TRE sustainability and operations#



Sustainability needs to be long term, but how do you plan for it when the scenario may change in 5 years? There is also an issue with research, this is a service yet funding requires teams to appear to be doing something new each time, and funders often prefer not to pay for infrastructure (also challenges with cost estimates and under/over expenditures).

There are several variables and questions about whether they should be free at point of use (distributing against overheads), or whether to employ a membership user model, a project fee model, standard features being free but charging for high demanding ones or something else. In all cases at least some core funding is required to ensure continuity, specialisation and quality.

What we want to ensure is that a public service exists.

Next Steps#

  • Create a roadmap that focuses on:

    • Technical skillsets

    • Information governance requirements

    • 10 year funding plan

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.

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

Business and operations to use OS TRE safely and securely

what is the perfect TRE/SDE environment future consolidation

Software development can be amortised across the community

SERP tenant

Training component

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

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

Core capabilities, exceptional stuff is great, but majority, early stage users, standardise and simplify.

Whatever it is, what’s missing the ability to understand data. GIGO

Standardisation of data makes it seem simpler than it is, reproducibility?

AI/ML store data for XX years, is it readable in that time?

Who picks up the storage costs for the data.


How can we make it more transparent

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

  • Individual researchers and work with them and the RO.

  • Free at the point of use funding?

  • 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

  • Research proposal, existing staff funding or contracted.

  • For commercial and public researchers usage has to be for public good, commit to publishing and not for profit

  • Virtual machines provided some policy for standardising storage/compute available

  • 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


  • 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

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?

Trust that the analysis/code is running as intended?

Roadmap plan#


  • What would a solution to this problem look like?

  • What resources would be needed (people, time, funds, infrastructure etc.)?

  • How can this community support you in getting them?

  • What working groups/orgs are already working on this, if any? How can we collaborate with them effectively?


A roadmap should address

  • Technical knowledge, skills, TRE staff skillsets

    • Why doing this has to be part of retaining people

    • Localising staff makes this easier, central models push more to thinking about pay

    • To address retention

    • Pipeline of talent

    • Can TRE model work in R

  • Not just technical, IG, where can I get more information

    • Consultancy

    • Embedded technical/operational/IG knowledge relevant to the problem.

    • Research - teaching balance.

  • Funding

    • Lots of politics, in HPC communities, good for those who get it. Not good for those who have to resort to begging

    • Not necessarily good for SDE

    • Analysis will follow data

    • People with data will need to bolt compute

    • HPC allocation modelled SDE account for compute/storage costs

    • Why should SDE and HPC be considered differently

    10 year plan - scope for accreditation

    • Chartered research infrastructure?

    • CSP platform neutral certifications for Data/Cloud

Infrastructure sustainability


  • Infrastructure/Developers

  • Operations

  • Data Scientists