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manics committed Jan 25, 2024
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@@ -37,7 +37,6 @@ Resources: Google RADLab: https://cloud.google.com/blog/topics/public-sector/goo
- Cloud provision via Jisc (as oppose to direct with the cloud provider) can be cheaper and it also handles SSO: https://www.jisc.ac.uk/forms/uk-access-management-federation-sign-up#
- Resources: Google RADLab: https://cloud.google.com/blog/topics/public-sector/googles-new-rad-lab-solution-helps-spin-cloud-projects-quickly-and-compliantly


## Roadmap plan

### Questions
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@@ -31,25 +31,28 @@ Take home message: Its not about the Technology. In fact the more TREs technical
- Roadmap and Next Steps

**Short term**: understanding what we have

- Define what is a TRE, wrt to **multiple** TREs within a PEST framework that highlights issues that are not just technical, for example includes the diversity of TRE models, the business models of TREs, where risk, responsibility and accountability lay, and includes certifiable PROCESS as a core pillar (shared SOPs). Multi-TREs require new Processes.
- Define a TRE Maturity Model that builds on above to develop a more objective model of TRUST, RISK and RESPONSIBILITY for inter-TRE data exchange. Could be used to assess, compare, and facilitate trust between TREs.
- A common language scale for the ‘tiers’ of TREs suitable for different levels of inter-TRE sensitivity.
- Identify and clarify PEST bottlenecks with examples

**Medium Term**: shifting to newer ways

- Review different architectures and processes for working between TREs
- What would be just enough with what we already have (e.g. 5SROCrate as m-TRE middleware using current processes)
- What m-TRE processes would we need to introduce
- The role of trusted intermediaries (brokers, federated analytics services) to take on risk and responsibility and reposition the Data Sharing Agreements. e.g global identity services linking identities and records, who takes responsibility?

**Long Term**: radical shift

- PPIE education outside the PPIE self selecting bubble to counter mistrust of government and conspiracy theory
- Expectation that data is owned by the NHS?
- Rethink of data holdings and services from Data Warehouses to Data Fabrics.

**Notes**

- What is a TRE ?
- What is a TRE ?
- Are they always repositories for single datasets, popup TRE?
- Not always - many of the environments have multiple users and projects on top of the core dataset, through project-based access through VMs/virtual desktops.
- There is also a requirement for high performance computing for some datatypes (GPU for AI/imaging, workflows etc)
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@@ -136,4 +136,3 @@ C. National SDE [RN]
D. SNSDEs
E. Local researchers
F. Common entities/stakeholders in health data space

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@@ -43,9 +43,9 @@ It is necessary to include PPIE in funding, make efforts to simplify language an
## Roadmap plan

- What resources would be needed (people, time, funds, infrastructure etc.)?
- Funding to recruit members of the public who might not normally get involved. Examples of using Sortition and Coal Rabie and IPSOS
- Utilising that expertise of external recruitment agencies
- Training and support ofhow to communicate with members of the public for academics and 'technicians'
- Funding to recruit members of the public who might not normally get involved. Examples of using Sortition and Coal Rabie and IPSOS
- Utilising that expertise of external recruitment agencies
- Training and support ofhow to communicate with members of the public for academics and 'technicians'
- How can this community support you in getting them?
- TRE specific PIE groups
- Embedding PIE skills in peoples careers
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@@ -45,8 +45,8 @@ So: separating ops teams from R&D teams in both people and funding terms is the
- UK Wide program to define governance framnework.
- Innovation must still be supported


### Handwritten notes on day

Transcripted by CMWG

- Incompatible standards?
@@ -59,18 +59,17 @@ Transcripted by CMWG
- Service environment often R&D (run it and improve it)
- Bridge: research prototype -> 'Product' (i.e. TRL 1 -> 9)
- Risk definition & tiering -> No standard?

- DEA?
- Legislate it


- Lots of standards arise for good reasons
- had to exist, so grew in isolation
- Have lost sight (perhaps) of the "why" are we doing this
- Existing inertia (changing engines when plane is in flight)
- Ops is funded one way, R&D funded by very diff methods, there is no clear bridge from one to the others
- Differing risk appetites from DCs, often for "poor" reasons


### Roadmap plan

#### Questions
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@@ -37,24 +37,30 @@ What we want to ensure is that a public service exists.
## Raw notes

Sustainability from funding perspective beyond the initial 5 years

- But what are things going to look like in 5 years time

CL centrally funded model

- Service in place, refreshed but need to appear to do something different each time to secure funding.

**Why different?**

- How costing then? Free at point of use, cost distributed against overheads.
- Constrain in the cloud?

Barts recover work space costs from research projects, distributed central cost on a membership/license/user model

- Difference between model for internal and external users.

Standard provision free, high storage/compute needs to be recovered

- More paperwork to create and chase invoices.

no funders like paying for infrastructure

What counts as core if it was funded?

- Duties imposed as data controllers law, or interpretation runs counter to wants of researchers

Folk specialising, if it doesn't get funded for the future that capability is lost.
@@ -64,6 +70,7 @@ Regional SDE model might lead the way of costing-funding-recovery
Some central funding

Specialist areas - operational team

- Different environments work differently from researcher perspective

Sustain people
@@ -83,13 +90,15 @@ Who provides desk-side support
Tracking usage, egress process, layers of tools and processes that need to be in place

In/out nature of TRE, tiered sensitivity? Commercial sensitivity. Has auditability in the TRE, does it need to be?

- Why different for UCL TRE?

Difference in TRE makes funding case easier, adding something new made it more interesting.

Using research funding to backfill

Estimate in advance what project is likely to use, operational costs, usually completely wrong and go over project

- Not sustainable to go consistently over budget
- Bill after usage is best, but challenging for proposal/funding

@@ -98,9 +107,11 @@ Cliff edge, have funding but only sufficient for 1 year not 3 years of project.
Following Access to HPC model

What can you take off the board if problem is solved strategically

- Good training for Data scientists: SC like training relevant to disciplines

Seems like we're trying to boil the ocean

- VDI, Excel may be R, Stata
- Developing things to deal with core use case

@@ -122,15 +133,16 @@ Constrained with the current model.

Guidance provided by RCs, institutional risk as the org have underwritten the project.


This breakout room continued during the second round

Concerned about being able to provide a service, don't control budgets

- Sustainability of providing a public service, rather than generating a business case

SNSDE comes under DH budgets, makes things easier

HDRUK MRC led 20 year vision 5 year cycle

- UKBB core underpinning funding
- Fund TREs for 3-5 years for specific projects
- Specific use cases not currently supported
@@ -140,6 +152,7 @@ HDRUK MRC led 20 year vision 5 year cycle
- Provide underpinning capacity?

What is ONS Model?

- Free at point of access
- Don't know how the budget is secured
- Funding comes through different sources ADR UK
@@ -149,35 +162,42 @@ What is ONS Model?
- Trying to enable research

Driven by what researchers ask for

- Intrinsic limit on budget call
- Budget for a specific network/platform
- Leverage external investment
- Some Pharma match funding
- Universities also fund

Move to long term funding

- Strategic level of funding, buffered from long-term budget
- Hub large funding but cliff-edged

Free at the point of use

- Incentivised-disinsentivised, equity of access
- Power users can over-consume, less accountability not having to justify use

consuming data token publication and harvesting data for private use

- Free at point of access so data is freely accessible
- Reminder: Don't offer data for commercial use

Challenges:

- Ingress-egress labour intensive to pour human eyes
- Automation tools for validating statistical disclosure test
- Skilled job
- Tools and more people-more efficient tools; more people would always be good.
- All TREs have these issues, share the solutions

More automation -IDS (Integrated Data Service- SRS Secure Research Service

- Free at point of use?? Cuts out some of the applications automated validation of inputs

Understand the whole pathway

- Fix one part and it just shows the next bottleneck
- Fraunhoffer 1/3-1/3-1/3 lights_on-academic-commercial_activity
- Sustainability, prime an initiative without committing to long term investment
@@ -187,10 +207,12 @@ More people - more monkeys on typewriters
Over focus on the medical use case currently, needs to rebalance.

Better understanding and economy of scale from small numbers.

- Focus critical mass on small number
- DARE UK would create a TRE to handle data as an offering

What is a TRE?

- At what point does a federated TRE network become a single TRE?
- TT: At the point at which you have seamless transition between TREs?

@@ -233,6 +255,7 @@ A roadmap should address
- Why should SDE and HPC be considered differently

10 year plan - scope for accreditation

- Chartered research infrastructure?
- CSP platform neutral certifications for Data/Cloud

@@ -243,4 +266,3 @@ People:
- Infrastructure/Developers
- Operations
- Data Scientists

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@@ -115,7 +115,7 @@ The stages of a researcher journey were explored in more detail, focusing on:
- Data Environment
- Data Analysis

And how many different people have built many solutions across this journey.
And how many different people have built many solutions across this journey.
In a lot of instances these solutions are quite different, meaning researchers have to use new processes, tools and methods when they go to different TREs.

HDR is aiming to convene the technology ecosystem that we have in the UK. This focused on the aspects of:

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