Note: you will also see the 'hcp-openaccess-logs' bucket, but you should not have, or need, access to this. Just like that, you have access to all HCP datasets for which you have permission. Select 'Add an External Bucket' from the menu and enter this id.

The name of this bucket is ' hcp-openaccess'. You will need to manually add the bucket that contains HCP datasets. Once you have connected, you should be automatically connected to the HCP Open Access data bucket, and see the 'HCP/' folder. Enter your AWS credentials where specified: Once you launch S3 Browser, you will be prompted to create an account. CyberDuck is another such free software client, available for both Mac and Windows. Here is one example, using Amazon S3 Browser, a free software package for Windows that lets you browse data on AWS from your desktop. There are a number of ways and applications that can be used to connect to the HCP data container on AWS. For security reasons, we do not store a copy of your AWS access keys in ConnectomeDB.Ĭonnecting to AWS Data Using These Credentials Once your set of credentials is created, we strongly encourage you to save them locally. Your access keys will be randomly generated. Your AWS login will be the same as your ConnectomeDB username. There will be a brief delay while ConnectomeDB communicates with our AWS bucket to establish permissions.ĪWS returns a set of credentials. Simply click on the 'Data Available on Amazon S3' button to open a modal window to generate them.

Creating AWS CredentialsĮach set of AWS credentials consists of three parts: a login, a public access key, and a secret access key. When new datasets are added to AWS, the only action you will need to take (if you already have AWS credentials) is to review and accept the data use terms for each new dataset. You will only need one set of credentials to access all datasets on AWS, but that access will be controlled by your acceptance of each dataset's data use terms. Each dataset that is available on AWS will have an icon in its dataset listing, as shown: HCP Datasets on AWSĪfter logging in to ConnectomeDB ( ), you will see a list of datasets that we are hosting. Information on setting up an AWS EC2 instance and processing is available here: EC2 Beginner Startup Advice.

Amazon S3 can be used for cloud-based HCP data processing (Amazon charges users for compute cycles) or as an alternative to Aspera for download of HCP data at no cost to the user (Note: in most cases, downloading HCP data through Amazon S3 WILL NOT be faster than Aspera-driven data download through ConnectomeDB). This tutorial will show you how to get a set of AWS credentials and connect to HCP data via Amazon S3. Otherwise, your private files might end up publicly accessible.The Human Connectome Project is now making image data available to use directly with Amazon Web Services, without you having to download from ConnectomeDB or order Connectome in a Box drives. It is important to set the permissions for the uploaded files correctly. It’s practical to set a bookmark for this connection: Bookmark > New Bookmark.For the username, enter your AWS access key, and for the password, enter your AWS secret access key (you can retrieve these credentials in the AWS Management Console).Select “S3 (Amazon Simple Storage Server)”.Download and install Cyberduck from here.Note that uploads are considerably faster via Cyberduck (or another S3 client) than via the AWS Management Console. It is an alternative to the AWS Management Console for managing your files on Amazon S3. Cyberduck is an FTP client that can be used to easily access and manage Amazon S3 buckets.

Cyberduck connect to s3