Resources
This page collects practical resources that others can adapt for their own initiatives. All templates and frameworks have been generalised — institutional and personal identifiers have been removed so they can be applied in any context.
Templates
1. Problem statement template
Use this to articulate the systemic problem your initiative addresses.
Template structure
The challenge: [One sentence describing the systemic problem — not a local complaint, but a structural gap]
Who is affected: [Which communities, institutions, or sectors experience this problem]
The evidence: [2-3 key data points from your evidence gathering]
The consequence of inaction: [What happens if nothing changes — frame in terms of competitiveness, public benefit, or research capability]
What we are proposing: [One sentence describing the coordination approach — not a technical solution, but a mechanism for collective action]
2. Stakeholder mapping template
| Category | Organisation / Role | Interest level | Influence level | Current engagement | Next action |
|---|---|---|---|---|---|
| Researchers | [Role type] | High / Med / Low | High / Med / Low | None / Informal / Formal | [Specific next step] |
| Infrastructure | [Role type] | ||||
| Institutional leadership | [Role type] | ||||
| Funders | [Role type] | ||||
| National bodies | [Role type] | ||||
| Industry | [Role type] | ||||
| Professional services | [Role type] | ||||
| Contracts office | [Role type] | ||||
| Policymakers | [Role type] |
3. Evidence survey framework
Use a mix of question types — multiple-choice, Likert-scale agreement statements, and free-text — to capture both quantitative patterns and qualitative insight. The framework below can be adapted to any research domain.
Section A: Scope and context
Establish what respondents work with and what is at stake.
- What types of [domain-specific] data are you working with that depend on access to sufficient infrastructure, expertise, or specialist support? (Multi-select from a curated list relevant to your domain)
- Given the data you specified, do you consider them mission-critical for your research to progress? (Yes / No / Maybe / Not applicable)
Section B: Training and skills
Determine the gap between the skills respondents need and the formal training they have received.
- Are you formally trained as a [relevant specialist role, e.g. data scientist, computational biologist, bioinformatician]? (Yes / No)
Section C: Institutional resources and infrastructure
Use Likert-scale agreement statements (Strongly agree – Strongly disagree) to measure perceptions of institutional provision. Where possible, break down responses by institution to reveal variation. Example statements:
- My institution does not have sufficient resources to support high-quality [domain] research
- I am provided with a cost-effective, local data storage solution that meets the needs of my research
- I have local access to up-to-date, community-approved data analysis workflows
- Local computing resources (e.g. CPU, GPU) are not a limitation for my research
- I have access to, or know where to find, expert advice on reproducible data analysis workflows
- I know how to prepare and share data in line with FAIR principles
Section D: Open-ended questions
Capture challenges and context that structured questions may miss.
- My biggest [domain] challenge is: (Free text)
- Who funds your research? (Free text)
Design tips
- Keep the survey short — aim for completion in under 10 minutes to maximise response rates
- Use Likert-scale statements rather than open-ended questions wherever possible, as they are easier to analyse at scale and reveal institutional variation
- Include at least one free-text question to surface challenges you did not anticipate
- If asking about institutional provision, break down results by institution — aggregate figures can mask significant disparities
- Pilot the survey with 3–5 respondents before full distribution to catch ambiguity
4. Institutional capability snapshot template
For each participating institution, gather:
- Compute: What HPC, cloud, and local compute is available? Capacity? Access model?
- Storage: What research data storage is available? Capacity? Cost model?
- Platforms: What specialist platforms or software are provided centrally?
- Licensing: What commercial software is licensed? Cost? Number of users? What is duplicated across institutions?
- People: How many research software engineers, data scientists, bioinformaticians are employed? On what terms? What is the turnover rate? Are positions sustainable or grant-funded?
- Governance: How is data access governed? What frameworks are in use?
- Training: What data science training is offered? At what career levels? Is it visible to researchers outside the department or institution? What external training resources are used?
- Financial sustainability: What is the total annual cost of data science infrastructure and support? Is this sustainable under current budget pressures?
- Known gaps: What does the institution identify as its biggest shortcomings — across infrastructure, training, and specialist workforce?
5. Workshop discussion template
For each structured discussion, prepare:
Discussion title: [Clear, action-oriented]
Key question: [The specific question the table must answer]
Required output: [What the table must produce — e.g. a ranked list, a yes/no vote with conditions, a named set of actions]
Evidence to have on the table: [Which briefing documents or data should be available]
Time allocation: [Fixed, non-negotiable]
Frameworks
The five-pillar framework
The framework developed through this initiative can be adapted to any domain. It reflects the conviction that coordinating research resources requires equal attention to technology, people, and skills — not a technology-first approach with people as an afterthought. The five pillars are:
- Infrastructure — the physical and digital resources required (compute, storage, platforms, and the people who run them)
- Governance — how decisions are made about access, use, and strategic direction
- Authentication and access — how users are identified, verified, and granted appropriate access across institutional boundaries
- Data ownership and IP — how intellectual property is managed in collaborative environments, and how data subjects’ rights are protected
- Careers and training — career pathways, recognition, training coordination, and skills development for the people who make data science work — from researchers to technical specialists
For each pillar, the framework asks:
- What already exists that we can build on?
- What are the distinct work packages?
- Who needs to be involved?
- What can be delivered in 12-18 months?
- What evidence or scoping is still needed?
- What are the risks of not acting?
The phased implementation roadmap
| Phase | Timeline | Focus | Key outputs |
|---|---|---|---|
| 1. Convene | Month 1 | Establish working group, agree terms of reference | Concordat signed, glossary v1, governance for the group itself |
| 2. Map | Months 1-3 | Stakeholder mapping and landscape review | Comprehensive inventory of existing resources, capabilities, and gaps |
| 3. Analyse | Months 3-6 | Gap analysis, business case development | Costed proposals, SWOT analysis, priority recommendations |
| 4. Pitch | Months 6-12 | Engage government and funders | Formal strategic case, funding applications |
| 5. Build | Months 12+ | Implement priority actions | Exemplar projects, shared services, training programmes |
Checklists
Pre-workshop checklist
- Target outcomes defined (max 4)
- Evidence summaries prepared and printed
- Discussion templates designed, tested, and printed
- Attendance curated for diversity of expertise and seniority
- Table facilitators briefed
- Note-takers assigned
- Photography/capture plan in place
- Name badges printed
- Flipcharts and markers acquired
- Room layout planned (table groups of 6-8; consider accessibility)
- Catering arranged (take note of dietary requirements)
- Post-workshop synthesis plan agreed
- Follow-up timeline communicated to participants in advance
Post-workshop checklist
- All outputs photographed and digitised
- Synthesis document produced
- Circulated to all participants
- Working group membership call issued
- Convening organisation identified
- First working group meeting scheduled
- 90-day deliverables defined
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