038/nn.4361]
IXI
- Information eXtraction from Images
-
http://brain-development.org/ixi-dataset/
- lifespan
- T1, T2, PD, MRA, DTI
Kirby 21
-
http://www.nitrc.org/projects/multimodal
- N=21
- test-retest
- T1, T1, DTI, FLAIR, ASL, VASO, rs-fMRI
- Landman et al. (2011) [10.1016/j.neuroimage.2010.11.047]
Maastrict 7T 700μm
-
https://zenodo.org/record/1117858
- T1w, PDw, T2*w images, n=5, age range 24-30, no medical condition
- MP2RAGE (T1, UNI, INV1, INV2) and T2*w (Multi-echo 3D GRE, TE1/TE2/TE3/TE4 = 2.53/7.03/12.55/20.35 ms) images, n=4, age range 24-58, no medical condition
- Gulban et al. (2018) 10.1371/journal.pone.0198335
MASSIVE
- Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation
-
http://massive-data.org/index.html
- N=1
- multiple dMRI aquisitions
- T1, T2, DWI, FLAIR
- Froeling et al. (2016) [10.1002/mrm.26259]
Mindboggle-101
- Mindboggle-101 data consist of three data sets:
- N=101 individually labeled human brain surfaces and volumes
- templates (unlabeled images combining the individual brains, used for registration)
- atlases (anatomical labels combining the individual brains, used for labeling)
- http://www.mindboggle.info/data.html
- Data: https://osf.io/nhtur/; Labels: http://mindboggle.info/labels.html
- Klein & Tourville (2012) [10.3389/fnins.2012.00171]
MIRIAD
- Minimal Interval Resonance Imaging in Alzheimer's Disease
-
https://www.ucl.ac.uk/drc/research/methods/miriad-scan-database
- N=69
- longitudinal
- T1
- Malone et al. (2013) [10.1016/j.neuroimage.2012.12.044]
MPI-LMBB
- MPI-Leipzig Mind-Brain-Body
- https://openfmri.org/dataset/ds000221/
-
https://www.nitrc.org/projects/mpilmbb/
- N=320
- behavior
- T1, T2, DWI, rs-fRMI
- Mendes et al. (2017) [10.1101/164764]
MSC
- Midnight Scan Club
-
https://openfmri.org/dataset/ds000224/
- N=10
- T1, T2, MRA, MRV, rs-fMRI, task fMRI
- Gordon et al. (2017) Neuron [10.1016/j.neuron.2017.07.011]
NACC
- National Alzheimer's Coordinating Center
-
https://www.alz.washington.edu
- lifespan; dementia; longitudinal
- T1, T2, DTI, FLAIR
- Morris et al. (2006) [10.1097/01.wad.0000213865.09806.92]
NCANDA
- National Consortium on Alcohol and Neurodevelopment in Adolescence
- http://ncanda.org
-
https://www.niaaa.nih.gov/research/major-initiatives/national-consortium-alcohol-and-neurodevelopment-adolescence#Data
- N ~ 800
- adolescence, alcohol, development, longitudinal
- T1, DTI, rs-fMRI
- Brown et al. (2015) [10.15288/jsad.2015.76.895]
NKIRS
- Nathan Kline Institute - Rockland Sample
-
http://fcon_1000.projects.nitrc.org/indi/enhanced/
- N=683 (and counting)
- developmental; lifespan; behavior
- T1, DTI, rs-fMRI
- Nooner et al. (2012) [10.3389/fnins.2012.00152]
OASIS cross-sectional
- Open Access Series of Imaging Studies
-
http://www.oasis-brains.org
- N=416
- lifespan; dementia; test-retest
- T1 only
- Marcus et al. (2007) J Cogn Neurosci [10.1162/jocn.2007.19.9.1498]
OASIS longitudinal
- Open Access Series of Imaging Studies
-
http://www.oasis-brains.org
- N=150
- older adults; longitudinal; dementia
- T1 only
- Marcus et al. (2010) J Cogn Neurosci [10.1162/jocn.2009.21407]
OpenNeuro (former OpenfMRI)
-
https://www.openneuro.org
- 75 datasets (and counting)
- 2600 subjects (and counting)
- Please see individual dataset pages for details
- Poldrack & Gorgolewski (2017) NeuroImage [10.1016/j.neuroimage.2015.05.073]
- Poldrack et al. (2013) Front Neuroinform [10.3389/fninf.2013.00012]
PING
- Pediatric Imaging, Neurocognition, and Genetics
-
http://pingstudy.ucsd.edu
- N=1493
- developmental; behavior
- T1, T2, DTI, and rs-fMRI
- Jernigan et al. (2016) NeuroImage [10.1016/j.neuroimage.2015.04.057]
PNC
- Philadelphia Neurodevelopmental Cohort
-
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v2.p2
- N=1445
- developmental
- T1, DTI, ASL, task fMRI
- Satterthwaite et al. (2014) NeuroImage [10.1016/j.neuroimage.2013.07.064]
- Satterthwaite et al. (2016) NeuroImage [10.1016/j.neuroimage.2015.03.056]
PTBP
- Pediatric Template of Brain Perfusion
-
https://figshare.com/articles/PTBP_Nifti/1190933
- N=210
- T1, dMRI, rs-fMRI, ASL
- Avants et al. (2015) [10.1038/sdata.2015.3]
SALD
- Southwest University Adult Lifespan Dataset
-
http://fcon_1000.projects.nitrc.org/indi/retro/sald.html
- N=494
- lifespan
- T1, rs-fMRI, task fMRI (subset)
- Wei et al. (2017) bioRxiv [10.1101/177279]
SchizConnect
-
http://schizconnect.org
- schizophrenia
- T1, T2, DTI, rs-fMRI, task fMRI
- Wang et al. (2016) NeuroImage [10.1016/j.neuroimage.2015.06.065]
studyforrest
-
http://studyforrest.org
- N=20
- T1, T2, dMRI, SWI, angiography
- up to 10+ hours of 7T and 3T fMRI per subject (2h hollywood movie [audio and/or audio-visual], localizers, retinotopic mapping, with simult. cardiac/resp, partially with simult. eyetracking)
- extensive movie stimulus annotations
- musical education, visual acuity, visual sensitivity
- Hanke et al. (2014) [10.1038/sdata.2014.3]
T1-250μm
-
http://hiresmri.ovgu.de
- N=1
- T1 (250 μm resolution)
- Lüsebrink et al. (2017) Sci Data [10.1038/sdata.2017.32]
TICV and Posterior Fossa Dataset
-
https://www.nitrc.org/frs/download.php/9386/TICV_BC2atlases_20160823.zip
- N = 20
- T1w Scans with manual annotations of Intracranial Vault and Posterior Fossa + 133 more brain regions manually annotated
- Orig MRI files distributed via neuromorphometrics.org
- Huo et al (2017) [10.1002/hbm.23432]
Test-Retest Reliability of Brain Volume Measurements
- https://openfmri.org/dataset/ds000239/
-
https://figshare.com/collections/Test_Retest_Reliability_of_Brain_Volume_Measurements/929651
- N=3, each scanned 40 times within 31 days
- T1
- MacLaren et al. (2014) [10.1038/sdata.2014.37]
UK Biobank
-
http://imaging.ukbiobank.ac.uk
- N>10,000 (target=100,000)
- lifespan; behavior; health
- Miller et al. (2016) [10.1038/nn.4393]
- Alfaro-Almagro et al. (2016) [10.1101/130385]
Download the Data
CoRR Download
The download_corr.ipynb jupyter notebook contains a script to webscrape the CoRR website and download the appropriate datasets.
Conda Environment
The conda_env.yml file contains the environment used to run the jupyter notebook. To create a conda environment from that file, execute
conda env create -f conda_env.yml
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].