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Title: Sharing data in the cloud      
dateReleased:
10-24-2016
privacy:
not applicable
aggregation:
instance of dataset
dateCreated:
10-19-2016
refinement:
curated
ID:
doi:10.5524/100233
creators:
O’Connor, David
Clark, Daniel J
Milham, Michael P
,
availability:
available
types:
sequence
description:
Cloud computing resources, such as Amazon Web Services (AWS), provide pay-as-you-go access to high-performance computer resources and dependable data storage solutions for performing large scale analyses of neuroimaging data . These are particularly attractive for researchers at small universities and in developing countries who lack the wherewithal to maintain their own high performance computing systems. The objective of this project is to upload data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Datasharing Initiatives (INDI) grass-roots data sharing initiatives into a Public S3 Bucket that has been generously provided by AWS. The entirety of the CoRR, ABIDE, ACPI, and ADHD-200 data collections and ENKIRS data for 427 individuals were uploaded during the OHBM Hackathon event. The data are available as individual files to make it easily index able by database infrastructures such as COINs LORIS and others. Additionally, this makes it easy for the users to download just the data that they want. The data in the bucket can be browsed and downloaded using a GUI based S3 file transfer software such as Cyberduck, or using the Boto python library. The data is structured as follows: bucketname/data/Projects/ProjectName/DataType. For example you can access raw data from the ENKI-RS, by specifying the following path in CyberDuck: https://s3.amazon.com/fcp-indi/data/Projects/RocklandSample/RawData.Uploading data shared through the FCP and INDI initiatives improves its accessibility for cloud-based and local computation. Future efforts for this project will include uploading the remainder of the FCP and INDI data and organizing the data in the new brain imaging data structure (BIDS) format.
accessURL: https://doi.org/10.5524/100233
storedIn:
GigaScience Database
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
GigaDB
homePage: http://gigadb.org/
ID:
SCR:006565
name:
Giga Science Database