Environment, Natural Resources and Agriculture (ENRA) research strategy 2027-2032: consultation

Annex C: ENRA Strategy for Decision Support

Introduction

The ENRA Decision Support Strategy sets a clear direction for enhancing the analytical capabilities of the 2027-2032 ENRA Research Programme. It focuses on strengthening the modelling and analysis activities delivered through the Strategic Research Programme, including supporting Underpinning National Capacity activities, to ensure a more integrated approach across the programme.

This strategy addresses current challenges and explores alternative approaches to meet future demands. Grounded in the principles of evidence-based decision- making, innovation, and alignment with national guidance and standards, it sets a vision for the development of Scotland’s modelling and data analysis capabilities within the ENRA research programme over time. It aims to deliver long-term improvements by addressing current barriers and enabling more consistent, collaborative, and policy-relevant use of data and models.

This strategy focuses on modelling for decision support, and the underpinning data activities that support modelling, within the 2027–2032 ENRA Research Programme. Specifically, those activities organised through the Strategic Research Programme and Underpinning National Capacity. It also offers an optional framework through which other delivery partners can engage. The following activities are within the scope of this strategy:

• Modelling activity funded and delivered through the 2027-2032 ENRA Research Programme’s Strategic Research Programme (SRP).
• Underpinning data and related processes that support modelling and analysis within the SRP, including data sharing, processing, quality assurance, and analytical workflows.

Purpose and Guiding Principles

The guiding principles of this strategy are:

• Working for the Public Good: Data and models are treated as shared assets that inform public policy and maximise societal benefit, delivering good value for public money. This includes ensuring that work with new and emerging technologies is ethical and transparent.
• Co-Design and Collaboration: Researchers, policymakers, and stakeholders will be engaged through consultations and workshops to co-create feasible and ambitious solutions.
• Delivering Impact: Data analysis and modelling activity underpin much of the wider research activity in the 2027-2032 ENRA Research Programme. This strategy will ensure analysis and modelling outputs support better policy outcomes and deliver cross-programme impact.

The desired modelling and underpinning data analysis outcomes for the 2027-2032 ENRA research programme, informed by these principles, are:

1. Ensuring traceability and transparency in how data, analysis, and models inform decisions.

2. Enabling timely, evidence-based policy decisions through accessible, high-quality analysis and modelling.

3. Fostering a culture of reuse, collaboration and active engagement across research and policy communities.

By delivering on these outcomes, the ENRA research programme will be better positioned for long-term success. Greater consistency and coordination in how underpinning data analysis and models are managed could help minimise duplication, encourage collaboration, and increase the overall utility of research outputs.

Vision for Modelling in the Research Programme

Ensuring a more integrated and accessible approach to modelling, and underpinning data, is a key priority for the next research programme. This annex sets out a series of guiding principles that reflect the Scottish Government’s vision to improve how data, analysis and modelling are used across the programme and deliver decision support tools for government. These principles are intended to foster greater transparency, timeliness, and collaboration in research and policy contexts. By promoting integration, collaboration, and improving access to data and models, the vision seeks to support more coherent, responsive, and evidence-informed decision- making.

• Ensuring traceability and transparency in how data, analysis, and models inform decisions.
• Building consistent approaches to underpinning data definitions and standards for analyses.
• Enhancing collaboration by improving cross-programme visibility of the most actively used datasets.
• Facilitating data linkage by identifying and sharing work across the programme that addresses common linkage challenges.
• Ensuring open sharing of code using effective platforms, and ensure clarity on model assumptions, inputs and products.
• Enabling timely, evidence-based policy decisions through accessible, high- quality analysis and modelling.
• Emphasising interoperability, collaboration, and coherent messaging in the delivery of models and analyses.
• Empowering analysts and modellers to experiment with new approaches and advance the state of the art.
• Committing to the role of clear, traceable quality assurance practices in delivering robust, reliable outputs.
• Fostering a culture of reuse, collaboration and active engagement across research and policy communities.
• Building stronger relationships between modellers, analysts and their policy partners to increase the relevance and impact of modelling.
• Creating new channels for policymakers to engage with the modelling community and raising awareness to help improve the understanding of modelling activity across Government.
• Addressing barriers to the effective sharing of data, methods, and insights that limit the policy impact of modelling and analysis.

Recognising the diversity of modelling needs, data types, and organisational contexts within the programme, a ‘one size fits all’ approach is not appropriate. Instead, the research programme must encourage flexibility and innovation, providing a framework that can evolve as technology, policy priorities, and user needs change. Where helpful, the strategy will draw on relevant public sector guidance on data and reform to support consistency and collaboration. Established frameworks like the Green Book, Magenta Book, Aqua Book, and the Scottish AI Playbook offer useful principles for ensuring quality, transparency, and relevance in analysis and modelling.

Governance Support for this Vision

As the research programme is developed, we will explore how enhanced governance can help align modelling activity with strategic aims. Potential areas for a modelling governance framework to explore are:

• Facilitating clearer communication between researchers and policymakers.
• Keeping the wider research community informed of changes to key underpinning datasets that could impact their work.
• Giving researchers avenues to highlight challenges, opportunities, and limitations of data, analysis and modelling to policymakers.
• Providing policymakers with greater opportunities to have regular, responsive engagement with models and analyses.

The overarching objective of the governance framework will be to foster collaboration and integration between different areas of modelling and analysis in the program. This will be a proportionate approach and will not be, for example, a forum for approving changes to individual models or for directing the development of models.

As part of any governance, it will be important to understand how mechanisms are supporting better integration, collaboration, and use of modelling across the programme. This could involve identifying broad indicators of progress, embedding regular opportunities for reflection, and creating inclusive ways to gather feedback from those involved in research and policy. These activities can help ensure that governance remains adaptive, learning-focused, and responsive to the evolving needs of the programme.

Enablers

As well as timely and effective governance, several enablers are key to delivering on the vision detailed above. These include:

• Skills and capacity development – Identifying and supporting opportunities for engagement and knowledge exchange between the research community and the Scottish Government to build long-term capacity and alignment. This includes fostering a shared appreciation of research lifecycles, analytical best practices, and the contexts in which evidence is used.
• Innovation and use of emerging technologies - To ensure the research programme remains forward-looking, we must encourage exploration of emerging technologies. This may include AI and Machine Learning for pattern detection and predictive modelling, data from LiDAR and high-resolution remote sensing for environmental monitoring.
• Best practice for model and data integration –Establishing shared principles for integrating models and data is essential to delivering the vision. This requires collaboration across the research and stakeholder communities and alignment with international standards and exemplars.

Next Steps

We are keen to receive feedback from stakeholders on the proposals set out above. Details on how to respond are provided in Section 6 of this document. In addition, we will be undertaking further engagement sessions with researchers and end-users to explore how these objectives can be most effectively embedded within the programme.