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Turing Data Safe Haven

Data Safe Haven is an open-source framework for creating Trusted Research Environments (TREs) to analyse sensitive data. It provides a set of scripts that will allow you to deploy, administer and use your own TRE on Microsoft Azure cloud.

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What Turing Data Safe Haven can do for you

Data Safe Haven cartoon by Scriberia for The Alan Turing Institute

:eyes: What is the Turing Data Safe Haven?

The Turing Data Safe Haven is an open-source framework for creating secure environments to analyse sensitive data. It provides a set of scripts and templates that will allow you to deploy, administer and use your own secure environment. It was developed as part of the Alan Turing Institute's Data Safe Havens in the Cloud project.

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All Contributors

:family: Community & support

:open_hands: Contributing

We are keen to transition our implementation from being a Turing project to being a community owned platform. We have worked together with the community to develop the policy, processes and design decisions for the Data Safe Haven.

We welcome contributions from anyone who is interested in the project. There are lots of ways to contribute, not just writing code!

See our Code of Conduct and our Contributor Guide to learn more about how we work together as a community and how you can contribute.

Contributors

:cake: Releases

If you're new to the project, why not check out our latest release?

You can also browse all our releases. Follow the link from any release to view and clone this repository as at that release.

Read our versioning scheme for how we number and label releases, as well as details of releases that have been used in production and releases that have undergone formal security evaluation.

When making a new release, open an issue on GitHub and choose the Release checklist template, which can be used to track the completion of security checks for the release.

:mailbox_with_mail: Vulnerability disclosure

We value those who take the time and effort to report security vulnerabilities. If you believe you have found a security vulnerability, please report it as outlined in our Security and vulnerability disclosure policy.

:bow: Acknowledgements

We are grateful for the following support for this project:

:warning: Disclaimer

The Alan Turing Institute and its group companies ("we", "us", the "Turing") make no representations, warranties, or guarantees, express or implied, regarding the information contained in this repository, including but not limited to information about the use or deployment of the Data Safe Haven and/or related materials. We expressly exclude any implied warranties or representations whatsoever including without limitation regarding the use of the Data Safe Haven and related materials for any particular purpose. The Data Safe Haven and related materials are provided on an 'as is' and 'as available' basis and you use them at your own cost and risk. To the fullest extent permitted by law, the Turing excludes any liability arising from your use of or inability to use this repository, any of the information or materials contained on it, and/or the Data Safe Haven.

Deployments of the Data Safe Haven code and/or related materials depend on their specific implementation into different environments and we cannot account for all of these variations. Safe use of any Data Safe Haven code or materials also relies upon individuals' and their organisations' good and responsible data handling processes and protocols and we make no representations and give no guarantees regarding the safety, security or suitability of any instance(s) of the deployment of the Data Safe Haven. The Turing assumes no responsibility for updating any of the content in this repository; however, the underlying code and related materials may change from time to time with updates and it is the user's responsibility to keep abreast of these updates.

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Programming languages
  • Python 97%
  • Shell 2%
  • Other 1%
License
</>Source code

Participating organisations

The Alan Turing Institute

Reference papers

Mentions

  • 1.
    A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services
    Author(s): Harrison Wilde, Lucia Lushi Chen, Austin Nguyen, Zoe Kimpel, Joshua Sidgwick, Adolfo De Unanue, Davide Veronese, Bilal Mateen, Rayid Ghani, Sebastian Vollmer
    Published in 2020

Contributors

JR
James Robinson
JM
Jim Madge
EC
Edward Chalstrey
Research Data Scientist
The Alan Turing Institute
MC
Matt Craddock
MO
Martin O'Reilly
The Alan Turing Institute
JH
James Hetherington
KW
Kirstie Jane Whitaker
HS
Hari Sood
FN
Federico Nanni
WW
Warwick Wood
The Alan Turing Institute
TD