Aurora is a Django web application that can receive, virus check and validate transfers of digital archival records, and allows archivists to appraise and accession those records.
The name of the application is a reference both to the natural light display often seen in the northern hemisphere - sometimes referred to as aurora borealis - as well as the Roman goddess of dawn.
Aurora is part of Project Electron, an initiative to build sustainable, open and user-centered infrastructure for the archival management of digital records at the Rockefeller Archive Center. Project updates are available on Bits & Bytes, the RAC's blog.
git clone https://github.com/RockefellerArchiveCenter/aurora.git cd aurora docker-compose up
Once the build and startup process has completed, log into Aurora at
http://localhost:8000 with the user/password pair
Detailed Installation Instructions
$ git clone https://github.com/RockefellerArchiveCenter/aurora.git
- Build and run Aurora. The initial build may take some time, so be patient!
$ cd aurora $ docker-compose up
- Once this process has completed, Aurora is available in your web browser at
http://localhost:8000. Log in using one of the default user accounts (see "User accounts" below).
Installation Notes for Windows Users
By default, when cloning to a Windows machine, git will convert line endings to DOS format, which will cause a variety of issues in the Docker container. To avoid these problems, clone the repo to Windows using
$ git clone https://github.com/RockefellerArchiveCenter/aurora.git --config core.autocrlf=input
When using Docker Toolbox, clone aurora to a location in the C:\Users directory. By default, Docker Toolbox only has access to this directory.
Note that with Docker Toolbox, Aurora will not default to run on
http://localhost:8000. Check the docker ip default:
$ docker-machine ip default
If desired, you can import a set of sample bags (not all of which are valid) by running the
Open up a new terminal window and navigate to the root of the application, then run
$ docker-compose exec web import_sample_data
Transferring Your Own Bags
If you'd like to transfer your own bags, you can do that by SFTPing them into the local container:
- Host name:
- Port number:
- Username: A username associated with an existing user account in Aurora (see below for default accounts)
- Password: The password associated with the user account above
The Docker container is currently configured to persist the MySQL database in local storage. This means that when you shut down the container using
docker-compose down all the data in the application will still be there the next time you run
docker-compose up. If you want to wipe out the database at shut down, simply run
docker-compose down -v.
By default, Aurora comes with five user accounts:
|donor||password||Read Only User|
See the Aurora User Documentation for more information about permissions associated with each user role.
Note that in the Docker container, all user passwords are reset to "password" each time the container is restarted. This behavior can be changed by editing
setup_objects.py, but note that this change will impact your ability to SFTP bags into the container.
Transferring digital records
Aurora scans subdirectories at the location specified by the
TRANSFER_UPLOADS_ROOT setting. It expects each organization to have its own directory, containing two subdirectories:
processing. Any new files or directories in the
uploads subdirectory are added to Aurora's processing queue.
At a high level, transfers are processed as follows:
- Transfers are checked to ensure they have a valid filename, in other words that the top-level directory (for unserialized bags) or filename (for serialized bags) does not contain illegal characters.
- Transfers are checked for viruses.
- Transfers are checked to ensure they have only one top-level directory.
- Size of transfers is checked to ensure it doesn't exceed
- Transfers are validated against the BagIt specification using
- Transfers are validated against the BagIt Profile specified in their
- Relevant PREMIS rights statements are assigned to transfers (see Organization Management section for details).
Aurora comes with a RESTful API, built using the Django Rest Framework. In addition to interacting with the API via your favorite command-line client, you can also use the browsable API interface available in the application.
Aurora uses JSON Web Tokens for validation. As with all token-based authentication, you should ensure the application is only available over SSL/TLS in order to avoid token tampering and replay attacks.
To get your token, send a POST request to the
/get-token/ endpoint, passing your username and password:
$ curl -X POST -d "username=admin&password=password123" http://localhost:8000/api/get-token/
Your token will be returned in the response. You can then use the token in requests such as:
$ curl -H "Authorization: JWT <your_token>" http://localhost:8000/api/orgs/1/
In a production environment, successfully authenticating against this endpoint may require setting Apache's
Django Admin Configuration
Aurora comes with the default Django admin site. Only users with superuser privileges are able to view this interface, which can be accessed by clicking on the profile menu and selecting "Administration".
In addition to allowing for the manual creation and deletion of certain objects, this interface also allows authorized users to edit system values which are used by the application, including the human-readable strings associated with Bag Log Codes. Care should be taken when making changes in the Django admin interface, particularly the creation or deletion of objects, since they can have unintended consequences.
Aurora is an open source project and we welcome contributions! If you want to fix a bug, or have an idea of how to enhance the application, the process looks like this:
- File an issue in this repository. This will provide a location to discuss proposed implementations of fixes or enhancements, and can then be tied to a subsequent pull request.
- If you have an idea of how to fix the bug (or make the improvements), fork the repository and work in your own branch. When you are done, push the branch back to this repository and set up a pull request. Automated unit tests are run on all pull requests. Any new code should have unit test coverage, documentation (if necessary), and should conform to the Python PEP8 style guidelines.
- After some back and forth between you and core committers (or individuals who have privileges to commit to the base branch of this repository), your code will probably be merged, perhaps with some minor changes.