ClusterOS Diagnostic Profile
Aberdeen Digital
Aberdeen Digital draws £203m of UKRI lead-led funding across 1,037 grants, anchored by Aberdeen (52%), Robert Gordon University (17%), with Total E&P Uk on the industrial side. 5 Companies House-traced spin-outs translate to £41m UKRI per spin-out.
The cluster shows medium-confidence "Stabilising around incumbents" and "Scaling activity instead of throughput" behaviour — research narrative is reinforced by recurring programme launches rather than narrowing toward commercial scaling, with academic capacity reabsorbing the cluster's signal.
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Same data examined through five diagnostic lenses — Pipeline, Leverage, Triple Helix, Throughput, Collaboration. The interactive diagnostic is currently in private preview.
Sources: UKRI Gateway to Research (grants, outcomes); OpenAlex (publications); Companies House (spin-out lifecycle); DSIT (cluster mapping); Public investment data. Snapshot May 2026.
Stabilisation stacks · Why single interventions fail
Value extraction events generate narrative about ecosystem success; narrative legitimises continued extraction by framing it as ecosystem contribution; uncertainty about whether extraction is harmful absorbed by the success narrative.
"A probe could test whether UKRI disaggregating the 248 follow-on funding events (P011) into "retained in region" vs. "extracted from region" categories in public reporting reduces the system's ability to absorb success and uncertainty signals through extraction-narrative coupling. If follow-on funding events were reported with geographic destination (e.g., "148 events: funding to Aberdeen-based entities; 100 events: funding to entities outside Aberdeen"), it might expose whether the success narrative (S7) around follow-on funding (currently framed as ecosystem success) obscures extraction patterns (S4), potentially making the difference between activity and retention visible and weakening the narrative legitimisation of extraction."
Leverage hypotheses are testable perturbations, not prescriptions. Where demand-side behaviour is weakly visible, the correct move is observation — improving visibility before attempting change.
Structural resemblances · Clusters with similar stall configurations
A full ClusterOS diagnostic adds actor questionnaire data, working sessions, and anchor interviews — producing higher-confidence stall identification, board-ready stack analysis, and leverage hypotheses calibrated to your specific context.