Data Safe Haven team repo

This is a project management repo for the Data Safe Havens in the Cloud team at The Alan Turing Institute. Learn more about the project here.

README Contents

• Useful links
• New starters checklist
  • First steps
  • Developer steps
• Contacts
• Releases

Useful links

Data Safe Haven Project

• DSH repo (public) which hosts the open source codebase (and the docs code)
• DSH docs (public) which includes user and deployment documentation
• DSH team repo (this repo) (public) used for team mangement and issues not related to the codebase
• DSH Team project board (private) where the the issues from the above repos that the team intend to work on are organised
• Turing DSH Sharepoint link (private)
• DSH roadmap/ unified board

Trusted Research Environments Service Area (TRESA)

• Turing TRE docs (public) which contains all the information on processes for managing "production" instances of DSH at Turing and the relevant people and tasks
• Trusted research repo (private) which contains the code for the Turing TRE docs and issues for the 2 project boards below
• TRESA - SRE board (private) which contains info of all the current/future/past SRE deployments at Turing under the production (prod4) SHM
• TRESA management board (private) which is for managing service area general issues

Standardised Architecture for Trusted Research Environments (SATRE)

• SATRE proposal (private) link to proposal doc in DSH sharepoint
• SA-TRE (public) GitHub org
• SATRE team repo (public) project management repo
• SATRE spec doc repo (public) repo used for hosting/writing the specification doc/ reference architecture (work package 1)
• SATRE backlog board (public) for managing issues from the above repos

New starters checklist

Welcome to the Data Safe Haven team! 🎉

You should read this page and follow the instructions if you're joining the project for the first time at The Alan Turing Institute.

First steps for all starters

1. Bookmark this page and the other useful links above
2. Read up: On the documentation site, click the Overview tab, where you will find links to resources that explain what Data Safe Haven is and why someone would want to use it.
   • In particular, it's worth studying the diagram which displays the overall architecture of DSH, including the distinction between the Safe Haven Management (SHM) and Secure Research Environments (SREs) and the data classification system.
3. Access: We have internal discussions about the project via Slack and manage our work via a combination of GitHub and Microsoft Sharepoint:
   • Slack: Ensure you have access to the Alan Turing Institute Slack workspace and join the following channels: #data-safe-haven and #data-safe-haven-team (the latter is private and you may need to request a team member to add you, see contacts).
   • Slack: You can also join the external-facing Slack workspace called Turing Data Safe Haven, used for release announcements and other communications with the community of DSH users. Ask a team member to add you (see contacts).
   • Sharepoint: Internal documents are stored on sharepoint. You will need to have your Turing SSO set up. Ask a team member to add you to the group with access rights (see contacts).
   • GitHub: We use a GitHub project board to manage who is working on what. Familiarise yourself if you haven't used one before and check you can add issues to the data-safe-haven-team or data-safe-haven repos from the project board view.
4. Explore: Ask one of the existing Data Safe Haven team (see contacts) to create a non-privileged user account in the test SRE called sandbox, so you can log in explore it from a user perspective and familiarise yourself with how it works. This is available at https://sandbox.prod4.turingsafehaven.ac.uk/

Next steps for developers

1. Deploy: The best way to familiarise yourself with the codebase is to have a go at deploying either an SRE to an existing SHM, or if time permits, a new SHM and attached SRE. Click on the Deployment tab in the DSH documentation and follow the guides available there. Either choose the latest release docs or try develop if you're feeling adventurous!
   • Note: When you're done with any test SHM/SREs, you should follow the "Tear down" instructions in the respective guides.
2. Open issues: If you notice any bugs in the Safe Haven code when deploying an SHM or SRE, open an issue on the main repo. Take care to follow the issue template.
3. Contribute code: If you want to contribute changes to the code base, perhaps to fix an issue that you have identified during deployment, create a fork of the main repository on your personal Github account, modify the relevant code on your fork, and then create a pull request.

Contacts

The DSH team includes a longer list of people at The Alan Turing Institute, but the people you should contact with queries about the information in this README are as follows:

• James Robinson
• Jim Madge
• Ed Chalstrey
• Matt Craddock

Turing new starters with a @turing.ac.uk email should be able to find the email addresses of the above via Microsoft Outlook, if they have any queries.

Releases

Follow the guidance on the README of the Data Safe Haven repo.

When ticking off the tasks on the release checklist issue you create, save all the logs it asks you to save in Sharepoint under release_logs. Copy the templates folder to your new release name and fill in the templates accordingly. You can use markdown2pdf.sh to build the PDF versions that get attached to the release.