The Dataset Friction Framework: measuring user-facing friction as a complement to FAIR

Abstract: Open research data services have matured to the point where the cost of sustaining them at scale has become a primary design constraint, driving providers to make deliberate choices that may reduce user convenience to keep the service viable. The FAIR (Findable, Accessible, Interoperable, Reuseable) principles describe whether a dataset is well stewarded, and FAIR compliance is often treated as a proxy for usability. FAIR does not capture the cost to a user of finding, accessing, interpreting, and applying a dataset. We introduce the Dataset Friction Framework (DFF) as a complement to FAIR, directly addressing usability. DFF measures user-facing friction across six dimensions, distinguishing engineered friction (deliberate data provider design choices that sustain a service) from accidental friction (defects that require remediation). The framework is validated against 18,556 support tickets from the European Centre for Medium-Range Weather Forecasts (January 2024 to May 2026), which serves 280,000 registered users. Restricting the analysis to tickets raised by external reporters reduces the corpus by 12.3%, but every dimension's internal-staff share falls below this baseline -- confirming that the reported friction signals are genuinely user-facing. We then assess three real datasets across three providers and show that FAIR compliance and DFF friction can disagree in both directions: a 92% FAIR-compliant dataset can still carry substantial friction, and a 42% FAIR score can be an artefact of anti-scraping policy rather than poor stewardship. The two measures are non-redundant and jointly informative: FAIR compliance does not predict DFF friction in either direction. This constitutes the first large-scale empirical application of the framework; cross-institutional validation is identified as the immediate next step.